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diff --git a/CoreML/ImageClassification/ImageClassification.mlproj/Models/ImageClassification 1.mlmodel b/CoreML/ImageClassification/ImageClassification.mlproj/Models/ImageClassification 1.mlmodel
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index 0000000..b697ba9
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diff --git a/CoreML/ImageClassification/ImageClassification.mlproj/Project.json b/CoreML/ImageClassification/ImageClassification.mlproj/Project.json
new file mode 100644
index 0000000..219d8e0
--- /dev/null
+++ b/CoreML/ImageClassification/ImageClassification.mlproj/Project.json
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+{"id":"1BF914EB-845D-439F-A7CB-298AE46E07B8","projectName":"ImageClassification","description":"","version":"1.1","authorName":"CommitGuy","createdDate":"2023-03-22T15:02:05Z","license":"","activityEntities":["projectCreated",[{"id":"94EE01E7-21D6-41ED-A1EC-3AB3AD55CAB9","title":"Project Created","createdDate":"2023-03-22T15:02:05Z","event":"projectCreated","subtitle":"ImageClassification"}]],"taskType":"imageClassifier","title":"Image Classification","location":"","versionErrorTitle":"Project file cannot be opened.","modifiedDate":"2023-03-22T15:02:05Z","settingsEntity":{"snapshotOnExtendTraining":true,"snapshotOnResumeTraining":false},"versionErrorSubtitle":"You need a newer version of Create ML to open this file. Download the latest version."}
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diff --git a/CoreML/README.md b/CoreML/README.md
new file mode 100644
index 0000000..e97792b
--- /dev/null
+++ b/CoreML/README.md
@@ -0,0 +1,37 @@
+# CreateML & CoreML
+
+## CreateML
+- Xcode를 실행 후 mouse 오른쪽 클릭 후 CreateML을 실행
+
+<img src="images/createml.png" width="40%"><br>
+
+- Template 선택
+
+<img src="images/createml_template.png" width="40%"><br>
+
+- Modeling 편집기
+
+<img src="images/modeling_editor.png"><br>
+
+  - Settings 탭에서 Training Data 를 설정해 Training 탭에서 학습 시킬 수 있음.
+  - Evaluation 탭에서 학습 후 결과를 확인 할 수 있으며
+  - Preview 탭에서 테스트 해볼 수 있음
+  - Output 탭에서 **[Get]**을 클릭하여 Model을 뽑을 수 있음
+
+CreateML은 전이 학습을 사용하기 때문에 모델을 완전히 처음부터 학습시키는 게 아니며, 그 덕에 모델을 빠르고 효율적으로 학습시키게 됨
+
+## CoreML
+- MLModel 파일을 추가하기 위해 Xcode 프로젝트 창에 mlmodel을 Drag & Drop 함
+
+- Utilities 탭에서 모델을 암호화하고 클라우드 배포를 설정할 수 있음
+<img src="images/ml_cloud.png"><br>
+
+### CoreML 추론
+CoreML을 사용하면 비동기 추론이 가능해야 하는데 그렇지 못할 경우 모델 추론에서 병목 현상이 일어날 수 있음. <br>
+CoreML은 모바일 API로 설계되어 모델 추론하는 동안 앱이 멈추는 사용자 경험을 제공하지 않도록 하는 패턴을 사용
+
+<img src="images/coreml_inference.png" width="40%"><br>
+
+비동기 작업으로 만들기 위해 디스패치 큐(Dispatch Queue)에서 핸들러를 생성
+<br>
+CoreML은 CIImage, CGImage, CVPixelBuffer, Data를 입력으로 받을 수 있음
\ No newline at end of file
diff --git a/CoreML/StableDiffusion/README.md b/CoreML/StableDiffusion/README.md
new file mode 100644
index 0000000..5f94c82
--- /dev/null
+++ b/CoreML/StableDiffusion/README.md
@@ -0,0 +1 @@
+# StableDiffusion
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.pbxproj b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.pbxproj
new file mode 100644
index 0000000..bbf8222
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.pbxproj
@@ -0,0 +1,403 @@
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+				GCC_WARN_UNUSED_VARIABLE = YES;
+				IPHONEOS_DEPLOYMENT_TARGET = 16.2;
+				MTL_ENABLE_DEBUG_INFO = NO;
+				MTL_FAST_MATH = YES;
+				SDKROOT = iphoneos;
+				SWIFT_COMPILATION_MODE = wholemodule;
+				SWIFT_OPTIMIZATION_LEVEL = "-O";
+				VALIDATE_PRODUCT = YES;
+			};
+			name = Release;
+		};
+		7848E38A29CBEF4C0034907E /* Debug */ = {
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+			buildSettings = {
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+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+				CODE_SIGN_STYLE = Automatic;
+				CURRENT_PROJECT_VERSION = 1;
+				DEVELOPMENT_ASSET_PATHS = "\"SD2Img2Img/Preview Content\"";
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+				LD_RUNPATH_SEARCH_PATHS = (
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+				);
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+			};
+			name = Debug;
+		};
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+			buildSettings = {
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+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
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+				DEVELOPMENT_ASSET_PATHS = "\"SD2Img2Img/Preview Content\"";
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+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				LD_RUNPATH_SEARCH_PATHS = (
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+				);
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+				PRODUCT_NAME = "$(TARGET_NAME)";
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+				TARGETED_DEVICE_FAMILY = "1,2";
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+			name = Release;
+		};
+/* End XCBuildConfiguration section */
+
+/* Begin XCConfigurationList section */
+		7848E37629CBEF4B0034907E /* Build configuration list for PBXProject "SD2Img2Img" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				7848E38729CBEF4C0034907E /* Debug */,
+				7848E38829CBEF4C0034907E /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
+		7848E38929CBEF4C0034907E /* Build configuration list for PBXNativeTarget "SD2Img2Img" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				7848E38A29CBEF4C0034907E /* Debug */,
+				7848E38B29CBEF4C0034907E /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
+/* End XCConfigurationList section */
+
+/* Begin XCRemoteSwiftPackageReference section */
+		784E31D929CBF34B00DC6519 /* XCRemoteSwiftPackageReference "ml-stable-diffusion" */ = {
+			isa = XCRemoteSwiftPackageReference;
+			repositoryURL = "https://github.com/apple/ml-stable-diffusion.git";
+			requirement = {
+				branch = main;
+				kind = branch;
+			};
+		};
+/* End XCRemoteSwiftPackageReference section */
+
+/* Begin XCSwiftPackageProductDependency section */
+		784E31DA29CBF34B00DC6519 /* StableDiffusion */ = {
+			isa = XCSwiftPackageProductDependency;
+			package = 784E31D929CBF34B00DC6519 /* XCRemoteSwiftPackageReference "ml-stable-diffusion" */;
+			productName = StableDiffusion;
+		};
+/* End XCSwiftPackageProductDependency section */
+	};
+	rootObject = 7848E37329CBEF4B0034907E /* Project object */;
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/contents.xcworkspacedata b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/contents.xcworkspacedata
new file mode 100644
index 0000000..919434a
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/contents.xcworkspacedata
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Workspace
+   version = "1.0">
+   <FileRef
+      location = "self:">
+   </FileRef>
+</Workspace>
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved
new file mode 100644
index 0000000..20e07b9
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/project.xcworkspace/xcshareddata/swiftpm/Package.resolved
@@ -0,0 +1,23 @@
+{
+  "pins" : [
+    {
+      "identity" : "ml-stable-diffusion",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/apple/ml-stable-diffusion.git",
+      "state" : {
+        "branch" : "main",
+        "revision" : "2c4e9de73c9e723de264356f9563706ea9104212"
+      }
+    },
+    {
+      "identity" : "swift-argument-parser",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/apple/swift-argument-parser.git",
+      "state" : {
+        "revision" : "fddd1c00396eed152c45a46bea9f47b98e59301d",
+        "version" : "1.2.0"
+      }
+    }
+  ],
+  "version" : 2
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist
new file mode 100644
index 0000000..cda346a
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+	<key>SchemeUserState</key>
+	<dict>
+		<key>SD2Img2Img.xcscheme_^#shared#^_</key>
+		<dict>
+			<key>orderHint</key>
+			<integer>0</integer>
+		</dict>
+	</dict>
+</dict>
+</plist>
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AccentColor.colorset/Contents.json b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AccentColor.colorset/Contents.json
new file mode 100644
index 0000000..eb87897
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AccentColor.colorset/Contents.json
@@ -0,0 +1,11 @@
+{
+  "colors" : [
+    {
+      "idiom" : "universal"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AppIcon.appiconset/Contents.json b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AppIcon.appiconset/Contents.json
new file mode 100644
index 0000000..13613e3
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/AppIcon.appiconset/Contents.json
@@ -0,0 +1,13 @@
+{
+  "images" : [
+    {
+      "idiom" : "universal",
+      "platform" : "ios",
+      "size" : "1024x1024"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/Contents.json b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/Contents.json
new file mode 100644
index 0000000..73c0059
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/Contents.json
@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/Contents.json b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/Contents.json
new file mode 100644
index 0000000..cf21256
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "cat_512x512.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/cat_512x512.jpg b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/cat_512x512.jpg
new file mode 100644
index 0000000..bc30c0f
Binary files /dev/null and b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Assets.xcassets/cat_512x512.imageset/cat_512x512.jpg differ
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/ContentView.swift b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/ContentView.swift
new file mode 100644
index 0000000..95bd7d6
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/ContentView.swift
@@ -0,0 +1,25 @@
+//
+//  ContentView.swift
+//  SD2Img2Img
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import SwiftUI
+
+struct ContentView: View {
+    @StateObject var imageGenerator = ImageGenerator()
+    
+    var body: some View {
+        VStack {
+            ImageToImageView(imageGenerator: imageGenerator)
+        }
+        .padding()
+    }
+}
+
+struct ContentView_Previews: PreviewProvider {
+    static var previews: some View {
+        ContentView()
+    }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Preview Content/Preview Assets.xcassets/Contents.json b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Preview Content/Preview Assets.xcassets/Contents.json
new file mode 100644
index 0000000..73c0059
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Preview Content/Preview Assets.xcassets/Contents.json	
@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD/ImageGenerator.swift b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD/ImageGenerator.swift
new file mode 100644
index 0000000..3287d62
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD/ImageGenerator.swift
@@ -0,0 +1,167 @@
+//
+//  ImageGenerator.swift
+//  SD2Img2Img
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import Foundation
+import StableDiffusion
+import CoreML
+import UIKit
+
+// @MainActor: MainThread에서의 동작을 보장
+@MainActor
+final class ImageGenerator: ObservableObject {
+    
+    struct GenerationParameter {
+        var prompt: String
+        var negativePrompt: String
+        var guidanceScale: Float
+        var seed: Int
+        var stepCount: Int
+        var imageCount: Int
+        var disableSafety: Bool
+        var startImage: CGImage?
+        var strength: Float = 1.0
+    }
+    
+    struct GeneratedImage: Identifiable {
+        let id: UUID = UUID()
+        let uiImage: UIImage
+    }
+    
+    struct GeneratedImages {
+        let prompt: String
+        let negativePrompt: String
+        let guidanceScale: Float
+        let imageCount: Int
+        let stepCount: Int
+        let seed: Int
+        let disableSafety: Bool
+        let images: [GeneratedImage]
+    }
+    
+    enum GenerationState: Equatable {
+        case idle
+        case generating(progressStep: Int)
+        static func == (lhs: Self, rhs: Self) -> Bool {
+            switch(lhs, rhs){
+            case (.idle, idle):
+                return true
+            case (.generating(let step1), .generating(let step2)):
+                if step1 == step2 {
+                    return true
+                } else {
+                    return false
+                }
+            default:
+                return false
+            }
+        
+        }
+    }
+    
+    @Published var generationState: GenerationState = .idle
+    @Published var generatedImages: GeneratedImages?
+    @Published var isPipelineCreated = false
+    
+    private var sdPipeline: StableDiffusionPipeline?
+    
+    init() {
+        
+    }
+    
+    // MARK: Setter Funcs
+    func setState(_ state: GenerationState) {
+        generationState = state
+    }
+    
+    func setPipeline(_ pipeline: StableDiffusionPipeline) {
+        sdPipeline = pipeline
+        isPipelineCreated = true
+    }
+    
+    func setGeneratedImages(_ images: GeneratedImages) {
+        generatedImages = images
+    }
+    
+    // swiftlint:disable function_body_length
+    func generateImages(_ parameter: GenerationParameter) {
+        guard generationState == .idle else { return }
+        // Runs the given nonthrowing operation asynchronously as part of a new top-level task.
+        Task.detached(priority: .high) {
+            await self.setState(.generating(progressStep: 0))
+            
+            if await self.sdPipeline == nil {
+                guard let path = Bundle.main.path(forResource: "CoreMLModels", ofType: nil, inDirectory: nil) else {
+                    fatalError("Fatal error: failed to find the CoreML Models.")
+                }
+                let resourceURL = URL(fileURLWithPath: path)
+                
+                let config = MLModelConfiguration()
+                
+                /*
+                 [Note]
+                 Specifying config.computeUnits is not necessary. Use the default
+                 
+                 Specifying config.computeUnits = .cpuAndNeuralEngine will cause an internal fatal error on devices.
+                 config.computeUnits = .cpuAndNeuralEngine
+                 
+                 Specifying config.computeUnits = .cpuAndGPU works on device with no reason
+                 if !ProcessInfo.processInfo.isiOSAppOnMac {
+                    config.computeUnits = .cpuAndGPU
+                 }
+                 */
+                
+                // ReduceMemory option was added at v0.1.0
+                // On iOS, the reduceMemory option should be set to true
+                let reduceMemory = ProcessInfo.processInfo.isiOSAppOnMac ? false:true
+                if let pipeline = try? StableDiffusionPipeline(resourcesAt: resourceURL, configuration: config, reduceMemory: reduceMemory) {
+                    await self.setPipeline(pipeline)
+                } else {
+                    fatalError("Fatal error: failed to create the Stable-Diffusion-Pipeline.")
+                }
+            }
+            
+            if let sdPipeline = await self.sdPipeline {
+                do {
+                    // Will Add ProgressHandle
+                    
+                    var configuration = StableDiffusionPipeline.Configuration(prompt: parameter.prompt)
+                    configuration.negativePrompt = parameter.negativePrompt
+                    configuration.imageCount = parameter.imageCount
+                    configuration.stepCount = parameter.stepCount
+                    configuration.seed = UInt32(parameter.seed)
+                    configuration.guidanceScale = parameter.guidanceScale
+                    configuration.disableSafety = parameter.disableSafety
+                    
+                    configuration.startingImage = parameter.startImage
+                    configuration.strength = parameter.strength
+                    
+                    let cgImages = try sdPipeline.generateImages(configuration: configuration)
+                    print("Images were Generated")
+                    
+                    let uiImages = cgImages.compactMap { image in
+                        if let cgImage = image {
+                            return UIImage(cgImage: cgImage)
+                        } else {
+                            return nil
+                        }
+                    }
+                    
+                    await self.setGeneratedImages(GeneratedImages(prompt: parameter.prompt,
+                                                                  negativePrompt: parameter.negativePrompt,
+                                                                  guidanceScale: parameter.guidanceScale,
+                                                                  imageCount: parameter.imageCount,
+                                                                  stepCount: parameter.stepCount,
+                                                                  seed: parameter.seed,
+                                                                  disableSafety: parameter.disableSafety,
+                                                                  images: uiImages.map{ uiImage in GeneratedImage(uiImage: uiImage) }))
+                } catch {
+                    print("Failed to generate images.")
+                }
+            }
+        }
+    }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD2Img2ImgApp.swift b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD2Img2ImgApp.swift
new file mode 100644
index 0000000..d670822
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/SD2Img2ImgApp.swift
@@ -0,0 +1,17 @@
+//
+//  SD2Img2ImgApp.swift
+//  SD2Img2Img
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import SwiftUI
+
+@main
+struct SD2Img2ImgApp: App {
+    var body: some Scene {
+        WindowGroup {
+            ContentView()
+        }
+    }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/ImageToImageView.swift b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/ImageToImageView.swift
new file mode 100644
index 0000000..85bd02a
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/ImageToImageView.swift
@@ -0,0 +1,60 @@
+//
+//  ImageToImageView.swift
+//  SD2Img2Img
+//
+//  Created by HanGyo Jeong on 2023/03/24.
+//
+
+import SwiftUI
+
+struct ImageToImageView: View {
+    static let prompt = "happy smile pretty cat"
+    static let negativePrompt =
+"""
+lowers, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits,
+cropped, worst quality, low quality, normal quality, jpeg artifacts, blurry, multiple legs, malformation
+"""
+    static let startImageName = "cat_512x512"
+    
+    @ObservedObject var imageGenerator: ImageGenerator
+    @State private var generationParameter = ImageGenerator.GenerationParameter(prompt: prompt,
+                                                                                negativePrompt: negativePrompt,
+                                                                                guidanceScale: 8.0,
+                                                                                seed: 1_000_000,
+                                                                                stepCount: 20,
+                                                                                imageCount: 1,
+                                                                                disableSafety: false,
+                                                                                startImage: UIImage(named:startImageName)?.cgImage,
+                                                                                strength: 0.5)
+    
+    var body: some View {
+        ScrollView {
+            VStack {
+                Text("Image to Image").font(.title3).bold().padding(6)
+                Text("Sample App using apple/ml-stable-diffusion").foregroundColor(.secondary).font(.caption).padding(.bottom)
+                
+                Image(ImageToImageView.startImageName).resizable().scaledToFit().frame(height:200)
+                
+                PromptView(parameter: $generationParameter).disabled(imageGenerator.generationState != .idle)
+                
+                if imageGenerator.generationState == .idle {
+                    Button(action: generate) {
+                        Text("Generate").font(.title)
+                    }.buttonStyle(.borderedProminent)
+                } else {
+                    ProgressView()
+                }
+                
+                if let generatedImages = imageGenerator.generatedImages {
+                    ForEach(generatedImages.images) {
+                        Image(uiImage: $0.uiImage).resizable().scaledToFit()
+                    }
+                }
+            }
+        }.padding()
+    }
+    
+    func generate() {
+        imageGenerator.generateImages(generationParameter)
+    }
+}
diff --git a/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/PromptView.swift b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/PromptView.swift
new file mode 100644
index 0000000..6cbcf69
--- /dev/null
+++ b/CoreML/StableDiffusion/SD2Img2Img/SD2Img2Img/Views/PromptView.swift
@@ -0,0 +1,71 @@
+//
+//  PromptView.swift
+//  SD2Img2Img
+//
+//  Created by HanGyo Jeong on 2023/03/24.
+//
+
+import SwiftUI
+
+struct PromptView: View {
+    @Binding var parameter: ImageGenerator.GenerationParameter
+    
+    var body: some View {
+        VStack {
+            HStack{
+                Text("Prompt:");
+                Spacer()
+            }
+            TextField("Prompt:", text: $parameter.prompt).textFieldStyle(RoundedBorderTextFieldStyle())
+            
+            HStack {
+                Text("Negative Prompt:");
+                Spacer()
+            }
+            TextField("Negative Prompt:", text: $parameter.prompt).textFieldStyle(RoundedBorderTextFieldStyle())
+            
+            Stepper(value: $parameter.guidanceScale, in: 0.0...40.0, step: 0.5) {
+                Text("Guidance scale: \(parameter.guidanceScale, specifier: "%.1f")")
+            }
+            Stepper(value: $parameter.imageCount, in: 1...10) {
+                Text("Image Count: \(parameter.imageCount)")
+            }
+            Stepper(value: $parameter.stepCount, in: 1...100) {
+                Text("Iteration Steps: \(parameter.stepCount)")
+            }
+            
+            HStack{
+                Text("Seed:"); Spacer()
+            }
+            TextField("Seed number (0 ... 4_294_967_295)", value: $parameter.seed, formatter: NumberFormatter())
+                .textFieldStyle(RoundedBorderTextFieldStyle())
+                .onSubmit {
+                    if parameter.seed < 0 {
+                        parameter.seed = 0
+                    } else if parameter.seed > UInt32.max {
+                        parameter.seed = Int(UInt32.max)
+                    } else {
+                        // Do Nothing
+                    }
+                }
+            
+            Stepper(value: $parameter.strength, in: 0.0...0.9, step: 0.1) {
+                Text("Strength: \(parameter.strength, specifier: "%.1f")")
+            }
+        }.padding()
+    }
+}
+
+//struct PromptView_Previews: PreviewProvider {
+//    @State static var param = ImageGenerator.GenerationParameter(prompt: "a prompt",
+//                                                                 negativePrompt: "a negative prompt",
+//                                                                 guidanceScale: 0.5,
+//                                                                 seed: 1_000,
+//                                                                 stepCount: 20,
+//                                                                 imageCount: 1,
+//                                                                 disableSafety: false,
+//                                                                 strength: 0.5)
+//    static var previews: some View {
+//        PromptView(parameter: $param)
+//    }
+//}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.pbxproj b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.pbxproj
new file mode 100644
index 0000000..744d6a3
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.pbxproj
@@ -0,0 +1,367 @@
+// !$*UTF8*$!
+{
+	archiveVersion = 1;
+	classes = {
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+	objectVersion = 56;
+	objects = {
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+/* End PBXBuildFile section */
+
+/* Begin PBXFileReference section */
+		7840B1AF29CB543B00CDC138 /* FlowerClassification.app */ = {isa = PBXFileReference; explicitFileType = wrapper.application; includeInIndex = 0; path = FlowerClassification.app; sourceTree = BUILT_PRODUCTS_DIR; };
+		7840B1B229CB543B00CDC138 /* AppDelegate.swift */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.swift; path = AppDelegate.swift; sourceTree = "<group>"; };
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+		7840B1BE29CB543C00CDC138 /* Base */ = {isa = PBXFileReference; lastKnownFileType = file.storyboard; name = Base; path = Base.lproj/LaunchScreen.storyboard; sourceTree = "<group>"; };
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+/* End PBXFrameworksBuildPhase section */
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+				7840B1B129CB543B00CDC138 /* FlowerClassification */,
+				7840B1B029CB543B00CDC138 /* Products */,
+			);
+			sourceTree = "<group>";
+		};
+		7840B1B029CB543B00CDC138 /* Products */ = {
+			isa = PBXGroup;
+			children = (
+				7840B1AF29CB543B00CDC138 /* FlowerClassification.app */,
+			);
+			name = Products;
+			sourceTree = "<group>";
+		};
+		7840B1B129CB543B00CDC138 /* FlowerClassification */ = {
+			isa = PBXGroup;
+			children = (
+				7840B1C629CB545000CDC138 /* flowers.mlmodel */,
+				7840B1B229CB543B00CDC138 /* AppDelegate.swift */,
+				7840B1B429CB543B00CDC138 /* SceneDelegate.swift */,
+				7840B1B629CB543B00CDC138 /* ViewController.swift */,
+				7840B1B829CB543B00CDC138 /* Main.storyboard */,
+				7840B1BB29CB543C00CDC138 /* Assets.xcassets */,
+				7840B1BD29CB543C00CDC138 /* LaunchScreen.storyboard */,
+				7840B1C029CB543C00CDC138 /* Info.plist */,
+			);
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+			sourceTree = "<group>";
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+/* End PBXGroup section */
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+			buildPhases = (
+				7840B1AB29CB543B00CDC138 /* Sources */,
+				7840B1AC29CB543B00CDC138 /* Frameworks */,
+				7840B1AD29CB543B00CDC138 /* Resources */,
+			);
+			buildRules = (
+			);
+			dependencies = (
+			);
+			name = FlowerClassification;
+			productName = FlowerClassification;
+			productReference = 7840B1AF29CB543B00CDC138 /* FlowerClassification.app */;
+			productType = "com.apple.product-type.application";
+		};
+/* End PBXNativeTarget section */
+
+/* Begin PBXProject section */
+		7840B1A729CB543B00CDC138 /* Project object */ = {
+			isa = PBXProject;
+			attributes = {
+				BuildIndependentTargetsInParallel = 1;
+				LastSwiftUpdateCheck = 1420;
+				LastUpgradeCheck = 1420;
+				TargetAttributes = {
+					7840B1AE29CB543B00CDC138 = {
+						CreatedOnToolsVersion = 14.2;
+					};
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+			projectRoot = "";
+			targets = (
+				7840B1AE29CB543B00CDC138 /* FlowerClassification */,
+			);
+		};
+/* End PBXProject section */
+
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+			isa = PBXResourcesBuildPhase;
+			buildActionMask = 2147483647;
+			files = (
+				7840B1BF29CB543C00CDC138 /* LaunchScreen.storyboard in Resources */,
+				7840B1BC29CB543C00CDC138 /* Assets.xcassets in Resources */,
+				7840B1BA29CB543B00CDC138 /* Main.storyboard in Resources */,
+			);
+			runOnlyForDeploymentPostprocessing = 0;
+		};
+/* End PBXResourcesBuildPhase section */
+
+/* Begin PBXSourcesBuildPhase section */
+		7840B1AB29CB543B00CDC138 /* Sources */ = {
+			isa = PBXSourcesBuildPhase;
+			buildActionMask = 2147483647;
+			files = (
+				7840B1B729CB543B00CDC138 /* ViewController.swift in Sources */,
+				7840B1C729CB545100CDC138 /* flowers.mlmodel in Sources */,
+				7840B1B329CB543B00CDC138 /* AppDelegate.swift in Sources */,
+				7840B1B529CB543B00CDC138 /* SceneDelegate.swift in Sources */,
+			);
+			runOnlyForDeploymentPostprocessing = 0;
+		};
+/* End PBXSourcesBuildPhase section */
+
+/* Begin PBXVariantGroup section */
+		7840B1B829CB543B00CDC138 /* Main.storyboard */ = {
+			isa = PBXVariantGroup;
+			children = (
+				7840B1B929CB543B00CDC138 /* Base */,
+			);
+			name = Main.storyboard;
+			sourceTree = "<group>";
+		};
+		7840B1BD29CB543C00CDC138 /* LaunchScreen.storyboard */ = {
+			isa = PBXVariantGroup;
+			children = (
+				7840B1BE29CB543C00CDC138 /* Base */,
+			);
+			name = LaunchScreen.storyboard;
+			sourceTree = "<group>";
+		};
+/* End PBXVariantGroup section */
+
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+		7840B1C129CB543C00CDC138 /* Debug */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ALWAYS_SEARCH_USER_PATHS = NO;
+				CLANG_ANALYZER_NONNULL = YES;
+				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
+				CLANG_CXX_LANGUAGE_STANDARD = "gnu++20";
+				CLANG_ENABLE_MODULES = YES;
+				CLANG_ENABLE_OBJC_ARC = YES;
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+				CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
+				CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
+				CLANG_WARN_EMPTY_BODY = YES;
+				CLANG_WARN_ENUM_CONVERSION = YES;
+				CLANG_WARN_INFINITE_RECURSION = YES;
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+				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
+				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
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+				CLANG_WARN_STRICT_PROTOTYPES = YES;
+				CLANG_WARN_SUSPICIOUS_MOVE = YES;
+				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
+				CLANG_WARN_UNREACHABLE_CODE = YES;
+				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
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+				DEBUG_INFORMATION_FORMAT = dwarf;
+				ENABLE_STRICT_OBJC_MSGSEND = YES;
+				ENABLE_TESTABILITY = YES;
+				GCC_C_LANGUAGE_STANDARD = gnu11;
+				GCC_DYNAMIC_NO_PIC = NO;
+				GCC_NO_COMMON_BLOCKS = YES;
+				GCC_OPTIMIZATION_LEVEL = 0;
+				GCC_PREPROCESSOR_DEFINITIONS = (
+					"DEBUG=1",
+					"$(inherited)",
+				);
+				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
+				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
+				GCC_WARN_UNDECLARED_SELECTOR = YES;
+				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
+				GCC_WARN_UNUSED_FUNCTION = YES;
+				GCC_WARN_UNUSED_VARIABLE = YES;
+				IPHONEOS_DEPLOYMENT_TARGET = 16.2;
+				MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
+				MTL_FAST_MATH = YES;
+				ONLY_ACTIVE_ARCH = YES;
+				SDKROOT = iphoneos;
+				SWIFT_ACTIVE_COMPILATION_CONDITIONS = DEBUG;
+				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
+			};
+			name = Debug;
+		};
+		7840B1C229CB543C00CDC138 /* Release */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ALWAYS_SEARCH_USER_PATHS = NO;
+				CLANG_ANALYZER_NONNULL = YES;
+				CLANG_ANALYZER_NUMBER_OBJECT_CONVERSION = YES_AGGRESSIVE;
+				CLANG_CXX_LANGUAGE_STANDARD = "gnu++20";
+				CLANG_ENABLE_MODULES = YES;
+				CLANG_ENABLE_OBJC_ARC = YES;
+				CLANG_ENABLE_OBJC_WEAK = YES;
+				CLANG_WARN_BLOCK_CAPTURE_AUTORELEASING = YES;
+				CLANG_WARN_BOOL_CONVERSION = YES;
+				CLANG_WARN_COMMA = YES;
+				CLANG_WARN_CONSTANT_CONVERSION = YES;
+				CLANG_WARN_DEPRECATED_OBJC_IMPLEMENTATIONS = YES;
+				CLANG_WARN_DIRECT_OBJC_ISA_USAGE = YES_ERROR;
+				CLANG_WARN_DOCUMENTATION_COMMENTS = YES;
+				CLANG_WARN_EMPTY_BODY = YES;
+				CLANG_WARN_ENUM_CONVERSION = YES;
+				CLANG_WARN_INFINITE_RECURSION = YES;
+				CLANG_WARN_INT_CONVERSION = YES;
+				CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
+				CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
+				CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
+				CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
+				CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
+				CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
+				CLANG_WARN_STRICT_PROTOTYPES = YES;
+				CLANG_WARN_SUSPICIOUS_MOVE = YES;
+				CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
+				CLANG_WARN_UNREACHABLE_CODE = YES;
+				CLANG_WARN__DUPLICATE_METHOD_MATCH = YES;
+				COPY_PHASE_STRIP = NO;
+				DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
+				ENABLE_NS_ASSERTIONS = NO;
+				ENABLE_STRICT_OBJC_MSGSEND = YES;
+				GCC_C_LANGUAGE_STANDARD = gnu11;
+				GCC_NO_COMMON_BLOCKS = YES;
+				GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
+				GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
+				GCC_WARN_UNDECLARED_SELECTOR = YES;
+				GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
+				GCC_WARN_UNUSED_FUNCTION = YES;
+				GCC_WARN_UNUSED_VARIABLE = YES;
+				IPHONEOS_DEPLOYMENT_TARGET = 16.2;
+				MTL_ENABLE_DEBUG_INFO = NO;
+				MTL_FAST_MATH = YES;
+				SDKROOT = iphoneos;
+				SWIFT_COMPILATION_MODE = wholemodule;
+				SWIFT_OPTIMIZATION_LEVEL = "-O";
+				VALIDATE_PRODUCT = YES;
+			};
+			name = Release;
+		};
+		7840B1C429CB543C00CDC138 /* Debug */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+				CODE_SIGN_STYLE = Automatic;
+				CURRENT_PROJECT_VERSION = 1;
+				DEVELOPMENT_TEAM = KYV3CSY2F3;
+				GENERATE_INFOPLIST_FILE = YES;
+				INFOPLIST_FILE = FlowerClassification/Info.plist;
+				INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+				INFOPLIST_KEY_UILaunchStoryboardName = LaunchScreen;
+				INFOPLIST_KEY_UIMainStoryboardFile = Main;
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				LD_RUNPATH_SEARCH_PATHS = (
+					"$(inherited)",
+					"@executable_path/Frameworks",
+				);
+				MARKETING_VERSION = 1.0;
+				PRODUCT_BUNDLE_IDENTIFIER = com.commitguy.flowerclassification.FlowerClassification;
+				PRODUCT_NAME = "$(TARGET_NAME)";
+				SWIFT_EMIT_LOC_STRINGS = YES;
+				SWIFT_VERSION = 5.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
+			};
+			name = Debug;
+		};
+		7840B1C529CB543C00CDC138 /* Release */ = {
+			isa = XCBuildConfiguration;
+			buildSettings = {
+				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
+				ASSETCATALOG_COMPILER_GLOBAL_ACCENT_COLOR_NAME = AccentColor;
+				CODE_SIGN_STYLE = Automatic;
+				CURRENT_PROJECT_VERSION = 1;
+				DEVELOPMENT_TEAM = KYV3CSY2F3;
+				GENERATE_INFOPLIST_FILE = YES;
+				INFOPLIST_FILE = FlowerClassification/Info.plist;
+				INFOPLIST_KEY_UIApplicationSupportsIndirectInputEvents = YES;
+				INFOPLIST_KEY_UILaunchStoryboardName = LaunchScreen;
+				INFOPLIST_KEY_UIMainStoryboardFile = Main;
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPad = "UIInterfaceOrientationPortrait UIInterfaceOrientationPortraitUpsideDown UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				INFOPLIST_KEY_UISupportedInterfaceOrientations_iPhone = "UIInterfaceOrientationPortrait UIInterfaceOrientationLandscapeLeft UIInterfaceOrientationLandscapeRight";
+				LD_RUNPATH_SEARCH_PATHS = (
+					"$(inherited)",
+					"@executable_path/Frameworks",
+				);
+				MARKETING_VERSION = 1.0;
+				PRODUCT_BUNDLE_IDENTIFIER = com.commitguy.flowerclassification.FlowerClassification;
+				PRODUCT_NAME = "$(TARGET_NAME)";
+				SWIFT_EMIT_LOC_STRINGS = YES;
+				SWIFT_VERSION = 5.0;
+				TARGETED_DEVICE_FAMILY = "1,2";
+			};
+			name = Release;
+		};
+/* End XCBuildConfiguration section */
+
+/* Begin XCConfigurationList section */
+		7840B1AA29CB543B00CDC138 /* Build configuration list for PBXProject "FlowerClassification" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				7840B1C129CB543C00CDC138 /* Debug */,
+				7840B1C229CB543C00CDC138 /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
+		7840B1C329CB543C00CDC138 /* Build configuration list for PBXNativeTarget "FlowerClassification" */ = {
+			isa = XCConfigurationList;
+			buildConfigurations = (
+				7840B1C429CB543C00CDC138 /* Debug */,
+				7840B1C529CB543C00CDC138 /* Release */,
+			);
+			defaultConfigurationIsVisible = 0;
+			defaultConfigurationName = Release;
+		};
+/* End XCConfigurationList section */
+	};
+	rootObject = 7840B1A729CB543B00CDC138 /* Project object */;
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.xcworkspace/contents.xcworkspacedata b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.xcworkspace/contents.xcworkspacedata
new file mode 100644
index 0000000..919434a
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/project.xcworkspace/contents.xcworkspacedata
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Workspace
+   version = "1.0">
+   <FileRef
+      location = "self:">
+   </FileRef>
+</Workspace>
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist
new file mode 100644
index 0000000..e964d1e
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification.xcodeproj/xcuserdata/hangyojeong.xcuserdatad/xcschemes/xcschememanagement.plist
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+	<key>SchemeUserState</key>
+	<dict>
+		<key>FlowerClassification.xcscheme_^#shared#^_</key>
+		<dict>
+			<key>orderHint</key>
+			<integer>0</integer>
+		</dict>
+	</dict>
+</dict>
+</plist>
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/AppDelegate.swift b/CoreML/Swift/FlowerClassification/FlowerClassification/AppDelegate.swift
new file mode 100644
index 0000000..073492d
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/AppDelegate.swift
@@ -0,0 +1,36 @@
+//
+//  AppDelegate.swift
+//  FlowerClassification
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import UIKit
+
+@main
+class AppDelegate: UIResponder, UIApplicationDelegate {
+
+
+
+    func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {
+        // Override point for customization after application launch.
+        return true
+    }
+
+    // MARK: UISceneSession Lifecycle
+
+    func application(_ application: UIApplication, configurationForConnecting connectingSceneSession: UISceneSession, options: UIScene.ConnectionOptions) -> UISceneConfiguration {
+        // Called when a new scene session is being created.
+        // Use this method to select a configuration to create the new scene with.
+        return UISceneConfiguration(name: "Default Configuration", sessionRole: connectingSceneSession.role)
+    }
+
+    func application(_ application: UIApplication, didDiscardSceneSessions sceneSessions: Set<UISceneSession>) {
+        // Called when the user discards a scene session.
+        // If any sessions were discarded while the application was not running, this will be called shortly after application:didFinishLaunchingWithOptions.
+        // Use this method to release any resources that were specific to the discarded scenes, as they will not return.
+    }
+
+
+}
+
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/5673728_71b8cb57eb.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/5673728_71b8cb57eb.jpg
new file mode 100644
index 0000000..2cb83fe
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/5673728_71b8cb57eb.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/Contents.json
new file mode 100644
index 0000000..bb0bc0d
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/1.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "5673728_71b8cb57eb.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/8684108_a85764b22d_n.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/8684108_a85764b22d_n.jpg
new file mode 100644
index 0000000..bbdff1c
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/8684108_a85764b22d_n.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/Contents.json
new file mode 100644
index 0000000..e788569
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/2.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "8684108_a85764b22d_n.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/10919961_0af657c4e8.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/10919961_0af657c4e8.jpg
new file mode 100644
index 0000000..6db67f0
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/10919961_0af657c4e8.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/Contents.json
new file mode 100644
index 0000000..d894938
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/3.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "10919961_0af657c4e8.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/11746276_de3dec8201.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/11746276_de3dec8201.jpg
new file mode 100644
index 0000000..55297ed
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/11746276_de3dec8201.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/Contents.json
new file mode 100644
index 0000000..902a179
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/4.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "11746276_de3dec8201.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/26254755_1bfc494ef1_n.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/26254755_1bfc494ef1_n.jpg
new file mode 100644
index 0000000..46141a0
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/26254755_1bfc494ef1_n.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/Contents.json
new file mode 100644
index 0000000..7fb5041
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/5.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "26254755_1bfc494ef1_n.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/123128873_546b8b7355_n.jpg b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/123128873_546b8b7355_n.jpg
new file mode 100644
index 0000000..8106451
Binary files /dev/null and b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/123128873_546b8b7355_n.jpg differ
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/Contents.json
new file mode 100644
index 0000000..2e56cff
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/6.imageset/Contents.json
@@ -0,0 +1,21 @@
+{
+  "images" : [
+    {
+      "filename" : "123128873_546b8b7355_n.jpg",
+      "idiom" : "universal",
+      "scale" : "1x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "2x"
+    },
+    {
+      "idiom" : "universal",
+      "scale" : "3x"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AccentColor.colorset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AccentColor.colorset/Contents.json
new file mode 100644
index 0000000..eb87897
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AccentColor.colorset/Contents.json
@@ -0,0 +1,11 @@
+{
+  "colors" : [
+    {
+      "idiom" : "universal"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AppIcon.appiconset/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AppIcon.appiconset/Contents.json
new file mode 100644
index 0000000..13613e3
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/AppIcon.appiconset/Contents.json
@@ -0,0 +1,13 @@
+{
+  "images" : [
+    {
+      "idiom" : "universal",
+      "platform" : "ios",
+      "size" : "1024x1024"
+    }
+  ],
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/Contents.json b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/Contents.json
new file mode 100644
index 0000000..73c0059
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Assets.xcassets/Contents.json
@@ -0,0 +1,6 @@
+{
+  "info" : {
+    "author" : "xcode",
+    "version" : 1
+  }
+}
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/LaunchScreen.storyboard b/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/LaunchScreen.storyboard
new file mode 100644
index 0000000..865e932
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/LaunchScreen.storyboard
@@ -0,0 +1,25 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="13122.16" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" launchScreen="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="01J-lp-oVM">
+    <dependencies>
+        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="13104.12"/>
+        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
+        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
+    </dependencies>
+    <scenes>
+        <!--View Controller-->
+        <scene sceneID="EHf-IW-A2E">
+            <objects>
+                <viewController id="01J-lp-oVM" sceneMemberID="viewController">
+                    <view key="view" contentMode="scaleToFill" id="Ze5-6b-2t3">
+                        <rect key="frame" x="0.0" y="0.0" width="375" height="667"/>
+                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
+                        <color key="backgroundColor" xcode11CocoaTouchSystemColor="systemBackgroundColor" cocoaTouchSystemColor="whiteColor"/>
+                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
+                    </view>
+                </viewController>
+                <placeholder placeholderIdentifier="IBFirstResponder" id="iYj-Kq-Ea1" userLabel="First Responder" sceneMemberID="firstResponder"/>
+            </objects>
+            <point key="canvasLocation" x="53" y="375"/>
+        </scene>
+    </scenes>
+</document>
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/Main.storyboard b/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/Main.storyboard
new file mode 100644
index 0000000..8b7c641
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Base.lproj/Main.storyboard
@@ -0,0 +1,108 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<document type="com.apple.InterfaceBuilder3.CocoaTouch.Storyboard.XIB" version="3.0" toolsVersion="21507" targetRuntime="iOS.CocoaTouch" propertyAccessControl="none" useAutolayout="YES" useTraitCollections="YES" useSafeAreas="YES" colorMatched="YES" initialViewController="BYZ-38-t0r">
+    <device id="retina6_12" orientation="portrait" appearance="light"/>
+    <dependencies>
+        <plugIn identifier="com.apple.InterfaceBuilder.IBCocoaTouchPlugin" version="21505"/>
+        <capability name="Safe area layout guides" minToolsVersion="9.0"/>
+        <capability name="System colors in document resources" minToolsVersion="11.0"/>
+        <capability name="documents saved in the Xcode 8 format" minToolsVersion="8.0"/>
+    </dependencies>
+    <scenes>
+        <!--View Controller-->
+        <scene sceneID="tne-QT-ifu">
+            <objects>
+                <viewController id="BYZ-38-t0r" customClass="ViewController" customModule="FlowerClassification" customModuleProvider="target" sceneMemberID="viewController">
+                    <view key="view" contentMode="scaleToFill" id="8bC-Xf-vdC">
+                        <rect key="frame" x="0.0" y="0.0" width="393" height="852"/>
+                        <autoresizingMask key="autoresizingMask" widthSizable="YES" heightSizable="YES"/>
+                        <subviews>
+                            <imageView clipsSubviews="YES" userInteractionEnabled="NO" contentMode="scaleAspectFit" horizontalHuggingPriority="251" verticalHuggingPriority="251" translatesAutoresizingMaskIntoConstraints="NO" id="GUn-pn-Nl8">
+                                <rect key="frame" x="16" y="59" width="361" height="214"/>
+                                <constraints>
+                                    <constraint firstAttribute="height" constant="214" id="8UT-If-hVF"/>
+                                    <constraint firstAttribute="width" secondItem="GUn-pn-Nl8" secondAttribute="height" multiplier="361:214" id="bBb-Ij-pYq"/>
+                                </constraints>
+                            </imageView>
+                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="system" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="1xA-F5-xPE" userLabel="PrevBtn">
+                                <rect key="frame" x="12" y="298" width="83" height="35"/>
+                                <constraints>
+                                    <constraint firstAttribute="width" secondItem="1xA-F5-xPE" secondAttribute="height" multiplier="83:35" id="I0y-vS-pED"/>
+                                    <constraint firstAttribute="width" constant="83" id="Iny-5o-ue2"/>
+                                    <constraint firstAttribute="height" constant="35" id="lRE-f8-px8"/>
+                                </constraints>
+                                <state key="normal" title="Button"/>
+                                <buttonConfiguration key="configuration" style="plain" title="&lt;&lt; Prev"/>
+                                <connections>
+                                    <action selector="prevButton:" destination="BYZ-38-t0r" eventType="touchUpInside" id="0KH-12-xvf"/>
+                                </connections>
+                            </button>
+                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="system" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="cq1-1C-iHO" userLabel="NextBtn">
+                                <rect key="frame" x="297" y="298" width="85" height="35"/>
+                                <constraints>
+                                    <constraint firstAttribute="width" constant="85" id="H5C-yE-NNh"/>
+                                    <constraint firstAttribute="width" secondItem="cq1-1C-iHO" secondAttribute="height" multiplier="17:7" id="jmo-Jg-dzF"/>
+                                    <constraint firstAttribute="height" constant="35" id="qio-gz-dCA"/>
+                                </constraints>
+                                <state key="normal" title="Button"/>
+                                <buttonConfiguration key="configuration" style="plain" title="Next &gt;&gt;"/>
+                                <connections>
+                                    <action selector="nextButton:" destination="BYZ-38-t0r" eventType="touchUpInside" id="H4B-my-UgX"/>
+                                </connections>
+                            </button>
+                            <button opaque="NO" contentMode="scaleToFill" contentHorizontalAlignment="center" contentVerticalAlignment="center" buttonType="system" lineBreakMode="middleTruncation" translatesAutoresizingMaskIntoConstraints="NO" id="Ygq-r4-PIK">
+                                <rect key="frame" x="154.66666666666666" y="361" width="84" height="35"/>
+                                <constraints>
+                                    <constraint firstAttribute="width" secondItem="Ygq-r4-PIK" secondAttribute="height" multiplier="12:5" id="l55-4p-y9J"/>
+                                </constraints>
+                                <state key="normal" title="Button"/>
+                                <buttonConfiguration key="configuration" style="plain" title="Classify"/>
+                                <connections>
+                                    <action selector="classifyButton:" destination="BYZ-38-t0r" eventType="touchUpInside" id="Qdw-zd-BOJ"/>
+                                </connections>
+                            </button>
+                            <label opaque="NO" userInteractionEnabled="NO" contentMode="left" horizontalHuggingPriority="251" verticalHuggingPriority="251" text="Label" lineBreakMode="tailTruncation" numberOfLines="3" baselineAdjustment="alignBaselines" adjustsFontSizeToFit="NO" translatesAutoresizingMaskIntoConstraints="NO" id="yGi-n6-beI">
+                                <rect key="frame" x="16" y="389.66666666666669" width="361" height="73"/>
+                                <constraints>
+                                    <constraint firstAttribute="height" constant="73" id="b4E-rI-FDT"/>
+                                    <constraint firstAttribute="width" constant="361" id="j5b-oG-d4G"/>
+                                    <constraint firstAttribute="width" secondItem="yGi-n6-beI" secondAttribute="height" multiplier="361:73" id="r1E-iZ-hEO"/>
+                                </constraints>
+                                <fontDescription key="fontDescription" type="system" pointSize="17"/>
+                                <nil key="textColor"/>
+                                <nil key="highlightedColor"/>
+                            </label>
+                        </subviews>
+                        <viewLayoutGuide key="safeArea" id="6Tk-OE-BBY"/>
+                        <color key="backgroundColor" systemColor="systemBackgroundColor"/>
+                        <constraints>
+                            <constraint firstItem="yGi-n6-beI" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="9Ij-Ws-xCh"/>
+                            <constraint firstItem="GUn-pn-Nl8" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="16" id="IKK-ky-cmY"/>
+                            <constraint firstItem="1xA-F5-xPE" firstAttribute="top" secondItem="GUn-pn-Nl8" secondAttribute="bottom" constant="25" id="L3N-Ox-NUd"/>
+                            <constraint firstItem="cq1-1C-iHO" firstAttribute="top" secondItem="GUn-pn-Nl8" secondAttribute="bottom" constant="25" id="T0K-zo-f5I"/>
+                            <constraint firstItem="6Tk-OE-BBY" firstAttribute="trailing" secondItem="GUn-pn-Nl8" secondAttribute="trailing" constant="16" id="UCw-al-lKS"/>
+                            <constraint firstItem="GUn-pn-Nl8" firstAttribute="top" secondItem="6Tk-OE-BBY" secondAttribute="top" id="Ufv-II-Moc"/>
+                            <constraint firstItem="6Tk-OE-BBY" firstAttribute="trailing" secondItem="cq1-1C-iHO" secondAttribute="trailing" constant="11" id="c3U-Fg-tDk"/>
+                            <constraint firstItem="yGi-n6-beI" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="16" id="e3C-0S-Cob"/>
+                            <constraint firstItem="6Tk-OE-BBY" firstAttribute="trailing" secondItem="yGi-n6-beI" secondAttribute="trailing" constant="16" id="kz1-zs-K4F"/>
+                            <constraint firstItem="yGi-n6-beI" firstAttribute="centerY" secondItem="8bC-Xf-vdC" secondAttribute="centerY" id="mZo-iJ-uvS"/>
+                            <constraint firstItem="1xA-F5-xPE" firstAttribute="leading" secondItem="6Tk-OE-BBY" secondAttribute="leading" constant="12" id="p6d-F0-TkG"/>
+                            <constraint firstItem="Ygq-r4-PIK" firstAttribute="centerX" secondItem="8bC-Xf-vdC" secondAttribute="centerX" id="xLD-lZ-YOH"/>
+                            <constraint firstItem="Ygq-r4-PIK" firstAttribute="top" secondItem="GUn-pn-Nl8" secondAttribute="bottom" constant="88" id="xOM-hr-ohc"/>
+                        </constraints>
+                    </view>
+                    <connections>
+                        <outlet property="imageView" destination="GUn-pn-Nl8" id="7Dc-s9-BbZ"/>
+                        <outlet property="txtOutput" destination="yGi-n6-beI" id="Odq-no-qsa"/>
+                    </connections>
+                </viewController>
+                <placeholder placeholderIdentifier="IBFirstResponder" id="dkx-z0-nzr" sceneMemberID="firstResponder"/>
+            </objects>
+            <point key="canvasLocation" x="9.9236641221374047" y="3.5211267605633805"/>
+        </scene>
+    </scenes>
+    <resources>
+        <systemColor name="systemBackgroundColor">
+            <color white="1" alpha="1" colorSpace="custom" customColorSpace="genericGamma22GrayColorSpace"/>
+        </systemColor>
+    </resources>
+</document>
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/Info.plist b/CoreML/Swift/FlowerClassification/FlowerClassification/Info.plist
new file mode 100644
index 0000000..dd3c9af
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/Info.plist
@@ -0,0 +1,25 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+	<key>UIApplicationSceneManifest</key>
+	<dict>
+		<key>UIApplicationSupportsMultipleScenes</key>
+		<false/>
+		<key>UISceneConfigurations</key>
+		<dict>
+			<key>UIWindowSceneSessionRoleApplication</key>
+			<array>
+				<dict>
+					<key>UISceneConfigurationName</key>
+					<string>Default Configuration</string>
+					<key>UISceneDelegateClassName</key>
+					<string>$(PRODUCT_MODULE_NAME).SceneDelegate</string>
+					<key>UISceneStoryboardFile</key>
+					<string>Main</string>
+				</dict>
+			</array>
+		</dict>
+	</dict>
+</dict>
+</plist>
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/SceneDelegate.swift b/CoreML/Swift/FlowerClassification/FlowerClassification/SceneDelegate.swift
new file mode 100644
index 0000000..69c5870
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/SceneDelegate.swift
@@ -0,0 +1,52 @@
+//
+//  SceneDelegate.swift
+//  FlowerClassification
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import UIKit
+
+class SceneDelegate: UIResponder, UIWindowSceneDelegate {
+
+    var window: UIWindow?
+
+
+    func scene(_ scene: UIScene, willConnectTo session: UISceneSession, options connectionOptions: UIScene.ConnectionOptions) {
+        // Use this method to optionally configure and attach the UIWindow `window` to the provided UIWindowScene `scene`.
+        // If using a storyboard, the `window` property will automatically be initialized and attached to the scene.
+        // This delegate does not imply the connecting scene or session are new (see `application:configurationForConnectingSceneSession` instead).
+        guard let _ = (scene as? UIWindowScene) else { return }
+    }
+
+    func sceneDidDisconnect(_ scene: UIScene) {
+        // Called as the scene is being released by the system.
+        // This occurs shortly after the scene enters the background, or when its session is discarded.
+        // Release any resources associated with this scene that can be re-created the next time the scene connects.
+        // The scene may re-connect later, as its session was not necessarily discarded (see `application:didDiscardSceneSessions` instead).
+    }
+
+    func sceneDidBecomeActive(_ scene: UIScene) {
+        // Called when the scene has moved from an inactive state to an active state.
+        // Use this method to restart any tasks that were paused (or not yet started) when the scene was inactive.
+    }
+
+    func sceneWillResignActive(_ scene: UIScene) {
+        // Called when the scene will move from an active state to an inactive state.
+        // This may occur due to temporary interruptions (ex. an incoming phone call).
+    }
+
+    func sceneWillEnterForeground(_ scene: UIScene) {
+        // Called as the scene transitions from the background to the foreground.
+        // Use this method to undo the changes made on entering the background.
+    }
+
+    func sceneDidEnterBackground(_ scene: UIScene) {
+        // Called as the scene transitions from the foreground to the background.
+        // Use this method to save data, release shared resources, and store enough scene-specific state information
+        // to restore the scene back to its current state.
+    }
+
+
+}
+
diff --git a/CoreML/Swift/FlowerClassification/FlowerClassification/ViewController.swift b/CoreML/Swift/FlowerClassification/FlowerClassification/ViewController.swift
new file mode 100644
index 0000000..7596e4f
--- /dev/null
+++ b/CoreML/Swift/FlowerClassification/FlowerClassification/ViewController.swift
@@ -0,0 +1,100 @@
+//
+//  ViewController.swift
+//  FlowerClassification
+//
+//  Created by HanGyo Jeong on 2023/03/23.
+//
+
+import UIKit
+import CoreML
+import Vision
+
+class ViewController: UIViewController {
+    let NUM_CLASSES = 5
+    var currentImage = 1
+    
+    @IBOutlet weak var txtOutput: UILabel!
+    @IBOutlet weak var imageView: UIImageView!
+    
+    override func viewDidLoad() {
+        super.viewDidLoad()
+        // Do any additional setup after loading the view.
+    }
+
+    func interpretImage() {
+        let theImage: UIImage = UIImage(named: String(currentImage))!
+        getClassification(for: theImage)
+    }
+    
+    @IBAction func prevButton(_ sender: Any) {
+        currentImage = currentImage - 1
+        if currentImage <= 0 {
+            currentImage = 6
+        }
+        loadImage()
+    }
+    @IBAction func nextButton(_ sender: Any) {
+        currentImage = currentImage + 1
+        if currentImage >= 7 {
+            currentImage = 1
+        }
+        loadImage()
+    }
+    @IBAction func classifyButton(_ sender: Any) {
+        interpretImage()
+    }
+    
+    func loadImage(){
+            imageView.image = UIImage(named: String(currentImage))
+        }
+    
+    func getClassification(for image: UIImage) {
+        let orientation = CGImagePropertyOrientation(rawValue: UInt32(image.imageOrientation.rawValue))!
+        guard let ciImage = CIImage(image: image) else { fatalError("...") }
+        
+        DispatchQueue.global(qos: .userInitiated).async {
+            let handler = VNImageRequestHandler(ciImage: ciImage, orientation: orientation)
+            
+            do {
+                try handler.perform([self.classificationRequest])
+            } catch {
+                print("...")
+            }
+        }
+    }
+    
+    // VNCoreMLRequest는 내부적으로 모델 초기화를 함
+    lazy var classificationRequest: VNCoreMLRequest = {
+        do {
+            let model = try VNCoreMLModel.init(for: flowers().model)
+            let request = VNCoreMLRequest(model: model, completionHandler: { [weak self] request, error in
+                self?.processResults(for: request, error: error)
+            })
+            request.imageCropAndScaleOption = .centerCrop
+            return request
+        } catch {
+            fatalError("...")
+        }
+    }()
+    
+    func processResults(for request: VNRequest, error: Error?) {
+        DispatchQueue.main.async {
+            guard let results = request.results else {
+                self.txtOutput.text = "..."
+                return
+            }
+            let classifications = results as! [VNClassificationObservation]
+            
+            if classifications.isEmpty {
+                self.txtOutput.text = "Nothing recognized."
+            } else {
+                let topClassifications = classifications.prefix(self.NUM_CLASSES)
+                let descriptions = topClassifications.map {
+                    classification in return String(format: " (%.2f) %@", classification.confidence, classification.identifier)
+                }
+                self.txtOutput.text = "Classification:\n" + descriptions.joined(separator: "\n")
+            }
+        }
+    }
+}
+
diff --git a/CoreML/flowers.mlmodel b/CoreML/flowers.mlmodel
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diff --git a/CoreML/images/ml_cloud.png b/CoreML/images/ml_cloud.png
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diff --git a/CoreML/images/modeling_editor.png b/CoreML/images/modeling_editor.png
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diff --git a/Transformer/01_TextClassification/README.md b/Transformer/01_TextClassification/README.md
new file mode 100644
index 0000000..c95579c
--- /dev/null
+++ b/Transformer/01_TextClassification/README.md
@@ -0,0 +1,213 @@
+# Text Classification using DistilBERT
+Huggingface의 DistilBERT(BERT와 비교해 성능은 비슷하지만 훨씬 작고 효율적) 모델을 사용해 6개의 감정 모델을 분류 <br>
+Huggingface 생태계의 핵심 라이브러리 사용
+- **데이터셋(Dataset)**
+- **토크나이저(Tokenizer)**
+- **트랜스포머스(Transformers)**
+
+## 1. 데이터셋
+### 허깅페이스 데이터셋 사용
+- **list_datasets()** 함수를 사용하면 허브에서 제공하는 데이터셋 목록이 출력됨
+- **load_dataset()** 함수로 데이터셋 로드 
+
+### Huggingface hub에 데이터셋이 없는 경우
+- 데이터셋은 로컬 데이터셋이나 원격 데이터셋에 사용 가능한 로딩 스크립트를 제공해줌
+- 포멧에 따른 데이터셋 로딩 방법
+    - CSV: load_dataset("csv", data_files="my_file.csv")
+    - text: load_dataset("text", data_files="my_file.txt")
+    - JSON: load_dataset("json", data_files="my_file.jsonl")
+- 원격 데이터 로딩 방법
+```
+dataset_url = "https://www.~/train.txt"
+!wget {dataset_url}
+emotions_local = load_dataset("csv", data_files="train.txt", sep=";", names=["text", "label"])
+```
+구분자(sep)와 열 이름(names)을 지정
+```
+dataset_url = "https://www.~/train.txt"
+emotions_local = load_dataset("csv", data_files=dataset_url, sep=";", names=["text", "label"])
+```
+
+### 데이터셋 to 데이터프레임
+- Dataset 객체를 판다스 DataFrame으로 변환하게 되면 고수준 데이터 시각화 API 등 편리한 API를 사용할 수 있음 
+- 데이터셋은 Dataset의 출력 포맷을 변경하는 **set_format()** 메서드를 제공
+
+### 클래스 분포 확인
+- 텍스트 분류 문제를 다룰땐 샘플의 클래스 분포 조사를 해야 함
+- 클래스 분포가 편향된 데이터 셋은 훈련 손실과 평가 지표 측면에서 균형 잡힌 데이터셋과 다른 처리법이 필요
+- pandas와 matplot을 이용하면 클래스 분포를 시각화할 수 있음
+
+- 데이터셋이 불균형 한 경우 처리방법
+    - 소수 클래스를 랜덤하게 오버샘플링(oversampling)
+    - 다수 클래스를 랜덤하게 언더샘플링(undersampling)
+    - 클래스의 대표성이 부족하다면 레이블된 데이터를 더 많이 수집 
+
+- 훈련/테스트 분할을 만들기 전에는 샘플링 전략을 사용하면 안됨 그렇지 않으면 분할 사이에 많은 정보가 새어나감
+    - 일반적으로 훈련 세트에만 샘플링 전략을 사용 
+- 불균형한 클래스 분포에서의 샘플링 기법: https://imbalanced-learn.org/stable/
+
+### 길이 확인
+- 트랜스포머 모델은 **최대 문맥 크기(maximum context size)** 라는 최대 입력 시퀀스 길이가 존재
+    - DistilBERT를 사용하는 애플리케이션에서 최대 문맥 크기는 512 토큰
+
+## 2. 토큰나이저
+- 토큰화란 문자열을 모델이 사용하는 기본 단위로 분할하는 단계
+
+### 문자 토큰화
+- 가장 간단한 토큰화 방법으로 각 문자를 개별로 모델에 주입하는 것
+- 텍스트를 숫치 데이터로 변환한 후 one-hot vector의 2D 텐서로 변경
+- one-hot vector는 machine learning에서 순서형(ordinal) 또는 명목형(nominal) 범주 데이터를 인코딩 하기 위해 자주 사용
+    - 텍스트를 단순히 숫자로만 변경할시 이름 사이에 가상의 순서 또는 상관없는 관계가 생길수 있음
+- 문자 토큰화는 철자 오류, 희귀단어 처리 등 장점이 있지만 단어 같은 언어 구조를 이 데이터에서 학습해야 한다는 큰 단점이 존재
+    - 문자 수준의 토큰화는 거의 사용되지 않음
+
+### 단어 토큰화
+- 텍스트를 문자가 아닌 단어로 분할, 단어를 정수로 매핑
+- 처음부터 단어를 사용하면 모델이 문자에서 단어를 학습하는 단계가 생략되어 훈련 과정의 복잡도가 감소함
+- 단어 토큰화는 단어에 곡용, 활용형, 철자 오류가 포함되어 어휘사전이 금세 수백만 개까지 늘어나는 단점이 존재
+    - 어휘 사전이 크면 신경망의 파라미터 역시 많이 필요해짐
+    - 어휘사전의 크기를 제한하는 일반적 방법은 드물게 등장하는 단어는 무시
+        - ex)말뭉치에서 자주 등장하는 10만개 단어만 사용 
+    - but, 그렇게 되면 단어 토큰화 과정에서 중요 정보를 일부 잃게 됨
+
+### 부분단어 토큰화
+- 부분단어 토큰화는 문자 토큰화와 단어 토큰화의 장점을 결합
+- 드물게 등장하는 단어를 더 작은 단위로 나누면 모델이 복잡한 단어나 철자 오류를 처리하기 용이
+- 입력 길이를 적절한 크기로 유지하기 위해 자주 등장하는 단어를 고유 항목으로 유지
+- NLP 에서 많이 사용되는 부분단어 토큰화는 BERT와 DistilBERT의 토큰나이저인 **WordPiece**
+- 트랜스포머스는 사전 훈련된 모델에 연관된 토크나이저를 빠르게 로드하는 **AutoTokenizer** 클래스 제공
+    - 이 클래스의 **from_pretrained()** 메서드를 허브의 모델ID나 로컬 파일 경로와 함께 호출
+- AutoTokenizer 클래스는 체크포인트 이름을 사용해 모델의 설정, 사전 훈련 가중치, 어휘 사전을 자동으로 추출하는 자동 클래스
+
+## 3. 텍스트 분류 모델 훈련
+- DistilBERT 모델은 텍스트 시퀀스에 있는 마스킹된 단어를 예측하도록 사전 훈련, 이 모델을 바로 텍스트 분류에 사용하지는 못함
+
+<img src="images/distilbert_textclassifier.png" width="50%">
+<br>
+
+    - 토큰 인코딩(Token Encoding)
+        - 텍스트를 토큰화 하여 Token-Encoding이라 부르는 one-hot 벡터로 나타냄
+        - Tokenizer 어휘 사전의 크기(2만 ~ 2백만)가 토큰 인코딩의 차원을 결정
+    - 토큰 임베딩(Token Embedding)
+        - 토큰 인코딩을 저차원 공간의 벡터인 토큰 임베딩으로 변환
+        - 토큰 임베딩을 Encoder 블록 층에 통과시켜 각 입력 토큰에 대한 은닉 상태를 생성
+    - 각 은닉 상태는 언어 모델링의 사전 훈련 목표(DistilBERT의 경우 마스킹된 토큰)을 달성하기 위해 마스킹된 입력 토큰을 예측하는 층으로 보냄
+
+- 텍스트 분류 모델 훈련 방법
+    - 특성 추출
+        - 사전 훈련된 모델을 수정하지 않고 은닉 상태를 특성(feature)으로 사용해 분류 모델을 훈련
+    - 미세 튜닝
+        - 사전 훈련된 모델의 파라미터도 업데이트하기 위해 전체 모델을 엔드-투-엔드로 훈련
+
+### 트랜스포머를 특성 추출기로 사용
+- 바디의 가중치를 동결하고 은닉 상태를 분류 모델의 특성으로 사용 
+- 이 방식은 작거나 얕은 모델을 빠르게 훈련한다는 장점이 존재
+
+### 사전 훈련된 모델 사용
+- 트랜스포머스의 자동 클래스인 **AutoModel**을 사용
+- AutoModel 클래스 또한 사전 훈련된 모델의 가중치를 로드하는 **from_pretrained()** 메서드 존재
+- AutoModel은 토큰 인코딩을 임베딩으로 변환한 다음 인코더 스택에 통과시켜 은닉 상태를 반환
+- 은닉 상태 텐서의 크기는 [batch_size, n_tokens, hidden_dim]
+    - ex) [1, 6, 768]: 6개의 입력 토큰마다 768차원의 벡터가 반환
+- 분류 작업에선 보통 [CLS] 토큰에 연관된 은닉 상태를 입력 특성으로 사용
+    - BERT모델에 있는 [CLS]토큰은 분류 작업에서 전체 시퀀스의 정보가 담긴 특수 토큰
+
+### 훈련 세트 시각화
+- UMAP 알고리즘을 사용해 벡터를 2D로 투영
+    - UMAP은 특성이 [0,1] 범위에 놓일 때 잘 동작함
+    - Scikit-learn의 MinMaxScaler를 적용
+
+## 트랜스포머 미세 튜닝
+- 미세튜닝방식에서는 은닉 상태를 고정된 특성으로 사용하지 않고 전체 모델을 훈련
+- 그렇기에 분류 헤드는 미분 가능해야 함(미분이 가능해야 역전파법으로 weight를 구할 수 있기 때문)
+- 트랜스포머스의 **Trainer** API를 사용
+- AutoModel 클래스 대신 AutoModelForSequenceClassification을 사용
+    - 사전 훈련된 모델 출력위에 베이스 모델과 함께 쉽게 훈련할 수 있는 분류헤드가 존재
+- 훈련 파라미터를 정의하기 위해 **TrainingArguments** 클래스를 사용
+    - 많은 정보를 저장하며 훈련과 평가를 상세히 제어 가능
+    - TrainingArguments Parameter
+        - output_dir: Output Directory, 모델의 예측과 체크포인트를 저장해두는 장소
+        - num_train_epochs: epochs 횟수
+        - learning_rate: 학습률
+        per_device_train_batch_size/ per_device_eval_batch_size: 배치크기
+        - weight_decay: 가중치 감쇠 정도
+        - evaluation_strategy: Training중 채택할 평가 전략
+            - "no" : 학습하는 동안 평가 하지 않음
+            - "steps" : eval_steps 마다 평가
+            - "epoch" : epoch 당 평가
+        - logging_steps: 업데이트 단계 수
+        - save_strategy: 체크 포인트 저장 전략
+            - "no": 저장하지 않음
+            - "epoch": epoch 마다 저장
+            - "steps": save_step마다 저장
+        - load_best_model_at_end: 학습이 끝났을때 가장 best model을 load
+        - 참고: https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/trainer#transformers.TrainingArguments
+
+## 오류 분석
+- 모델의 손실 기준으로 검증 샘플을 정렬
+- 정방향 패스의 결과와 레이블을 사용하면 손실을 자동 계산 가능
+
+## CODE
+colab을 사용해 실행
+- colab/MotionClassifier.ipynb
+
+## Reference
+1. 아파치 애로우(Apache Arrow)
+    - 서로 다른 데이터 인프라가 서로 간의 데이터 공유를 위해 API를 이용할때 발생하는 문제점 중 하나는 직렬화와 역직렬화의 오버헤드가 너무 높다는 것
+    - Arrow는 언어, 플랫폼과 상관없이 메모리 상에서 컬럼 구조로 데이터를 정의하여 CPU, GPU에서 메모리를 빠르게 읽고 쓸 수 있도록 해줌
+
+    <img src="images/apache_arrow.png" width="50%"><br>
+
+2. 박스 플롯(Box Plot)
+    - 많은 데이터를 눈으로 확인하기 어려울 때 그림을 이용해 데이터 집합의 범위와 중앙값을 빠르게 확인하는 목적으로 사용
+
+    <img src="images/boxplot.png" width="50%"><br>
+
+3. WordPiece
+    - https://wikidocs.net/166826
+
+4. Embedding Layer
+    - 문자 입력에 대해 학습을 요할 때 필요 Layer
+    - 단어를 의미론적 기하공간에 매핑할 수 있도록 벡터화 시킴
+
+5. UMAP
+    - 차원 축소 기술로 머신러닝 개발자들이 고차원 데이터셋을 이해하고 시각화하기 위해 사용하는 툴
+    - umap 동작 원리: https://m.blog.naver.com/myohyun/222421460444
+    - umap github: https://github.com/lmcinnes/umap
+    - UMAP 생성자 파라미터
+
+    <img src="images/umap_parameters.png" width="50%">
+    <br>
+
+6. 오차행렬(confused matrix)
+    - 학습된 분류 모델이 예측을 수행하며 얼마나 햇갈리고(confused) 있는지도 함께 보여주는 지표
+
+    <img src="images/confusedmatrix.png" width="50%">
+    <br>
+
+    - True: 예측 클래스 값과 실제 클래스 값이 같음을 의미
+    - False: 예측 클래스 값과 실제 클래스 값이 다름을 의미
+    - Accuracy = (TN + TP) / (TN + FP + FN + TP)
+    - 다중 클래스의 경우 자기 클래스는 Positive, 나머지는 모두 Negative로 처리
+
+7. f1 점수
+    - Precision(정밀도)
+        - 모델이 True라고 분류한 것 중에 실제 True인 것의 비율
+        - Precision = (TP) / (TP + FP)
+    - Recall(재현율)
+        - 실제 True인 것중에 모델이 True라고 예측한 것의 비율
+        - Recall = (TP) / (TP + FN)
+    - Ex] 날씨가 맑다/아니다를 예측
+        - Precision: 날씨 예측 모델이 맑다로 예측했는데 실제 날씨가 맑았는지를 살펴보는 지표
+        - Recall: 실제 날씨가 맑은 날중에서 모델이 맑다고 예측한 비율을 나타내는 지표
+    - Precision, Recall은 모두 실제 True인 정답을 모델이 True라고 예측한 경우에 관심이 있으나 보는 관점이 다른 것
+    - Accuracy(정확도)
+        - True를 True로 False를 False라고 예측한 지표(Precision과 Recall은 True만 고려)
+        - 그러나 Accuracy의 경우 데이터가 한쪽으로 편중될시 문제가 발생
+            - 데이터가 한쪽으로 많이 쏠려 있을시 그 Data만 예측해도 정확도가 올라가게 됨
+    - F1 Score
+        - Precision과 Recall의 조화 평균
+        - F1 Score = 2 * 1 / ((1/Precision) + (1/Recall))
+        - F1 Score는 데이터 Label이 불균형 구조일 때 모델의 성능을 정확히 평가, 성능을 하나의 숫자로 표현이 가능 
+
+    - https://sumniya.tistory.com/26
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urllib3<1.27,>=1.21.1\n","  Downloading urllib3-1.26.13-py2.py3-none-any.whl (140 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: xxhash, urllib3, multiprocess, responses, huggingface-hub, datasets\n","  Attempting uninstall: urllib3\n","    Found existing installation: urllib3 1.24.3\n","    Uninstalling urllib3-1.24.3:\n","      Successfully uninstalled urllib3-1.24.3\n","Successfully installed datasets-2.8.0 huggingface-hub-0.11.1 multiprocess-0.70.14 responses-0.18.0 urllib3-1.26.13 xxhash-3.2.0\n"]}]},{"cell_type":"code","execution_count":3,"id":"2f3c6918","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2f3c6918","executionInfo":{"status":"ok","timestamp":1673341831251,"user_tz":-540,"elapsed":5877,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"9827fa97-0000-4b0b-ea1c-245aa2e3b8f6"},"outputs":[{"output_type":"stream","name":"stdout","text":["현재 허브에는 18224개의 데이터셋이 존재\n","처음 10개 데이터셋: ['acronym_identification', 'ade_corpus_v2', 'adversarial_qa', 'aeslc', 'afrikaans_ner_corpus', 'ag_news', 'ai2_arc', 'air_dialogue', 'ajgt_twitter_ar', 'allegro_reviews']\n"]}],"source":["from datasets import list_datasets\n","\n","all_datasets = list_datasets()\n","print(f\"현재 허브에는 {len(all_datasets)}개의 데이터셋이 존재\")\n","print(f\"처음 10개 데이터셋: {all_datasets[:10]}\")"]},{"cell_type":"code","execution_count":4,"id":"feed8efa","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":377,"referenced_widgets":["15be0db60e7447b39d8a3efb0178f99d","6c2a216ef9454384a48332dfcd11e854","321df68850b64edbb4bf68f3efc4666c","2f251ea5146e42c8a10e3e1299d20ee7","e6f31047df2b410d91620c2f04f8714c","48d26eaa385347bb8f1df071dbb3fcf5","37e087967b954cf39bbed8efc00f8910","affcdf3100514e08ae78b45fdff62bb7","d4dbe15a78104e4080f3168d27a28f2f","17d39ba9ceb74e4a971c9d7fe0a388e5","ad9e1452c5a44231a749edefc88ec559","8596765b76d8475687f9a68471baa8c2","245cdb335127490eb54f0b211711187c","ee552a6f31454b0eba764bed5304c447","3ccae33d0f704dd4ab20ed71ad34629d","3e85d7ab6dc9402ea32346d736445024","ca67643c59934657987dbe05ff1f6a79","19ce3569029c443abcd029d7b1e5434f","ec09ef01f31b4c63b70a4f8686084da9","fff4ffa55682415d9f2964ed56978877","67591dd5ed2b4cd4aa72809954922e4e","41a7de832edd405cb11f56b9d7b5ce4b","4d4e5bdc8d534162ba27f70bd5e1f082","16d9f032b1d14503971178f91ccd9957","61e5561b9f374d0baf8d5f73094798fc","1a8d91a9ac674e41a6e0bf62c5b35477","a5addd4c0f114525acde2000dc504386","bbd6396309394aebac8c614a6a93bab6","8da6d9afaae94419b6580759f2760247","c0b1272b05d94b6d803882a8067d6afb","9c4da2a7141f4df88ffc59d3d63c694a","a980d9bda04444ed94d1519d7d628a49","f4a2e61b61d645629758014940686e07","204278e03cfa4697804abf20ff225813","1a82751128d443f4ac5b5303c1b73153","6c8c86abf14d44c5b4efdb368da1a513","bd9de3f5b9574ac3bf3a45d05196e734","e9c04430c58b45518f4e663d4732ebbe","740a2671e40842f79f715ba2d258440b","bc4307eb644e4a6ebfaa308ec79b5911","1f7ab1db7a5c43a386c4097d8469d4a2","e3e4baf588234e2a80af2e8e9858ed5f","31985c31745a4dfebf496417e9b46f3a","d0e5f1132e754fe59287c20f4b94f96b","7c2a5fe9cff34e31ac794deb1e788d8d","acb980cf775e44299b861e54a2eff750","62c0ff2b19c84cdfad5a83ff8720983f","7303d71c059d4066942330e3e70bede0","ac5e2af54b694df5983d1418332e7636","d4a4259ce3c14993af19a12affd34eb8","6f2500756b6f42c391298eeaed5f8854","c98890a867614a26920d4bb067bee304","85754c374f0d43e0bad78444da647c61","fcca2b9f79e0446f9e2f91e0270e9b4b","dd50d5fb200746ccaecb690921ec2c42","7bfe63d5a7c54b4ea7bc6221105d0cbb","e6d52c04d9644613b040bf53957eb46f","97c67c9ea7d9422581abe4a21e091b4b","7fcb248425894c77bef4326f479c1926","36258d1c281a4d2eb26f92513288726d","c0fa09c25d334099899a963c3bc36c00","2da35527796b47eb9ab3cb1cbcaccaf8","3820bb7924a14e27b2b23d83f63d6606","ca8c43945b2a4ec391c00c7b10b48504","1abe53ac1c564bb58c8b7c06ff097d11","b7828189ea0c4d7792da50b67e748efd","bed71be988084af68c0628bcbcfe63f6","17c3146d7a6449f4ad08ea1d3052add1","66ef552e0f0e4591bb39fb6f0fc034d6","5df6a4f46d82455488419dd8e7df3470","07a62cdc075c4e56a4e68189564ac136","c5d0ec37d3cd40558754aa9480eb2a6b","6084c39bce3c4b5ab13d7586c4431a3c","a71d88aedade49038831bc24d2281e3a","562cf26a240949c787ff51dc1f3ef4a2","df95194ca38e43fb83250fa86d096613","bf1dc66bb9164730b4fe171c24604125","c545f017ed8e4a3e8add6a23e7e37314","418167aad1554edea1dc5d1d6b879ccd","812d351083b24a9db1f535b1c6ef5e61","61cfdd6ec9664df98ad6fa2a202318f8","ec5517f772c648f8839125acc7163bd2","bf2332fb0c2c440b9b71f00818936a53","f12f2f547a7e417db26df14ef00c6c80","79be6dfa4f8145d09f072ed6f5cb4d3a","6cfca50926a04bf0b7455de53ed39e07","ede97313536a4a1882e0363a28d771fc","77b2d4741f58419c87725f88409fa34d","774d58af423e4d559b55fb7ca867b73e","2249c0ee4a6c4a12b5c1b004ef0f7bea","8c6a709ec66f4bf89d7dc1d99ebfcd85","b203c5ac560b4c0b91da8d3936c9701e","a56c46fb64984a99acf33eaf1d0749e2","654e5a1bf15944139a8af596fea472ad","57f07b09eaff4e2cbb2baf0f55aa6f56","2f78b83bf6ab4e309657919870f72914","7619571658cd4fa2841a04d78dc1fbe0","cc4c766ab3cd4d31a6a1c2bd62fd1ac1","d546cdab698241b593f2a4668cbbe718","1dcfb16f3b99497fab648fbc508622af","0edf7b00d74e4aa08a2a3dde6f9377d9","68663f8ab1464294ad24fe1550746272","62dec3a2e87744a8accf7e17bdfc4fe1","7e386e08bdd34471b5be7c5b578e3fe8","e0da041ea0a34884a3ac413a0632c05a","dc452bd507d04875922b12d79a12be8c","cdd8485752434ebabbc37d1e4f268e5d","131e10a0bb814925b81350ee521d10ec","9d0a2f1b16554acc8ee8fa798b3d80d5","4e22f63e5af146279cdafa6e14fa0235","3ac8646d66e9466cb19ca475d5c35319","4e20703456eb4983bfd220202193f770","ff1ff119aa864603bc9e179ad039db1c","79c0f1e2dedf459fad9d99c9da23921f","f4536072a0f245ce955bf17c4e69fff7","62e068731b004692b62bfbe3d6a7d4bd","21401b5db07742ccbd904349816513b8","12d18e67c09045aa95cb9e59a9b6e2a8","fca1857b9a8f48d28b74fc06d6986bd5","826dafb23b96401b854502fd7faff689","0f6f60d107854832ab63a53fbfbd079c","d7190fe0ffbe44ed88183863b01ae154","37f0d2cf136240c9a05d09530134731b","4b00f1b6b9a44812ace3d76be6eddc93","43a0ec11689f4195871427b3518a564d","a67d6cc425c94c4a8a114da132392197","46df938913904dff8d9d6ac483529a5c","140e7085dabc494d89eeeeb30d5f8a19","a2f713bb502644fd93663239d7af8c4d","aa0bb486843e4663952242eed3ba1261","1bb5c0a141984ad39f5fb3ce2be79552","f38775a718884ecdbaf317b2369550da"]},"id":"feed8efa","executionInfo":{"status":"ok","timestamp":1673341846622,"user_tz":-540,"elapsed":15373,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"6f88ae9d-a345-410c-e429-2153da87dd9b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/3.97k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"15be0db60e7447b39d8a3efb0178f99d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/3.28k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8596765b76d8475687f9a68471baa8c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/8.78k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d4e5bdc8d534162ba27f70bd5e1f082"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n"]},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset emotion/split to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"204278e03cfa4697804abf20ff225813"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/592k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c2a5fe9cff34e31ac794deb1e788d8d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/74.0k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bfe63d5a7c54b4ea7bc6221105d0cbb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/74.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bed71be988084af68c0628bcbcfe63f6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c545f017ed8e4a3e8add6a23e7e37314"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"774d58af423e4d559b55fb7ca867b73e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1dcfb16f3b99497fab648fbc508622af"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3ac8646d66e9466cb19ca475d5c35319"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset emotion downloaded and prepared to /root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d7190fe0ffbe44ed88183863b01ae154"}},"metadata":{}}],"source":["from datasets import load_dataset\n","\n","emotions = load_dataset(\"emotion\")"]},{"cell_type":"code","execution_count":5,"id":"cbd5df51","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cbd5df51","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":29,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8314008d-a773-4e5d-ea7a-c89d768f2479"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 16000\n","    })\n","    validation: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 2000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 2000\n","    })\n","})"]},"metadata":{},"execution_count":5}],"source":["emotions"]},{"cell_type":"code","execution_count":6,"id":"63910423","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"63910423","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":26,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d3e3ac03-0dfb-45f5-d797-08e69d161f55"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['text', 'label'],\n","    num_rows: 16000\n","})"]},"metadata":{},"execution_count":6}],"source":["train_ds = emotions[\"train\"]\n","train_ds"]},{"cell_type":"code","execution_count":7,"id":"29ff03af","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"29ff03af","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":24,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"dacc3214-fbb5-436f-ce8d-99f205ffe6b7"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': 'i didnt feel humiliated', 'label': 0}"]},"metadata":{},"execution_count":7}],"source":["train_ds[0]"]},{"cell_type":"code","execution_count":8,"id":"100d35c4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"100d35c4","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":23,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"a2f438ad-febf-4f84-e52e-18f3ac105b25"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['text', 'label']"]},"metadata":{},"execution_count":8}],"source":["train_ds.column_names"]},{"cell_type":"code","execution_count":9,"id":"dd5f381c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dd5f381c","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":22,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"2936877e-856e-4c1f-81e2-9bc3a2a87be9"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}\n"]}],"source":["# Dataset의 속성\n","print(train_ds.features)"]},{"cell_type":"code","execution_count":10,"id":"37d99831","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"37d99831","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":21,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"c30b4b56-b13f-4f43-8eb2-1f8ca3a5ff20"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'text': ['i didnt feel humiliated',\n","  'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n","  'im grabbing a minute to post i feel greedy wrong',\n","  'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n","  'i am feeling grouchy'],\n"," 'label': [0, 0, 3, 2, 3]}"]},"metadata":{},"execution_count":10}],"source":["train_ds[:5]"]},{"cell_type":"code","execution_count":11,"id":"8c00c688","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8c00c688","executionInfo":{"status":"ok","timestamp":1673341846623,"user_tz":-540,"elapsed":20,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"a0638f20-0487-44c5-db17-deac6f76a52f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['i didnt feel humiliated',\n"," 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n"," 'im grabbing a minute to post i feel greedy wrong',\n"," 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n"," 'i am feeling grouchy']"]},"metadata":{},"execution_count":11}],"source":["train_ds['text'][:5]"]},{"cell_type":"code","execution_count":12,"id":"f6dd1a51","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"f6dd1a51","executionInfo":{"status":"ok","timestamp":1673341846624,"user_tz":-540,"elapsed":19,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"5c0a6a9b-1cde-4769-d985-8a56ec2cde83"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                text  label\n","0                            i didnt feel humiliated      0\n","1  i can go from feeling so hopeless to so damned...      0\n","2   im grabbing a minute to post i feel greedy wrong      3\n","3  i am ever feeling nostalgic about the fireplac...      2\n","4                               i am feeling grouchy      3"],"text/html":["\n","  <div id=\"df-e873e5da-bd1c-44bb-ab2d-14eb8ae8ae37\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    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emotions[\"train\"][:]\n","df.head()"]},{"cell_type":"code","execution_count":13,"id":"44b1fd35","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"44b1fd35","executionInfo":{"status":"ok","timestamp":1673341847308,"user_tz":-540,"elapsed":689,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"517a6d8a-9827-43d8-e204-7256ac6bee8a"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                text  label label_name\n","0                            i didnt feel humiliated      0    sadness\n","1  i can go from feeling so hopeless to so damned...      0    sadness\n","2   im grabbing a minute to post i feel greedy wrong      3      anger\n","3  i am ever feeling nostalgic about the fireplac...      2       love\n","4                               i am feeling grouchy      3      anger"],"text/html":["\n","  <div id=\"df-53dfe82f-e9c3-418d-8229-610c4b86858d\">\n","    <div 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":13}],"source":["def label_int2str(row):\n","    return emotions[\"train\"].features[\"label\"].int2str(row)\n","\n","df[\"label_name\"] = df[\"label\"].apply(label_int2str)\n","df.head()"]},{"cell_type":"code","execution_count":14,"id":"b96c7176","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":281},"id":"b96c7176","executionInfo":{"status":"ok","timestamp":1673341847309,"user_tz":-540,"elapsed":18,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"087030f8-fada-40fc-e3d9-8996591ebc5b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["import matplotlib.pyplot as plt\n","\n","# plot.barh() : Make a horizontal bar plot\n","df[\"label_name\"].value_counts(ascending=True).plot.barh()\n","plt.title(\"Frequency of Classes\")\n","plt.show()"]},{"cell_type":"code","execution_count":15,"id":"50520dc3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":335},"id":"50520dc3","executionInfo":{"status":"ok","timestamp":1673341847309,"user_tz":-540,"elapsed":16,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"14c364fc-f39d-4c5e-9176-a52d437fcb66"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n","  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}],"source":["df[\"Words Per Tweet\"] = df[\"text\"].str.split().apply(len)\n","df.boxplot(\"Words Per Tweet\", by=\"label_name\", grid=False, showfliers=False, color=\"black\")\n","plt.suptitle(\"\")\n","plt.xlabel(\"\")\n","plt.show()"]},{"cell_type":"code","execution_count":16,"id":"bae0a4c8","metadata":{"id":"bae0a4c8","executionInfo":{"status":"ok","timestamp":1673341847309,"user_tz":-540,"elapsed":11,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"outputs":[],"source":["# 데이터셋의 출력 포맷 초기화\n","emotions.reset_format()"]},{"cell_type":"markdown","id":"530d7e4a","metadata":{"id":"530d7e4a"},"source":["# 토큰화"]},{"cell_type":"markdown","id":"b397f12b","metadata":{"id":"b397f12b"},"source":["## 문자 토큰화\n","각 문자를 개별로 모델에 주입 <br>\n","파이썬의 str 객체 내부는 사실 배열이므로 문자 수준의 토큰화가 손쉽게 가능"]},{"cell_type":"code","execution_count":17,"id":"9b5c18a4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9b5c18a4","executionInfo":{"status":"ok","timestamp":1673341847310,"user_tz":-540,"elapsed":12,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"81675c55-2892-4542-fd6d-d6fbf120999b"},"outputs":[{"output_type":"stream","name":"stdout","text":["['T', 'o', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n"]}],"source":["text = \"Tokenizing text is a core task of NLP.\"\n","tokenized_text = list(text)\n","print(tokenized_text)"]},{"cell_type":"code","execution_count":18,"id":"acf0acdd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"acf0acdd","executionInfo":{"status":"ok","timestamp":1673341847310,"user_tz":-540,"elapsed":10,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"88f785d3-4cc2-4635-c044-e7368fb02582"},"outputs":[{"output_type":"stream","name":"stdout","text":["{' ': 0, '.': 1, 'L': 2, 'N': 3, 'P': 4, 'T': 5, 'a': 6, 'c': 7, 'e': 8, 'f': 9, 'g': 10, 'i': 11, 'k': 12, 'n': 13, 'o': 14, 'r': 15, 's': 16, 't': 17, 'x': 18, 'z': 19}\n"]}],"source":["token2idx = {ch: idx for idx, ch in enumerate(sorted(set(tokenized_text)))}\n","print(token2idx)"]},{"cell_type":"code","execution_count":19,"id":"ff118c22","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ff118c22","executionInfo":{"status":"ok","timestamp":1673341847310,"user_tz":-540,"elapsed":9,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d86f3c49-412b-43fa-e063-431158532361"},"outputs":[{"output_type":"stream","name":"stdout","text":["[5, 14, 12, 8, 13, 11, 19, 11, 13, 10, 0, 17, 8, 18, 17, 0, 11, 16, 0, 6, 0, 7, 14, 15, 8, 0, 17, 6, 16, 12, 0, 14, 9, 0, 3, 2, 4, 1]\n"]}],"source":["input_ids = [token2idx[token] for token in tokenized_text]\n","print(input_ids)"]},{"cell_type":"code","execution_count":20,"id":"8508c09d","metadata":{"id":"8508c09d","executionInfo":{"status":"ok","timestamp":1673341851106,"user_tz":-540,"elapsed":3803,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"outputs":[],"source":["import torch\n","import torch.nn.functional as F"]},{"cell_type":"code","execution_count":21,"id":"4ce2e4e1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4ce2e4e1","executionInfo":{"status":"ok","timestamp":1673341851106,"user_tz":-540,"elapsed":31,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f93c10f1-a432-4141-c108-e40acb976646"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([38, 20])"]},"metadata":{},"execution_count":21}],"source":["input_ids = torch.tensor(input_ids)\n","one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n","one_hot_encodings.shape"]},{"cell_type":"markdown","id":"8b8449c2","metadata":{"id":"8b8449c2"},"source":["38개의 입력 토큰 각각에 20차원의 원-핫 벡터가 만들어짐\n","\n","5 .....len(20) <br>\n","14.....len(20) <br>\n","12.....len(20) <br>\n",".\n",".\n",". .....len(20) <br>\n","2 .....len(20) <br>\n","4 .....len(20) <br>\n","1 .....len(20) <br>\n","\n","각 토큰별 20개의 원소를 가진 벡터가 만들어짐"]},{"cell_type":"code","execution_count":22,"id":"58874e4b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"58874e4b","executionInfo":{"status":"ok","timestamp":1673341851107,"user_tz":-540,"elapsed":28,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"e5ff92cb-cc63-455c-f447-8ed4e1b8846c"},"outputs":[{"output_type":"stream","name":"stdout","text":["토큰: T\n","텐서 인덱스: 5\n","원-핫 인코딩: tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n"]}],"source":["print(f\"토큰: {tokenized_text[0]}\")\n","print(f\"텐서 인덱스: {input_ids[0]}\")\n","print(f\"원-핫 인코딩: {one_hot_encodings[0]}\")"]},{"cell_type":"markdown","id":"5729671a","metadata":{"id":"5729671a"},"source":["## 단어 토큰화\n","텍스트를 문자가 아닌 단어로 분할하고 각 단어를 정수로 매핑"]},{"cell_type":"code","execution_count":23,"id":"6fbe209f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6fbe209f","executionInfo":{"status":"ok","timestamp":1673341851107,"user_tz":-540,"elapsed":23,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d3cd0fbe-5a53-411d-f084-ac04b70efff5"},"outputs":[{"output_type":"stream","name":"stdout","text":["['Tokenizing', 'text', 'is', 'a', 'core', 'task', 'of', 'NLP.']\n"]}],"source":["tokenized_text = text.split()\n","print(tokenized_text)"]},{"cell_type":"markdown","id":"f8988f71","metadata":{"id":"f8988f71"},"source":["단어 토큰화는 단어에 곡용, 활용형, 철자 오류가 포함되어 어휘 사전이 금세 수백만 개까지 늘어나게 됨"]},{"cell_type":"markdown","id":"bfb042f0","metadata":{"id":"bfb042f0"},"source":["단어를 토큰화 하게 되면 어휘사전이 커지게 되고 이러면 신경망의 파라미터 역시 많이 필요하게 됨\n","<br>\n","- 어휘 사전의 크기를 제한하는 일반적인 방법은 드물게 등장하는 단어는 무시하는 것. 이러한 단어는 'UNK' 토큰으로 매핑\n","- 하지만 이렇게 되면 토큰화 과정에서 중요 정보 일부를 잃게 됨\n","- 모든 입력 정보와 일부 입력 구조를 유지하는 문자 토큰화와 단어 토큰화를 절충하는 방법으로 **부분단어 토큰화(subword tokenization)** 라는 방법"]},{"cell_type":"markdown","id":"95a96577","metadata":{"id":"95a96577"},"source":["## 부분단어 토큰화\n","- 부분단어 토큰화는 문자 단어 토큰화 + 단어 토큰화 의 장점을 결합\n","- 드물게 등장하는 단어를 더 작은 단위로 나누면 모델이 복잡한 단어나 철자 오류를 처리하기 용이\n","- 다른 방법으론 입력 길이를 적절한 크기로 유지하기 위해 자주 등장하는 단어를 고유한 항목으로 유지\n","<br><br>\n","- NLP 분야에서 널리 사용되는 부분단어 토큰화 중 먼저 BERT와 DistilBERT의 토크나이저 **WordPiece**"]},{"cell_type":"code","source":["!pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NmnMUChO63ZZ","executionInfo":{"status":"ok","timestamp":1673341860819,"user_tz":-540,"elapsed":9733,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"c0b8dfaa-3526-445d-db30-cdf01b04fed8"},"id":"NmnMUChO63ZZ","execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m79.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n","Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n","  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m69.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.26.13)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (4.0.0)\n","Installing collected packages: tokenizers, transformers\n","Successfully installed tokenizers-0.13.2 transformers-4.25.1\n"]}]},{"cell_type":"code","execution_count":25,"id":"2379be10","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["2cbf56d11bd84bd39a2e8f00be50ab98","fa29a3affda84dc38e53a7ec02e4a1f2","1886290ec37f4f7eb7dced6d494cbe6a","b37bdca77d064cf2b7ef2bf21edc554b","14709700a96448cda2f036f9886463f9","e9230e8b794f433e9ed48f62a244c1f3","16ab6bdfe98a44dcb15fb374ffdd7dcb","f9ac65f7c12b4c70b1481fd0052ebcab","a49d46601b5d4395bd61aa89b3c4f261","fa363e3b514f47909eb1caf3bda7d4ac","4c2ddc339f2948cda9331332583f71ef","815b635b8e2d429ab30cc56170508f2b","2783f2b3b469440e829ecf06ce441e24","740089b7671147a8b2aa5334dbe299a1","a55dbf76cedf4c51a80127d1368f00fa","fcc0299f56d54e069e252951755dd8d9","5a16a657767448af9953262cbe566fda","279728f2f8e842af9b078cdd58d71af0","a3452d2825e040f9aad5667274d1d9d9","c8b548fcf7e14265ade8eb891b4717bf","3ef5ff5cb4c54628bb3bc720a65f93a2","eb8260186835427ab020dbdf4d484794","5f67cfa6f99c4045a77ef8f221ed8a72","b07045b7f68944aeb2c8bd0e41994b93","137fcc68aefc48179ababf622aad0776","96b98cd1ec9946d58e510739cf43324e","c6eb6b1626314623b55ad235895da2f9","72cad00b563945b6b5c9c43f8eb34172","2971a235130f4f72b0b8486364d63ad5","c70eb58ccb2347d38c8cde483c47191f","b7b7225c7d7943e280b421c760b72caa","147c8d4a28d146a792e7c8a037bcb011","823d188f499e47f6a8d87503c4ad77f7","029fdd7a3b0842229d7b0a93c5867fa1","438bcf79e8694464ad3a846a0643be03","a8baed453dd9425fb3d788033508119d","375bd083c65b4bf882f21c29c858890c","149d349aa3cb46c588d164bd37b84117","a12bce3f9ff841f683b19a6b581c587f","4d864c90cd41415e9e610edf9fba526b","323fcf7620f04d9da487b8628e229e5b","c8259c53289d4100a9fab111d220bea3","7ac6ebbf3dc040ae9a9b3614557da598","6d19cf0811e64cd7b29cb63458060fad"]},"id":"2379be10","executionInfo":{"status":"ok","timestamp":1673341874956,"user_tz":-540,"elapsed":14138,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8f74a0e1-dd3f-43e8-f284-26c904b9be1d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2cbf56d11bd84bd39a2e8f00be50ab98"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"815b635b8e2d429ab30cc56170508f2b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5f67cfa6f99c4045a77ef8f221ed8a72"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"029fdd7a3b0842229d7b0a93c5867fa1"}},"metadata":{}}],"source":["from transformers import AutoTokenizer\n","\n","model_ckpt = \"distilbert-base-uncased\"\n","tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"]},{"cell_type":"code","execution_count":26,"id":"6b1e2b90","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6b1e2b90","executionInfo":{"status":"ok","timestamp":1673341874957,"user_tz":-540,"elapsed":22,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"16c0fabd-4d45-42d4-ed44-4c87993154ae"},"outputs":[{"output_type":"stream","name":"stdout","text":["Tokenizing text is a core task of NLP.\n","{'input_ids': [101, 19204, 6026, 3793, 2003, 1037, 4563, 4708, 1997, 17953, 2361, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"]}],"source":["encoded_text = tokenizer(text)\n","print(text)\n","print(encoded_text)"]},{"cell_type":"code","execution_count":27,"id":"0b85a1b6","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0b85a1b6","executionInfo":{"status":"ok","timestamp":1673341874957,"user_tz":-540,"elapsed":17,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"500481b2-c7fd-43ab-e330-d1fb9cdfde0f"},"outputs":[{"output_type":"stream","name":"stdout","text":["['[CLS]', 'token', '##izing', 'text', 'is', 'a', 'core', 'task', 'of', 'nl', '##p', '.', '[SEP]']\n"]}],"source":["tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n","print(tokens)"]},{"cell_type":"markdown","id":"94b6f7c5","metadata":{"id":"94b6f7c5"},"source":["1. 특수 토큰 [CLS], [SEP]가 시퀀스 처음과 끝에 추가\n","2. 토큰이 모두 소문자로 변환\n","3. 'tokenizing'과 'NLP'가 각각 2개의 토큰으로 나뉘어짐. 자주 등장하는 단어가 아니기 때문 ##izing, ##p에 있는 #은 공백이 아님을 의미\n","\n","이런 접두사가 붙은 토큰은 문자열로 다시 바꿀 때 앞의 토큰과 합침"]},{"cell_type":"code","execution_count":28,"id":"259920c8","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"259920c8","executionInfo":{"status":"ok","timestamp":1673341874957,"user_tz":-540,"elapsed":13,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"6e40373a-15c9-4b35-c5da-7ddfa7551462"},"outputs":[{"output_type":"stream","name":"stdout","text":["[CLS] tokenizing text is a core task of nlp. [SEP]\n"]}],"source":["print(tokenizer.convert_tokens_to_string(tokens))"]},{"cell_type":"code","execution_count":29,"id":"46c19084","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"46c19084","executionInfo":{"status":"ok","timestamp":1673341874958,"user_tz":-540,"elapsed":13,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"6713e3b3-974f-4acc-fb61-4af89cf3cf87"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["30522"]},"metadata":{},"execution_count":29}],"source":["# 어휘 사전 크기 확인\n","tokenizer.vocab_size"]},{"cell_type":"code","execution_count":30,"id":"15e57040","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"15e57040","executionInfo":{"status":"ok","timestamp":1673341874958,"user_tz":-540,"elapsed":11,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8f35ebb4-8bf7-4ecc-fe38-a71d549097db"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["512"]},"metadata":{},"execution_count":30}],"source":["# 모델 최대 문맥 크기\n","tokenizer.model_max_length"]},{"cell_type":"code","execution_count":31,"id":"dbbba4bb","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dbbba4bb","executionInfo":{"status":"ok","timestamp":1673341874958,"user_tz":-540,"elapsed":10,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"b07f3a9f-2f66-4096-c557-a4212ac10115"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["['input_ids', 'attention_mask']"]},"metadata":{},"execution_count":31}],"source":["# 모델이 정방향 패스(forward pass)\n","tokenizer.model_input_names"]},{"cell_type":"markdown","id":"f8fa19c9","metadata":{"id":"f8fa19c9"},"source":["# 전체 데이터셋 토큰화\n"]},{"cell_type":"code","execution_count":32,"id":"5e5a58d3","metadata":{"id":"5e5a58d3","executionInfo":{"status":"ok","timestamp":1673341874958,"user_tz":-540,"elapsed":9,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"outputs":[],"source":["def tokenize(batch):\n","    return tokenizer(batch[\"text\"], padding=True, truncation=True)"]},{"cell_type":"markdown","id":"db6da0cc","metadata":{"id":"db6da0cc"},"source":["Tokenizer를 샘플 배치에 적용\n","<br>\n","- padding=True: 배치에 있는 가장 긴 샘플 크기에 맞춰 샘플을 0으로 패딩\n","- truncation=True: 모델의 최대 문맥 크기에 맞춰 샘플을 잘라냄"]},{"cell_type":"code","execution_count":33,"id":"9658e034","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9658e034","executionInfo":{"status":"ok","timestamp":1673341874958,"user_tz":-540,"elapsed":8,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"edf35851-8dfa-49ae-e48a-4c7e85ecabef"},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': [[101, 1045, 2134, 2102, 2514, 26608, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1045, 2064, 2175, 2013, 3110, 2061, 20625, 2000, 2061, 9636, 17772, 2074, 2013, 2108, 2105, 2619, 2040, 14977, 1998, 2003, 8300, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"]}],"source":["print(tokenize(emotions[\"train\"][:2]))"]},{"cell_type":"code","execution_count":34,"id":"03458041","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["69c863e980624e10bb6afad72fed6cd1","3c160b0fb3d7466aa0f79c6233d16001","8b26dd18f285460296a17d6b856f30aa","d795813bd1e447ec8ca874a4eb58c806","8b9b4713c05e402491fa537414d0bd0b","d49fc731f3634038b245ed8b19fec175","ac1f10abc80449b3b570f462332de626","834b4dee42f84269801adc0f5517a89f","6c363fc2f9bd454f86bd477dada30a71","17d0256a8547455fbe4e4f6935cea7a4","f622e7b698bf47bcb1233230e6771772","048922deeb3141b1805c22f41a880ecc","6148410294ec48c3804ed15e7034cb4f","70773097ffad4c9b85b6a739a514e1b3","3da5ccb880f848dcaef28bb694c4bb70","c9dcf555150b4ecd80addf1c1bb33824","5ab0a87049344e46929a316bc2a2e3e9","deb4b8225c6d4fdba6f2efbb33b17000","a7953cfec05a4585b366d3d8e4b2123b","b912f40f4cc247f3a782da9507ef3e09","1a2e990827f248e7bc47da5ef7f17826","31014192818d46ff8b3671e3c5d180de","0177ae63703f4b01889c07ad4cf86e29","02a9d73f776e431198ace480d231d10b","3036423ebe6a49ad91f0de6e728d0ad8","85c41307b3c84b5a86a24f8a2463a939","9a097fa5b27749c7aa379a4ca349bf5b","e21e33b65f604a56b7ee6c3839362574","41bd67ce77564fa5be2527f800620262","48ba903b498a4dbe82b77dd7df3b28c5","4d0d3d035fc6444fa43b487ce81bb981","5ad971ef99af4d379967cf291d13ab74","a8c5dfa23d0f46d5a2b03350abb62c07"]},"id":"03458041","executionInfo":{"status":"ok","timestamp":1673341875660,"user_tz":-540,"elapsed":709,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f24f9a52-ed17-4b4d-e482-e2a83330c933"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"69c863e980624e10bb6afad72fed6cd1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"048922deeb3141b1805c22f41a880ecc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0177ae63703f4b01889c07ad4cf86e29"}},"metadata":{}}],"source":["emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"]},{"cell_type":"markdown","id":"ceffe2fe","metadata":{"id":"ceffe2fe"},"source":["map() 메서드는 말뭉치에 있는 모든 샘플에 개별적으로 작용 <br>\n","\n","- batched=True: 트윗을 배치로 인코딩\n","- batch_size=None: 전체 데이터셋이 하나의 배치로 tokenize() 함수에 적용, 입력 텐서와 어텐션 마스크는 전역적으로 동일한 크기로 생성"]},{"cell_type":"code","execution_count":35,"id":"3de1ab5c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3de1ab5c","executionInfo":{"status":"ok","timestamp":1673341875660,"user_tz":-540,"elapsed":4,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"b30e5c75-b3d2-4e6a-e93c-35a19694756f"},"outputs":[{"output_type":"stream","name":"stdout","text":["['text', 'label', 'input_ids', 'attention_mask']\n"]}],"source":["print(emotions_encoded[\"train\"].column_names)"]},{"cell_type":"markdown","id":"c3d9224c","metadata":{"id":"c3d9224c"},"source":["# 텍스트 분류 모델 훈련\n","\n","### 특성 추출\n","사전 훈련된 모델을 수정하지 않고 은닉 상태를 특성(feature)으로 사용해 분류 모델을 훈련\n","\n","### 미세 튜닝\n","사전 훈련된 모델의 파라미터도 업데이트하기 위해 전체 모델을 엔드-투-엔드로 훈련"]},{"cell_type":"markdown","id":"2b923107","metadata":{"id":"2b923107"},"source":["## 1. 텍스트 분류 모델 훈련 - 특성 추출 방법\n"]},{"cell_type":"markdown","id":"c6560f7f","metadata":{"id":"c6560f7f"},"source":["사전 훈련된 모델 사용하기"]},{"cell_type":"code","execution_count":36,"id":"e6f48da5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":121,"referenced_widgets":["6d20ccf8cbfc434d83d29cf94d66fa2f","563f6e63d6334ffdb92b824ca4543e26","3a3663e4ccf447178e720efe74226eb2","bd5032bb6bd04505a16d6aaeb344a411","843844a0068c4252931450f24da6b86e","bcd73e1f781c4d1d950ae79b4f6787bf","3490bc468f2e4bbeb57e76aaf9706308","2b8f7b15d44346e8a86b7414be26c8f3","e3bad1e7fa8142ce9d618d0c8b1a2225","402cabec74604cc09d0fff59dd529621","4882a7357dd3468491f3bf78eec1909c"]},"id":"e6f48da5","executionInfo":{"status":"ok","timestamp":1673341889298,"user_tz":-540,"elapsed":13640,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"495b6d23-a7ac-4d6a-931f-95091aa040ab"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6d20ccf8cbfc434d83d29cf94d66fa2f"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight']\n","- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]}],"source":["from transformers import AutoModel\n","\n","model_ckpt = \"distilbert-base-uncased\"\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = AutoModel.from_pretrained(model_ckpt).to(device)"]},{"cell_type":"code","execution_count":37,"id":"5e3da298","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5e3da298","executionInfo":{"status":"ok","timestamp":1673341889299,"user_tz":-540,"elapsed":28,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"dda0ebab-4117-4dd9-d2b5-bd91d6250cae"},"outputs":[{"output_type":"stream","name":"stdout","text":["입력 텐서 크기: torch.Size([1, 6])\n"]}],"source":["text = \"this is a test\"\n","inputs = tokenizer(text, return_tensors=\"pt\")\n","print(f\"입력 텐서 크기: {inputs['input_ids'].size()}\")"]},{"cell_type":"code","execution_count":38,"id":"4f99ad6f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4f99ad6f","executionInfo":{"status":"ok","timestamp":1673341889299,"user_tz":-540,"elapsed":25,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"85baedc1-5401-401e-dd42-bc2faa6ec094"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["dict_items([('input_ids', tensor([[ 101, 2023, 2003, 1037, 3231,  102]])), ('attention_mask', tensor([[1, 1, 1, 1, 1, 1]]))])"]},"metadata":{},"execution_count":38}],"source":["inputs.items()"]},{"cell_type":"code","execution_count":39,"id":"a81ee80b","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a81ee80b","executionInfo":{"status":"ok","timestamp":1673341889299,"user_tz":-540,"elapsed":21,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8bee0bfb-90c4-47e0-b66a-dc074e034beb"},"outputs":[{"output_type":"stream","name":"stdout","text":["k: input_ids\n","v: tensor([[ 101, 2023, 2003, 1037, 3231,  102]])\n","k: attention_mask\n","v: tensor([[1, 1, 1, 1, 1, 1]])\n"]}],"source":["for k, v in inputs.items():\n","    print(f\"k: {k}\")\n","    print(f\"v: {v}\")"]},{"cell_type":"code","execution_count":40,"id":"c3d5a3ca","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c3d5a3ca","executionInfo":{"status":"ok","timestamp":1673341892896,"user_tz":-540,"elapsed":3616,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"30aa5c1d-3005-46cf-aff1-49fd7afb635b"},"outputs":[{"output_type":"stream","name":"stdout","text":["BaseModelOutput(last_hidden_state=tensor([[[-0.1565, -0.1862,  0.0528,  ..., -0.1188,  0.0662,  0.5470],\n","         [-0.3575, -0.6484, -0.0618,  ..., -0.3040,  0.3508,  0.5221],\n","         [-0.2772, -0.4459,  0.1818,  ..., -0.0948, -0.0076,  0.9958],\n","         [-0.2841, -0.3917,  0.3753,  ..., -0.2151, -0.1173,  1.0526],\n","         [ 0.2661, -0.5094, -0.3180,  ..., -0.4203,  0.0144, -0.2149],\n","         [ 0.9441,  0.0112, -0.4714,  ...,  0.1439, -0.7288, -0.1619]]],\n","       device='cuda:0'), hidden_states=None, attentions=None)\n"]}],"source":["inputs = {k:v.to(device) for k,v in inputs.items()}\n","with torch.no_grad():\n","    outputs = model(**inputs)\n","    \n","print(outputs)"]},{"cell_type":"code","execution_count":41,"id":"a7b6ba5d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a7b6ba5d","executionInfo":{"status":"ok","timestamp":1673341892897,"user_tz":-540,"elapsed":13,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"a81448d1-223a-4f49-80a7-25d48a80832f"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 6, 768])"]},"metadata":{},"execution_count":41}],"source":["outputs.last_hidden_state.size()"]},{"cell_type":"markdown","id":"7f551af1","metadata":{"id":"7f551af1"},"source":["은닉 상태 텐서의 크기는 [batch_size, n_tokens, hidden_dim] <br>\n","-> 6개의 입력 토큰마다 768차원의 벡터가 반환"]},{"cell_type":"code","execution_count":42,"id":"9a5b950a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9a5b950a","executionInfo":{"status":"ok","timestamp":1673341892897,"user_tz":-540,"elapsed":9,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f0fc13d5-071e-429f-ee2b-2513a406595e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["torch.Size([1, 768])"]},"metadata":{},"execution_count":42}],"source":["outputs.last_hidden_state[:,0].size()"]},{"cell_type":"code","execution_count":43,"id":"c6eb5006","metadata":{"id":"c6eb5006","executionInfo":{"status":"ok","timestamp":1673341892897,"user_tz":-540,"elapsed":4,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"outputs":[],"source":["def extract_hidden_states(batch):\n","    inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n","    \n","    # 마지막 은닉 상태를 추출\n","    with torch.no_grad():\n","        last_hidden_state = model(**inputs).last_hidden_state\n","        \n","    # [CLS] 토큰에 대한 벡터를 반환\n","    return {\"hidden_state\": last_hidden_state[:,0].cpu().numpy()}"]},{"cell_type":"code","execution_count":44,"id":"1faae4d9","metadata":{"id":"1faae4d9","executionInfo":{"status":"ok","timestamp":1673341892897,"user_tz":-540,"elapsed":4,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"outputs":[],"source":["emotions_encoded.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])"]},{"cell_type":"code","execution_count":45,"id":"49cf0fca","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["101a4116f12b44f6b426fc2814944eb8","9edbf9a8ba5d4142a0a12e5ea696da5c","8af71848005147cfaedd0c9ec9d4fd49","44dbd17197c64b538c63a610f7f31617","c7fbfa047d884faeb5969ae896515fc1","84928d15dbe2497baa439cb5721b83a1","ba72138f0e604758b4490c754875af78","b0e164b8f84946f88721e0c0069120a1","6e54fca8c14542cabb8b8a4b33e2dbf8","0bf2839bc8d64d2e94ad85881c74bf92","6ef1bc813fcd44e88a28492317397f9a","ec33154d5df8415098870a8cfa336916","b1611173b99e4e7e887289ec11b22549","b2b36893a8404cbeb820029a11f026c1","1026b11d38b64ec88111620d4e6496b5","a40d5fc95a36471d90292263ecbf3a9a","1f92f00548c1492bb9d5fe9a827c1465","b8fc14d3ad2e493cab95946f99eaa22d","8674806cef2647afa397d1a1a67aeb9c","1afb862c628b422788c23230a7c33263","456d3d915eab4f77b0bf0f2e0326d818","92f5f4755e724e12a59fe33e372e84e4","2fd74fded781498f86d9b097b7bebd2d","b3f0a364d8db417e95971d9bc93cd894","ba119c289d864b8ab108e9cf52a49820","01aa5eadc6a946528e32244d30383ed7","c51fa6e6e9864a7bb0be5bfb9f71c637","1fd5b81af51d4b30b17f630586339560","f0cc4fc15d62434499f1e0fefe2f03b3","3d10c62f029449bcbc574169ac502748","ecb19ac1b1664734a661aa34c037e9b8","73c4003f63bc4da1bffd26ba93218857","e869dea153bf4883ba3e883359e4ba99"]},"id":"49cf0fca","executionInfo":{"status":"ok","timestamp":1673341936842,"user_tz":-540,"elapsed":43949,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"ad796a8e-2d7c-4be6-8df2-03eae34e0955"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/16 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"101a4116f12b44f6b426fc2814944eb8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ec33154d5df8415098870a8cfa336916"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fd74fded781498f86d9b097b7bebd2d"}},"metadata":{}}],"source":["# 모든 분할에 대해 은닉 상태를 한 번에 추출\n","emotions_hidden = emotions_encoded.map(extract_hidden_states, batched=True)"]},{"cell_type":"code","source":["emotions_hidden[\"train\"].column_names"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A9HpReiy88_e","executionInfo":{"status":"ok","timestamp":1673341980649,"user_tz":-540,"elapsed":6,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"42edc709-8ce1-4ded-a946-304a06750a34"},"id":"A9HpReiy88_e","execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['text', 'label', 'input_ids', 'attention_mask', 'hidden_state']"]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","source":["## 특성 행렬 만들기\n","전처리된 데이터셋에 분류 모델을 훈련하는 데 필요한 모든 정보가 담김 <br>\n","은닉 상태를 입력 특성으로 사용하고 레이블을 타깃으로 사용"],"metadata":{"id":"pIOakwuJ9K3v"},"id":"pIOakwuJ9K3v"},{"cell_type":"code","source":["import numpy as np\n","\n","X_train = np.array(emotions_hidden[\"train\"][\"hidden_state\"])\n","X_valid = np.array(emotions_hidden[\"validation\"][\"hidden_state\"])\n","Y_train = np.array(emotions_hidden[\"train\"][\"label\"])\n","Y_valid = np.array(emotions_hidden[\"validation\"][\"label\"])\n","\n","X_train.shape, X_valid.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fmVDUYhH9gWa","executionInfo":{"status":"ok","timestamp":1673342197640,"user_tz":-540,"elapsed":7,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"e2af6fb0-be6c-4965-f794-201abf2e5bc7"},"id":"fmVDUYhH9gWa","execution_count":47,"outputs":[{"output_type":"execute_result","data":{"text/plain":["((16000, 768), (2000, 768))"]},"metadata":{},"execution_count":47}]},{"cell_type":"markdown","source":["## 훈련 세트 시각화\n","은닉 상태로 모델을 훈련하기 전에 분류하려는 감정에 대한 유용한 표현을 제공하는지 확인이 필요<br>\n","\n","768차원의 은닉 상태를 시각화하기 어렵기에 UMAP 알고리즘을 사용해 이 벡터를 2D로 투영 <br>\n","UMAP은 특성이 [0,1] 범위에 놓일 때 잘 작동. 사이킷런의 MinMaxScaler를 적용한 후 umap-learn 라이브러리의 UMAP 구현으로 은닉 상태의 차원을 축소"],"metadata":{"id":"dWkueZ3m-FFb"},"id":"dWkueZ3m-FFb"},{"cell_type":"code","source":["!pip uninstall umap"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bYDxENRtADzq","executionInfo":{"status":"ok","timestamp":1673342785584,"user_tz":-540,"elapsed":12206,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"4f259645-7939-440c-9ab1-efcc711e55c3"},"id":"bYDxENRtADzq","execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["Found existing installation: umap 0.1.1\n","Uninstalling umap-0.1.1:\n","  Would remove:\n","    /usr/local/lib/python3.8/dist-packages/umap-0.1.1.dist-info/*\n","    /usr/local/lib/python3.8/dist-packages/umap/*\n","  Would not remove (might be manually added):\n","    /usr/local/lib/python3.8/dist-packages/umap/aligned_umap.py\n","    /usr/local/lib/python3.8/dist-packages/umap/distances.py\n","    /usr/local/lib/python3.8/dist-packages/umap/layouts.py\n","    /usr/local/lib/python3.8/dist-packages/umap/parametric_umap.py\n","    /usr/local/lib/python3.8/dist-packages/umap/plot.py\n","    /usr/local/lib/python3.8/dist-packages/umap/sparse.py\n","    /usr/local/lib/python3.8/dist-packages/umap/spectral.py\n","    /usr/local/lib/python3.8/dist-packages/umap/umap_.py\n","    /usr/local/lib/python3.8/dist-packages/umap/utils.py\n","    /usr/local/lib/python3.8/dist-packages/umap/validation.py\n","Proceed (Y/n)? Y\n","  Successfully uninstalled umap-0.1.1\n"]}]},{"cell_type":"code","source":["!pip install umap-learn"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xybNOFKY_mcB","executionInfo":{"status":"ok","timestamp":1673342794193,"user_tz":-540,"elapsed":4302,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8ea84a9f-5555-4bf6-f408-d6c810321e0f"},"id":"xybNOFKY_mcB","execution_count":54,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: umap-learn in /usr/local/lib/python3.8/dist-packages (0.5.3)\n","Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.0.2)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.21.6)\n","Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (0.56.4)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from umap-learn) (4.64.1)\n","Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (1.7.3)\n","Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.8/dist-packages (from umap-learn) (0.5.8)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (57.4.0)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (5.2.0)\n","Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.8/dist-packages (from numba>=0.49->umap-learn) (0.39.1)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from pynndescent>=0.5->umap-learn) (1.2.0)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata->numba>=0.49->umap-learn) (3.11.0)\n"]}]},{"cell_type":"code","source":["# from umap import UMAP\n","import umap.umap_ as umap\n","from sklearn.preprocessing import MinMaxScaler\n","\n","# 특성 스케일을 [0,1] 범위로 조정\n","X_scaled = MinMaxScaler().fit_transform(X_train)\n","# UMAP 객체를 생성하고 훈련\n","mapper = umap.UMAP(n_components=2, metric=\"cosine\").fit(X_scaled)\n","\n","# 2D 임베딩의 데이터프레임을 생성\n","df_emb = pd.DataFrame(mapper.embedding_, columns=[\"X\", \"Y\"])\n","df_emb[\"label\"] = Y_train\n","df_emb.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"Sx0BapUg-xoq","executionInfo":{"status":"ok","timestamp":1673343223945,"user_tz":-540,"elapsed":42777,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"56c1711b-2f35-4d77-9bb2-51bc2fa92f77"},"id":"Sx0BapUg-xoq","execution_count":61,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          X         Y  label\n","0  4.256140  7.099303      0\n","1 -2.679181  6.564047      0\n","2  5.314543  3.535637      3\n","3 -2.102647  4.541106      2\n","4 -3.089052  4.619032      3"],"text/html":["\n","  <div id=\"df-48aa16e0-e461-4ff1-bbd2-d9b7ec4d1202\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>X</th>\n","      <th>Y</th>\n","      <th>label</th>\n"," 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emotions[\"train\"].features[\"label\"].names\n","\n","for i, (label, cmap) in enumerate(zip(labels, cmaps)):\n","  df_emb_sub = df_emb.query(f\"label == {i}\")\n","  axes[i].hexbin(df_emb_sub[\"X\"], df_emb_sub[\"Y\"], cmap=cmap, gridsize=20, linewidths=(0,))\n","  axes[i].set_title(label)\n","  axes[i].set_xticks([]), axes[i].set_yticks([])\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":369},"id":"Pj8-Ath4CD3i","executionInfo":{"status":"ok","timestamp":1673343641150,"user_tz":-540,"elapsed":1097,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d71499db-64a3-449a-e912-fcc63aec0459"},"id":"Pj8-Ath4CD3i","execution_count":63,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 504x360 with 6 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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 간단한 분류 모델 훈련\n","은닉 상태가 감정별로 조금씩 다르지만 일부 감정 사이에는 명확한 경계가 없기도 함 <br>\n","이 은닉 상태를 사용해 사이킷런의 로지스틱 회귀(logistic regression) 모델을 훈련"],"metadata":{"id":"G9UX5LkODxbZ"},"id":"G9UX5LkODxbZ"},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","\n","# 수렴을 보장하기 위해 'max_iter'를 증가\n","lr_clf = LogisticRegression(max_iter=3000)\n","lr_clf.fit(X_train, Y_train)\n","lr_clf.score(X_valid, Y_valid)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7wRPfAEDD8Kn","executionInfo":{"status":"ok","timestamp":1673344183711,"user_tz":-540,"elapsed":224801,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"18062b77-087e-4fce-d6c0-95e1c0b063dd"},"id":"7wRPfAEDD8Kn","execution_count":64,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.633"]},"metadata":{},"execution_count":64}]},{"cell_type":"markdown","source":["오차 행렬(confusion matrix)\n","\n","<br>\n","오차 행렬은 진짜 레이블과 예측 레이블의 관계를 보여줌"],"metadata":{"id":"Bm3KZUPZFtj5"},"id":"Bm3KZUPZFtj5"},{"cell_type":"code","source":["from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n","\n","def plot_confusion_matrix(y_preds, y_true, labels):\n","  cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n","  fig, ax = plt.subplots(figsize=(6,6))\n","  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n","  disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n","  plt.title(\"Normalized confusion matrix\")\n","  plt.show()\n","\n","y_preds = lr_clf.predict(X_valid)\n","plot_confusion_matrix(y_preds, Y_valid, labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"8ykaFc2WFq_3","executionInfo":{"status":"ok","timestamp":1673344451707,"user_tz":-540,"elapsed":650,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"cfa77d1c-4e0f-4902-b5a8-3e60a17e3460"},"id":"8ykaFc2WFq_3","execution_count":65,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["- anger, fear는 sadness와 가장 많이 혼동\n","- love, surprise는 joy로 많이 오인"],"metadata":{"id":"uMQIrPVEGw_0"},"id":"uMQIrPVEGw_0"},{"cell_type":"markdown","source":["## 2. 텍스트 분류 모델 훈련 - 미세 튜닝\n","\n","트랜스포머를 엔드-투-엔드로 미세 튜닝하는 방법 <br>\n","미세 튜닝 방식에서는 은닉 상태를 고정된 특성으로 사용하지 않고 전체 모델을 훈련 (분류 헤드는 미분 가능 해야 함)\n"],"metadata":{"id":"5KZ4dG9dHIVB"},"id":"5KZ4dG9dHIVB"},{"cell_type":"markdown","source":["분류 모델에 입력으로 사용하는 은닉 상태를 훈련하면 분류 작업에 적합하지 않은 데이터를 다룬다는 문제를 회피 할 수 있음"],"metadata":{"id":"0wmNDecZHroJ"},"id":"0wmNDecZHroJ"},{"cell_type":"markdown","source":["### 사전 훈련된 모델 로드\n","\n","특성 기반 방식에서 사용한 것과 같은 사전 훈련된 DistilBERT 모델을 사용 <br>\n","\n","But, AutoModel 대신 AutoModelForSequenceClassification을 사용, 이 모델은 사전 훈련된 모델 출력위에 베이스 모델과 함께 쉽게 훈련할 수 있는 분류 헤드가 있음 <br>\n","\n","분류 헤드의 출력 크기를 설정하기 위해 모델이 예측할 레이블 개수(여기서는 6)을 지정"],"metadata":{"id":"N642fZjSH0YQ"},"id":"N642fZjSH0YQ"},{"cell_type":"code","source":["from transformers import AutoModelForSequenceClassification\n","\n","num_labels = 6\n","model = (AutoModelForSequenceClassification.from_pretrained(model_ckpt, num_labels=num_labels).to(device))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1qo-aIBbG9jc","executionInfo":{"status":"ok","timestamp":1673345010467,"user_tz":-540,"elapsed":3708,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"abbffc8d-f3b7-4f04-9e27-bbb9d1c1358d"},"id":"1qo-aIBbG9jc","execution_count":66,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.weight']\n","- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"markdown","source":["모델 일부가 랜덤하게 초기화된다는 경고를 보냄. (분류 헤드가 아직 훈련되지 않아 발생)"],"metadata":{"id":"R_Ik1FnzIy1c"},"id":"R_Ik1FnzIy1c"},{"cell_type":"markdown","source":["#### 성공 지표 정의\n","\n","미세 튜닝 과정에서 모델 성능을 평가할 때 사용할 측정 지표를 정의 <br>\n","훈련하는 동안 성능을 모니터링하기 위해 Trainer에 사용할 compute_metrics() 함수를 정의"],"metadata":{"id":"YQ1rmWVjI5p4"},"id":"YQ1rmWVjI5p4"},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, f1_score\n","\n","def compute_metrics(pred):\n","  labels = pred.label_ids\n","  preds = pred.predictions.argmax(-1)\n","  f1 = f1_score(labels, preds, average=\"weighted\")\n","  acc = accuracy_score(labels, preds)\n","  return {\"accuracy\":acc, \"f1\":f1}"],"metadata":{"id":"lYfMGf2gJQNJ","executionInfo":{"status":"ok","timestamp":1673345269650,"user_tz":-540,"elapsed":923,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"id":"lYfMGf2gJQNJ","execution_count":67,"outputs":[]},{"cell_type":"code","source":["from transformers import Trainer, TrainingArguments\n","\n","batch_size=64\n","logging_steps = len(emotions_encoded[\"train\"]) // batch_size\n","model_name = f\"{model_ckpt}-finetuned-emotion\"\n","training_args = TrainingArguments(output_dir=model_name,\n","                                  num_train_epochs=2,\n","                                  learning_rate=2e-5,\n","                                  per_device_train_batch_size=batch_size,\n","                                  per_device_eval_batch_size=batch_size,\n","                                  weight_decay=0.01,\n","                                  evaluation_strategy=\"epoch\",\n","                                  disable_tqdm=False,\n","                                  logging_steps=logging_steps,\n","                                  push_to_hub=False,\n","                                  save_strategy=\"epoch\",\n","                                  load_best_model_at_end=True,\n","                                  log_level=\"error\")"],"metadata":{"id":"TzQOKwCXJ9M0","executionInfo":{"status":"ok","timestamp":1673345650008,"user_tz":-540,"elapsed":459,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"id":"TzQOKwCXJ9M0","execution_count":69,"outputs":[]},{"cell_type":"code","source":["from transformers import Trainer\n","\n","trainer = Trainer(model=model, args=training_args, compute_metrics=compute_metrics, train_dataset=emotions_encoded[\"train\"], eval_dataset=emotions_encoded[\"validation\"], tokenizer=tokenizer)\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":228},"id":"zX24iuGHLF0u","executionInfo":{"status":"ok","timestamp":1673345986689,"user_tz":-540,"elapsed":237842,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d77ec0a6-0847-4e4b-bef8-41044907f25d"},"id":"zX24iuGHLF0u","execution_count":70,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='500' max='500' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [500/500 03:56, Epoch 2/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.828300</td>\n","      <td>0.308017</td>\n","      <td>0.911000</td>\n","      <td>0.908939</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.244800</td>\n","      <td>0.213043</td>\n","      <td>0.921000</td>\n","      <td>0.920982</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=500, training_loss=0.5365411682128907, metrics={'train_runtime': 237.4156, 'train_samples_per_second': 134.785, 'train_steps_per_second': 2.106, 'total_flos': 720342861696000.0, 'train_loss': 0.5365411682128907, 'epoch': 2.0})"]},"metadata":{},"execution_count":70}]},{"cell_type":"code","source":["preds_output = trainer.predict(emotions_encoded[\"validation\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":17},"id":"A58G56dvMZX3","executionInfo":{"status":"ok","timestamp":1673346023963,"user_tz":-540,"elapsed":6167,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"fee6f7b4-4b89-48f1-fc63-aaa6cea1ac73"},"id":"A58G56dvMZX3","execution_count":71,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}}]},{"cell_type":"code","source":["preds_output.metrics"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XBUSiYPlMheE","executionInfo":{"status":"ok","timestamp":1673346045245,"user_tz":-540,"elapsed":1141,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"cbca25ec-1eff-435d-f9c7-32b0403dbddf"},"id":"XBUSiYPlMheE","execution_count":72,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'test_loss': 0.21304255723953247,\n"," 'test_accuracy': 0.921,\n"," 'test_f1': 0.9209815490643459,\n"," 'test_runtime': 5.5724,\n"," 'test_samples_per_second': 358.913,\n"," 'test_steps_per_second': 5.743}"]},"metadata":{},"execution_count":72}]},{"cell_type":"code","source":["y_preds = np.argmax(preds_output.predictions, axis=1)"],"metadata":{"id":"MAQvfKvVMuaq","executionInfo":{"status":"ok","timestamp":1673346102420,"user_tz":-540,"elapsed":2,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"id":"MAQvfKvVMuaq","execution_count":73,"outputs":[]},{"cell_type":"code","source":["plot_confusion_matrix(y_preds, Y_valid, labels)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"0MtdrVCAMzxy","executionInfo":{"status":"ok","timestamp":1673346130828,"user_tz":-540,"elapsed":7,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"ac56f53b-7b3e-4e68-9a27-29424de927c9"},"id":"0MtdrVCAMzxy","execution_count":75,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#### 오류 분석\n","\n","모델의 손실 기준으로 검증 샘플을 정렬 <br>\n","정방향 패스의 결과와 레이블을 사용하면 손실은 자동으로 계산 가능."],"metadata":{"id":"HD_r8G7GNKu8"},"id":"HD_r8G7GNKu8"},{"cell_type":"code","source":["# 손실과 예측 레이블을 반환\n","from torch.nn.functional import cross_entropy\n","\n","def forward_pass_with_label(batch):\n","  # 모든 입력 텐서를 모델과 같은 장치로 이동\n","  inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n","\n","  with torch.no_grad():\n","    output = model(**inputs)\n","    pred_label = torch.argmax(output.logits, axis=-1)\n","    loss = cross_entropy(output.logits, batch[\"label\"].to(device), reduction=\"none\")\n","\n","  # 다른 데이터셋 열과 호환되도력 출력을 CPU로 옮김\n","  return {\"loss\":loss.cpu().numpy(), \"predicted_label\":pred_label.cpu().numpy()}"],"metadata":{"id":"_vElx2WqNeIE","executionInfo":{"status":"ok","timestamp":1673346476379,"user_tz":-540,"elapsed":4,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"id":"_vElx2WqNeIE","execution_count":76,"outputs":[]},{"cell_type":"markdown","source":["map 메서드로 이 함수를 적용해 모든 샘플의 손실을 구함"],"metadata":{"id":"R1pbWEwvOQls"},"id":"R1pbWEwvOQls"},{"cell_type":"code","source":["# 데이터셋을 다시 파이토치 텐서로 변환\n","emotions_encoded.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])\n","\n","# 손실 값을 계산\n","emotions_encoded[\"validation\"] = emotions_encoded[\"validation\"].map(forward_pass_with_label, batched=True, batch_size=16)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["80aa883b3cf649ceb6e5b85ed5ea5509","313593925c2a4141a64864e53e122063","0b9065b551c8412f80340a506e6c685b","2c451fd1646d400d8d24adef4c5edaab","44040ea5691b408a8f206b0acc3b9ea7","838fadbc513045ce9492e00a834433af","aeb48bd095004b65b3eccd405c997500","d94d1a4d44ad4a4594f1008be77cfe51","0bdcc5fd663c45d79e31442b9abb40e5","8e0cd84d88f0431eb265ecf5114b2288","7a4511d0bb9143569319cf5ae8c7836f"]},"id":"9BUPB6pkOPJh","executionInfo":{"status":"ok","timestamp":1673346600282,"user_tz":-540,"elapsed":13943,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"4d3981b7-b2b5-44ed-be79-fabb1adf30f7"},"id":"9BUPB6pkOPJh","execution_count":77,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/125 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"80aa883b3cf649ceb6e5b85ed5ea5509"}},"metadata":{}}]},{"cell_type":"markdown","source":["텍스트, 손실, 예측 레이블과 진짜 레이블로 DataFrame을 생성"],"metadata":{"id":"ksf6TBZOOwOr"},"id":"ksf6TBZOOwOr"},{"cell_type":"code","source":["emotions_encoded.set_format(\"pandas\")\n","cols = [\"text\", \"label\", \"predicted_label\", \"loss\"]\n","df_test = emotions_encoded[\"validation\"][:][cols]\n","df_test[\"label\"] = df_test[\"label\"].apply(label_int2str)\n","df_test[\"predicted_label\"] = (df_test[\"predicted_label\"].apply(label_int2str))"],"metadata":{"id":"qp9-oIkNO0Bo","executionInfo":{"status":"ok","timestamp":1673346776092,"user_tz":-540,"elapsed":838,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"id":"qp9-oIkNO0Bo","execution_count":78,"outputs":[]},{"cell_type":"markdown","source":["emotions_encoded를 손실 기준으로 오름차순 혹은 내림차순으로 정렬\n","\n","--> 데이터에 레이블을 부여하는 프로세스는 모두 완벽하지 않음 <br>\n","잘못 레이블링된 샘플이 존재 할 수 있음 앞의 방식을 사용하면 이런 레이블을 빠르게 찾아 수정이 가능"],"metadata":{"id":"KWR1b4ztPZhW"},"id":"KWR1b4ztPZhW"},{"cell_type":"markdown","source":["#### 데이터셋의 특이사항\n","텍스트 데이터에서는 입력에 포함된 특수 문자나 문자열이 모델 예측에 큰 영향을 미치기도 함 <br>\n","모델의 가장 나쁜 예측을 들여다보게 되면 이런 특성을 찾게 되고, 데이터를 정제하거나 비슷한 샘플을 추가하면 모델이 안정됨"],"metadata":{"id":"hHxQvP99P4g5"},"id":"hHxQvP99P4g5"},{"cell_type":"code","source":["df_test.sort_values(\"loss\", ascending=False).head(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"YBLpveUHQDXr","executionInfo":{"status":"ok","timestamp":1673346975822,"user_tz":-540,"elapsed":1126,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f57ccba6-0b46-4b98-83c5-aed7b0c160a6"},"id":"YBLpveUHQDXr","execution_count":79,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                   text     label  \\\n","1963  i called myself pro life and voted for perry w...       joy   \n","1950  i as representative of everything thats wrong ...  surprise   \n","1274  i am going to several holiday parties and i ca...       joy   \n","1111  im lazy my characters fall into categories of ...       joy   \n","1509  i guess this is a memoir so it feels like that...       joy   \n","1870  i guess i feel betrayed because i admired him ...       joy   \n","1801  i feel that he was being overshadowed by the s...      love   \n","318   i felt ashamed of these feelings and was scare...      fear   \n","1500  i guess we would naturally feel a sense of lon...     anger   \n","882   i feel badly about reneging on my commitment t...      love   \n","\n","     predicted_label      loss  \n","1963         sadness  5.367970  \n","1950         sadness  5.284013  \n","1274         sadness  5.276999  \n","1111            fear  5.139698  \n","1509            fear  5.066939  \n","1870         sadness  5.032856  \n","1801         sadness  4.932096  \n","318          sadness  4.787760  \n","1500         sadness  4.679534  \n","882          sadness  4.383337  "],"text/html":["\n","  <div id=\"df-930b6b62-bcb3-4e5a-8c7a-d4a0b3c33403\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","      <th>predicted_label</th>\n","      <th>loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1963</th>\n","      <td>i called myself pro life and voted for perry w...</td>\n","      <td>joy</td>\n","      <td>sadness</td>\n","      <td>5.367970</td>\n","    </tr>\n","    <tr>\n","      <th>1950</th>\n","      <td>i as representative of everything thats wrong ...</td>\n","      <td>surprise</td>\n","      <td>sadness</td>\n","      <td>5.284013</td>\n","    </tr>\n","    <tr>\n","      <th>1274</th>\n","      <td>i am going to several holiday parties and i ca...</td>\n","      <td>joy</td>\n","      <td>sadness</td>\n","      <td>5.276999</td>\n","    </tr>\n","    <tr>\n","      <th>1111</th>\n","      <td>im lazy my characters fall into categories of ...</td>\n","      <td>joy</td>\n","      <td>fear</td>\n","      <td>5.139698</td>\n","    </tr>\n","    <tr>\n","      <th>1509</th>\n","      <td>i guess this is a memoir so it feels like that...</td>\n","      <td>joy</td>\n","      <td>fear</td>\n","      <td>5.066939</td>\n","    </tr>\n","    <tr>\n","      <th>1870</th>\n","      <td>i guess i feel betrayed because i admired him ...</td>\n","      <td>joy</td>\n","      <td>sadness</td>\n","      <td>5.032856</td>\n","    </tr>\n","    <tr>\n","      <th>1801</th>\n","      <td>i feel that he was being overshadowed by the s...</td>\n","      <td>love</td>\n","      <td>sadness</td>\n","      <td>4.932096</td>\n","    </tr>\n","    <tr>\n","      <th>318</th>\n","      <td>i felt ashamed of these feelings and was scare...</td>\n","      <td>fear</td>\n","      <td>sadness</td>\n","      <td>4.787760</td>\n","    </tr>\n","    <tr>\n","      <th>1500</th>\n","      <td>i guess we would naturally feel a sense of lon...</td>\n","      <td>anger</td>\n","      <td>sadness</td>\n","      <td>4.679534</td>\n","    </tr>\n","    <tr>\n","      <th>882</th>\n","      <td>i feel badly about reneging on my commitment t...</td>\n","      <td>love</td>\n","      <td>sadness</td>\n","      <td>4.383337</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-930b6b62-bcb3-4e5a-8c7a-d4a0b3c33403')\"\n","              title=\"Convert this dataframe to an interactive 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selection on the front ...   joy             joy   \n","1263                 i feel this way about blake lively   joy             joy   \n","\n","          loss  \n","19    0.015154  \n","205   0.015296  \n","669   0.015381  \n","604   0.015426  \n","11    0.015474  \n","1513  0.015520  \n","578   0.015627  \n","1320  0.015640  \n","329   0.015814  \n","1263  0.015964  "],"text/html":["\n","  <div id=\"df-94155b08-0813-4af8-a775-a2507edd6b58\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","      <th>label</th>\n","      <th>predicted_label</th>\n","   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<td>0.015474</td>\n","    </tr>\n","    <tr>\n","      <th>1513</th>\n","      <td>i have also been getting back into my gym rout...</td>\n","      <td>joy</td>\n","      <td>joy</td>\n","      <td>0.015520</td>\n","    </tr>\n","    <tr>\n","      <th>578</th>\n","      <td>i got to christmas feeling positive about the ...</td>\n","      <td>joy</td>\n","      <td>joy</td>\n","      <td>0.015627</td>\n","    </tr>\n","    <tr>\n","      <th>1320</th>\n","      <td>im feeling positive but its impossible to desc...</td>\n","      <td>joy</td>\n","      <td>joy</td>\n","      <td>0.015640</td>\n","    </tr>\n","    <tr>\n","      <th>329</th>\n","      <td>i have had my treasury selection on the front ...</td>\n","      <td>joy</td>\n","      <td>joy</td>\n","      <td>0.015814</td>\n","    </tr>\n","    <tr>\n","      <th>1263</th>\n","      <td>i feel this way about blake lively</td>\n","      <td>joy</td>\n","      <td>joy</td>\n","      <td>0.015964</td>\n","    </tr>\n","  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+# 다중언어 개체명 인식 (MultiLanguage Name Entity Recognition)
+
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{len(xtreme_subsets)}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130,"referenced_widgets":["463f5547f42a407ea77c9da398cd0d70","3e8e0acc912442dcb2c4c9ab990d9408","e24c62ac74114852a4aba3da8279f997","b7ae4618d0dc4d80b4425c3ca2ac59ac","6ec2206460944b53abfa4e3fb6900f68","57deeee9ba6a4b13a5fc7c92b5e2316b","f6d6006d40a849d89e7e118b03661ed1","048a919356ba4c5f9047b7e3e315deb9","e5dd8f341c584512a9bb41fbfffbffc0","1225a3d5d9724ce4988cdedbbd1c37ac","971d5d798c564be7a38b86548aec1f4e","81a0f10dfe0a4c6abfd1c0437a13b1c2","670de4b7e76f408488d5dabc8dcdc64d","82c09669de694a318b21ce5e43530086","f2d860c19e4a4374aed9462457a8a44c","0ca6b4590a7e47c58da0e4570ac503a7","2fe526df2564455f9003114ebc74d911","9384638f81cd4c95a32b75064711fca6","ac3226796b3e4387b2578d1de79f3ce9","7880a8251bbe4524a345dac05300ceed","e8bcf6f9f89f4af3b67046a16dce62fb","0e2d378d62ed423e8d7d03f3d90d72a5","fdf2854735904915b5d9600a2fb43dc7","def20d8846dc4fb289254a64ed3ac05b","b83be59fa2834698bbcbae99d2a77dde","efc301732ca644aa97b437f539289cef","aa5b46580ef541c4a2b0879397cf5468","65fb2f5a632b4970bc8ac087341d4a21","f072833a7bb64cf5a34d08e7e1c18f35","d6c0de66f82348f08b694484812fad12","93cdf06140e84a9392470a75d55e9435","46a99245732946ca8906452625cf4393","f5c26f9c034847d1907ba505b0e1a4b5"]},"id":"RI7VQ3hAvolA","executionInfo":{"status":"ok","timestamp":1675820976873,"user_tz":-540,"elapsed":9184,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f81e49e9-ca19-4062-ff2f-3cc764de391d"},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/37.5k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"463f5547f42a407ea77c9da398cd0d70"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/593k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"81a0f10dfe0a4c6abfd1c0437a13b1c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/105k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fdf2854735904915b5d9600a2fb43dc7"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["XTREME 서브셋 개수: 183\n"]}]},{"cell_type":"code","source":["panx_subsets = [s for s in xtreme_subsets if s.startswith(\"PAN\")]\n","panx_subsets[:3]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d4AN8nBiwM62","executionInfo":{"status":"ok","timestamp":1675820976874,"user_tz":-540,"elapsed":10,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"951ec0f0-86ca-4477-b619-2238e00050e5"},"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['PAN-X.af', 'PAN-X.ar', 'PAN-X.bg']"]},"metadata":{},"execution_count":4}]},{"cell_type":"markdown","source":["ISO 639-1 언어 코드로 보이는 두 문자로 된 접미사가 존재 \n","<br>\n","ex] 독일어(de), 프랑스어(fr), 이탈리아어(it), 영어(en)"],"metadata":{"id":"uagWPzGswb1z"}},{"cell_type":"code","source":["from collections import defaultdict\n","from datasets import DatasetDict\n","\n","from datasets import load_dataset\n","\n","langs = [\"de\", \"fr\", \"it\", \"en\"]\n","# 스위스에서 사용하는 언어로 독일어(62.9%), 프랑스어(22.9%), 이탈리아어(8.4%), 영어(5.9%)\n","fracs = [0.629, 0.229, 0.084, 0.059]\n","\n","# 키가 없는 경우 DatasetDict를 반환\n","panx_ch = defaultdict(DatasetDict)\n","\n","for lang, frac in zip(langs, fracs):\n","  # 다국어 말뭉치를 로드\n","  ds = load_dataset(\"xtreme\", name=f\"PAN-X.{lang}\")\n","  # 각 분할을 언어 비율에 따라 다운 샘플링하고 섞기\n","  # ds는 train, validaton, test 3가지로 나옴 \n","  for split in ds:\n","    # 데이터셋에 의도하지 않은 편향이 들어가지 않도록 shuffle 함수를 사용\n","    # select() 메서드는 fracs 값을 따라 각 말뭉치를 다운샘플링\n","    panx_ch[lang][split] = (ds[split].shuffle(seed=0).select(range(int(frac * ds[split].num_rows))))  \n","    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Jung","userId":"11224950994115057744"}},"outputId":"851b37ff-848a-4a21-b8eb-fc118b2858c2"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset xtreme/PAN-X.de to /root/.cache/huggingface/datasets/xtreme/PAN-X.de/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/234M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e734c6943df64bc9b362c9d9e0c502b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/20000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7067bd8b5bf44733918d96a9756daa16"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8db9618fc24444e1b1e0648188494313"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8c0fd6197d7a470c9456de121a762cf0"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.de/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"528fea30282642ffa2648136daf752ed"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset xtreme/PAN-X.fr to /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/20000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17091a06c8e645f3bf44ceb65aa500a7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"455d943989564ef89f404387adda250c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"90247571f6a5433491df5e18e65e9679"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fdca93baefd4245ac3873186d587c6e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset xtreme/PAN-X.it to /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/20000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3391d8a5aaaf4e1098cce55341610356"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46981a07ed6d4e2799ceb643c28b1363"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ea5e3a222af7442bb5d3785c901418cc"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"91bf26ecbf69487ca698874a0fefd0a4"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset xtreme/PAN-X.en to /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4...\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/20000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e8c2180648c40c588ab3afe5eaabc50"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dc9f3eeb357841538c6234a39ff3394c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/10000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c9a290e3a6cc494595310142f6816c1e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset xtreme downloaded and prepared to /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae117bea6d4847c4aa9cfc9882ac6cc0"}},"metadata":{}}]},{"cell_type":"code","source":["import pandas as pd\n","\n","pd.DataFrame({lang: [panx_ch[lang][\"train\"].num_rows] for lang in langs}, index=[\"Number of training examples\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"rWMqNZ7oza_P","executionInfo":{"status":"ok","timestamp":1675821041701,"user_tz":-540,"elapsed":35,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"affbc911-49b9-43b6-c49a-d9398f050322"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                de    fr    it    en\n","Number of training examples  12580  4580  1680  1180"],"text/html":["\n","  <div id=\"df-20ed284c-9cbe-4d99-b563-23d4879d79e1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>de</th>\n","      <th>fr</th>\n","      <th>it</th>\n","      <th>en</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Number of training examples</th>\n","      <td>12580</td>\n","      <td>4580</td>\n","      <td>1680</td>\n","      <td>1180</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20ed284c-9cbe-4d99-b563-23d4879d79e1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-20ed284c-9cbe-4d99-b563-23d4879d79e1 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["element = panx_ch[\"de\"][\"train\"][0]\n","for key, value in element.items():\n","  print(f\"{key}: {value}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aKPfBEQa0QPc","executionInfo":{"status":"ok","timestamp":1675821041701,"user_tz":-540,"elapsed":32,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"735a815d-23bf-410e-90bd-23cd01707dcf"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["tokens: ['2.000', 'Einwohnern', 'an', 'der', 'Danziger', 'Bucht', 'in', 'der', 'polnischen', 'Woiwodschaft', 'Pommern', '.']\n","ner_tags: [0, 0, 0, 0, 5, 6, 0, 0, 5, 5, 6, 0]\n","langs: ['de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de', 'de']\n"]}]},{"cell_type":"markdown","source":["ner_tags 열은 각 개체명이 매핑된 클래스 ID에 해당"],"metadata":{"id":"hqdQleVD14e5"}},{"cell_type":"code","source":["for key, value in panx_ch[\"de\"][\"train\"].features.items():\n","  print(f\"{key}: {value}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hM9FN5Zo19Di","executionInfo":{"status":"ok","timestamp":1675821041701,"user_tz":-540,"elapsed":27,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"b6fc3246-d49c-4660-9e7e-01d17b2ca4f5"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["tokens: Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n","ner_tags: Sequence(feature=ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], id=None), length=-1, id=None)\n","langs: Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)\n"]}]},{"cell_type":"code","source":["tags = panx_ch[\"de\"][\"train\"].features[\"ner_tags\"].feature\n","print(tags)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dJekic5A4myS","executionInfo":{"status":"ok","timestamp":1675821041701,"user_tz":-540,"elapsed":25,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"aea7ab1e-6eec-48b3-a022-c34fa1b2c48b"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], id=None)\n"]}]},{"cell_type":"markdown","source":["각 태그의 클래스 이름을 담은 새로운 역(ner_tags_str)을 훈련 세트에 추가"],"metadata":{"id":"gBHMZN4SA7Hp"}},{"cell_type":"code","source":["def create_tag_names(batch):\n","  return {\"ner_tags_str\": [tags.int2str(idx) for idx in batch[\"ner_tags\"]]}\n","\n","panx_de = panx_ch[\"de\"].map(create_tag_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["211d525aa2814de2b0559e0dba68865f","1486435bca7f495fa811be4138aba46a","32a958e5606945f3804b1a846ae17e9a","01b0dbf5c17a4adb8019f9d012430c92","5bcdcf410d0e418b83d645a0f514e1fd","40371b8e54db4d1ea5c8851b7d353943","9b3a23f11be043f0ad9d9a4ff7876167","0b837815453b420ea0eccfec7293a5f3","e981c54d25834d029685877830f3469d","3d953ad6dff14d268a8537ff39c19bd3","d0ba5d92c8c04a61b6a92e73b81af2b2","a90a1575956d471f8150a7585033ae18","a3729075e35a425b90da9a04ccf385d3","c737f90a71504bdfa60c6b64b767735c","36fe20b170df4c698e4c36346a3f0766","6d67aeddb4674c2195669854c0dc547a","302aa0a2e6d84a07a2d3d1af5894e6be","88c7be98258f4de48094f21a21e21832","42c8625242a244f6b93f54ddbb7f2fe2","46f5749cbb4644bdbd38becc552e3259","0e1811d3227849ddb8f855d7079e675b","cb2f805494394845aff0fb4e567fef50","e09e8e70bbb44f32b63f38f94066b47d","2085200a95a248a79e0ce40d4b96f476","d5d1e64b89a74cc0b4133b7dbf462676","3d04d7c1e2f547a981d78ec01746f7c0","10ed85fdb5614fe9a88e419f158a7f41","a7d15fc1f337473980bb5135ef18cf2b","c6643cdb310c45fd8dcf25afdb9084cc","3d1cd8e019d34762b3dd4062b434649d","d39c7ae79ba84ed0bfe6e6e0a24f21af","0ef37e041b0047f4ad5c5c1aec14cbb4","cdac238f7cf445cc84041d7302624626"]},"id":"gso5en67AD5T","executionInfo":{"status":"ok","timestamp":1675821045732,"user_tz":-540,"elapsed":4054,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"daf58063-f561-4511-cabc-b2b44f99fa26"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/12580 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"211d525aa2814de2b0559e0dba68865f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6290 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a90a1575956d471f8150a7585033ae18"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/6290 [00:00<?, ?ex/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e09e8e70bbb44f32b63f38f94066b47d"}},"metadata":{}}]},{"cell_type":"code","source":["de_example = panx_de[\"train\"][0]\n","pd.DataFrame([de_example[\"tokens\"], de_example[\"ner_tags_str\"]], ['Tokens', 'Tags'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"O_s4LbJkAX0R","executionInfo":{"status":"ok","timestamp":1675821045732,"user_tz":-540,"elapsed":27,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"9a82f138-8251-4a61-9c6b-53efab33f595"},"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           0           1   2    3         4      5   6    7           8   \\\n","Tokens  2.000  Einwohnern  an  der  Danziger  Bucht  in  der  polnischen   \n","Tags        O           O   O    O     B-LOC  I-LOC   O    O       B-LOC   \n","\n","                  9        10 11  \n","Tokens  Woiwodschaft  Pommern  .  \n","Tags           B-LOC    I-LOC  O  "],"text/html":["\n","  <div id=\"df-6bb96800-c116-48cf-8846-aa6b223d04b8\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    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<td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-LOC</td>\n","      <td>I-LOC</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-LOC</td>\n","      <td>B-LOC</td>\n","      <td>I-LOC</td>\n","      <td>O</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bb96800-c116-48cf-8846-aa6b223d04b8')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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방식으로 평가\n","<br>\n","## 1. en\n","- 영어 훈련 데이터에서 미세 튜닝한 다음 각 언어의 테스트 세트에서 평가\n","\n","## 2. each\n","- 언어별 성능을 측정하기 위해 단일 언어의 테스트 세트에서 미세 튜닝하고 평가\n","\n","## 3. all\n","- 모든 훈련 데이터에서 미세 튜닝해 각 언어의 테스트 세트에서 평가\n","\n"],"metadata":{"id":"wi71g2zCCX_p"}},{"cell_type":"markdown","source":["# XLM-R 토큰화\n","XLM-R은 WordPiece 토크나이저 대신 100개 언어의 텍스트에서 훈련된 SentencePiece라는 토크나이저를 사용"],"metadata":{"id":"4FANse1eD9u2"}},{"cell_type":"code","source":["!pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"enKCHp9yFGm6","executionInfo":{"status":"ok","timestamp":1675821054744,"user_tz":-540,"elapsed":9035,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"9f9f1135-0bfd-45d1-bc4e-09e39cb2daee"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers\n","  Downloading transformers-4.26.0-py3-none-any.whl (6.3 MB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m49.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.12.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.25.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (23.0)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n","Requirement already satisfied: filelock 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urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.26.14)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n","Installing collected packages: tokenizers, transformers\n","Successfully installed tokenizers-0.13.2 transformers-4.26.0\n"]}]},{"cell_type":"code","source":["from transformers import AutoTokenizer\n","\n","bert_model_name = \"bert-base-cased\"\n","xlmr_model_name = \"xlm-roberta-base\"\n","bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)\n","xlmr_tokenizer = AutoTokenizer.from_pretrained(xlmr_model_name)"],"metadata":{"id":"5OnUUkFDERfK","executionInfo":{"status":"ok","timestamp":1675821078805,"user_tz":-540,"elapsed":24081,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"colab":{"base_uri":"https://localhost:8080/","height":241,"referenced_widgets":["f1f69f016e5f4d179fc1a3a3639e194f","4f5da1ad913e472c9d77e1cdc4f8e2b6","ebd7b36421f948439548e7903ed81ee6","96f5ba8063074c748b250f3d38de52f2","31ddd298f1894a069583d6d93d2e45eb","2ead4e84970946d9859ce37da2175d3e","acd513b021d24cd4b127755190f90302","016e44c2b25545e693c1a316ad17deaf","4185518d566c42468315e518bf9fcfe7","0ed70f7d08fa49819bb9c60eafaec297","0644205b912848a7a7f77830b7258223","94f51cdae0494f0dbcf0b341047c90dc","efa3383f5b524d56ae509f4d8e278d22","fb036d925be044438f01290f948f8e02","8365cc260cba472d85e6a02ded253ca1","0e2d034462e94a178aa4cfa54343bc69","8c91896dddd142b1996ab976b14cc1c7","d582d329ca4449349a6e08e11ac7d25c","c71c37c8c3f443f88c38d94ba7a597c7","00e0165acad84074a1b02f60b8993d8c","345a8b6eefe348de982f2fe100e4b94c","57553ee721924d69b2846ab8a05e4305","4604e0047d924a839e969b1f219a8d5e","c24a2e3f6fa842b6ac52996933480550","5ee0e7b975c74d4cbdc765e7d82e2ef0","de8687af54af49b99a3f05ec8654c83d","89139f8f31c846c48a3d0b329b881bde","33cd59f8716d4f7ea40821c7774a38fa","2e793a8f212a427186a427b54c00fe33","6a6f5218d1c54be7a30777ceb4fd6b00","85f8bbb7d8b24b5a95c7fb674d6cb43a","60207316f72e435f9266225c5f6fbc19","ff8103828112408cbf16c5c6d13b2a33","f753628d026a471a8b6f7987011dfe91","670b05b90bdf4587beb6a9e1b3f998e8","8b91d4f194844d68bd81acd3438b7ba2","07e98f59b8fb4ffe9c21f62f2e1b3ae5","6ccf052943204fb7be8c2a2987237148","d94df671dfa14c02bac4f12819c0748e","d9978751d44d486b8d99c812088d29ce","c9dfffd412d045a283e46566158deee7","69e2116a570c4b61b55b7dd7ff0545e7","b991d0341ba748a5a86e36682c350a10","098f8beefedf487e8e620a33e5b4873d","5bfc16d5cd14405d969e20261f913e40","2ddc82fde5284c5fa22e80a1ded0c4de","0090c677db7045df9b5ce4935468b238","9f9f9d032e9c468990f61a9769620a6d","332ea6b461d744bdaf2edbf3048d80ea","fdcfc65469e04a4baef6def0234b3884","021e8ac000564e9692aa09f083f36704","dab4bb5c9bd44bff9e91421be0977990","6d7f64577b9c48a8848a84dfded91fe4","9d602ef4136d43f0a5b6beee8555a3bb","bc060a9491da4500b6279c7e4f8880e3","80d6515bf0e145c189303a631f4ea08f","801affdca836427988a60f8d21deda58","f83ba0cbd9cc4a1588c7e76b43f068c0","e1b3263abf434c8d94b02b0be45f9434","d97430ac276e4589b503b90886cb8a00","df18f7c7b94a40ae828ea9eac0a60b34","925a4454e6e449218c42585198563cbf","55df9e5858dd4284b40482ebfa346142","e90cebb0049341fdb70f11727cbf5e79","1d630e8957ad45728b9a539d32692cc1","f4ca2927907243ffa08b2baa44941069","d1fd8e9e28984643b4f55abd35f77840","123eb17fe6c243688872a572c2cc88ec","8cac781872fb407facc613e411f8d0c4","fc3d29bb3e2d42a696a2b51aa9e19459","784055e398204ce8a9029852e73ef5bd","bab93cded8a045f299b7374306048456","914f3a746bd547f9a951132454e7818a","c8d7f27575f1400a932be83e3b2c2e07","e4b4b462d7af44efa4be4c8e1259da99","a4017fc65bb64bb391f7f90fcad7629c","686bc45dd1384f158656a37caa5e4563"]},"outputId":"c51b37aa-7370-4d7b-a9c0-e093e4b9c23b"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading 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Jung","userId":"11224950994115057744"}},"outputId":"fa795f05-3048-43f9-8317-789e57e22abf"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["['[CLS]', 'Jack', 'Spa', '##rrow', 'loves', 'Net', 'York', '!', '[SEP]']\n","['<s>', '▁Jack', '▁Spar', 'row', '▁love', 's', '▁Net', '▁York', '!', '</s>']\n"]}]},{"cell_type":"markdown","source":["## 토큰화 파이프라인\n","토큰화를 다룰 때 문자열을 모델에 주입할 정수로 변환하는 연산으로만 여겼지만 이는 전적으로 옳은 것은 아님. 실제 처리 파이프 라인은 아래와 같이 4 단계로 구성됨\n","\n","1. 정규화\n","2. 사전 토큰화\n","3. 토크나이저 모델\n","4. 사후처리\n","\n","1. 정규화\n","- 원시 문자열을 깨끗하게 만들기 위해 적용하는 일련의 연산, 대게 이런 연산에는 공백과 악센트가 붙는 문자를 제거하는 작업 등이 포함\n","- 유니코드 정규화(https://unicode.org/reports/tr15/)는 많은 토크나이저에서 적용하는 또 다른 일반적 정규화 연산\n","\n","2. 사전 토큰화(pretokenization)\n","- 텍스트를 더 작은 객체로 분할하며 훈련 마지막에 생성되는 토큰의 상한선을 제공\n","- 사전 토큰화가 텍스트를 단어로 분할하고 최종 토큰은 이 단어의 일부가 된다 생각하면 됨\n","- 영어, 독어 등 많은 유럽어족의 언어에서 문자열이 일반적으로 공백과 구두점을 기준 삼아 단어로 분할\n","  - 이런 단어는 파이프라인의 다음 단계에서 BPE, 유니그램 알고리즘을 사용해 부분단어(subword)로 분할하기 더 쉬움\n","\n","3. 토크나이저 모델\n","- 부분단어 분할 모델을 단어에 적용, 이 모델은 단어를 부분단어로 나눠 어휘사전의 크기와 OOV 토큰의 개수를 줄이는 역할을 함\n","- 부분단어 알고리즘\n","  - BPE,\n","  - 유니그램\n","  - WordPiece\n","\n","4. 사후처리\n","- 토큰 리스트에 부가적인 변환을 적용\n","  - Ex] 입력 토큰 인덱스의 시퀀스 처음과 끝에 특수 토큰을 추가하는 경우 등"],"metadata":{"id":"JIsrxh8oF-Q7"}},{"cell_type":"markdown","source":["# SentencePiece 토크나이저\n","유니그램이라는 부분단어 분할 방식을 기반으로 각 입력 텍스트를 유니코드 문자 시퀀스로 인코딩\n","- 일본어 등의 많은 언어에 공백문자가 있지 않는데 SentencePiece의 또 다른 고유한 특징은 공백문자가 유니코드 기호 U+2581 또는 '_' 문자에 할당 되므로 언어별 사전 토크나이저에 의존하지 않고 정확하게 시퀀스 복원이 가능 "],"metadata":{"id":"kFQNqaQgJLdW"}},{"cell_type":"markdown","source":["# 개체명 인식을 위한 트랜스포머\n","![NER_Transformer_Structure.png](data:image/png;base64,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)\n","\n"],"metadata":{"id":"tfce7a6ELLGY"}},{"cell_type":"markdown","source":["토큰 분류 작업에서 부분단어 처리 방법\n","<br>\n","예를 들어 이름 Christa는 Chr와 #ista로 토큰화 됨 이중 어느 단어에 (아니면 두 단어 모두에) B-PER 레이블을 할당해야 할까요?\n","<br>\n","BERT 논문에서 저자들은 첫번째 부분단어('Chr')에 할당하고 이어지는 부분단어는 무시, 무시한 부분단어를 IGN으로 표시. 나중에 후처리 단계에서 첫 번째 부분단어의 예측 레이블을 후속 부분단어로 쉽게 전파 가능"],"metadata":{"id":"rYnY6i3ANrS2"}},{"cell_type":"markdown","source":["# 트랜스포머 모델 클래스\n","트랜스포머스는 아키텍처와 작업마다 전용 클래스를 제공\n","<br>\n","작업에 연관된 모델 클래스 이름은 <ModelName>For<Task> 형식을 따름. AutoModel 클래스를 사용하는 경우 AutoModelFor<Task>\n","<br><br>\n","트랜스포머스는 기존 모델을 특정 작업에 맞춰 쉽게 확장 가능하도록 설계됨. 사전 훈련된 모델에서 가중치를 로드하고 작업에 특화된 헬퍼 함수를 사용. 특정 용도의 사용자 정의 모델을 만들 수 있음"],"metadata":{"id":"SNgK8H2mQFzH"}},{"cell_type":"markdown","source":["## 바디와 헤드\n","트랜스포머스의 모델들은 바디(body)와 헤드(head)로 나뉜 모델 구조를 가짐\n","<br>\n","모델의 마지막 층이 후속 작업에 맞는 층으로 변환. 이 마지막 층이 모델의 **헤드** 이며 작업에 특화\n","<br>\n","헤드를 제외한 모델의 나머지 부분을 **바디**라 함. 작업에 특화되지 않은 토큰 임베딩과 트랜스포머 층이 포함. 모델의 바디는 BertModel 또는 GPT2Model 같은 클래스로 구현되며 마지막 바디의 마지막 층의 은닉 상태를 반환\n","![Transformer_BodyHead.png](data:image/png;base64,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wVr1KyQACCCCAAAII+BXQaxoWBCItoHMXDplkHlO3thr26UrK3IWRrrnYZS7QgHGPHYfLnN3Gyb3PrYodBBlGAAEEEEAAAQRUQK9l9JqGBYEoCGTqhm/tQto7RYV2Kd3RTF0xwXQjzUQhi+Qh4QKBBoxqdeYHZsiDC1ZLV08+4XQUDwEEEEAAAQSSJlCTzVjXMkkrF+WJuIC5f1ADQPv+wpwVHG4bdKaWrtERr73EZy/wgHHa2CHy8UOmyTX3vpR4PAqIAAIIIIAAAskS0GsYvZZhQSAUATPiaLbZzF3Yt7XQDDqTbZ5k5i4M/LI8lCKQaPoEQvlknn74znLXUytkxbq29IlSYgQQQAABBBCIpcCEEY2i1zAsCPgVyDSM7m0tzPYddKZxjN+kOR6BiguEEjA21tXIt0+aLZ/79b8rXiBOiAACCCCAAAIIeBHQaxe9hmFBwJFAtlayTRNNa+G2QWd0qoptXUkzzF3oiJCd4iEQ2rfiPtNHycmmW8d19y+NhwS5RAABBBBAAIHUCug1i167sCAwSKB26LbWQp2zcPschsxdOEiKFQkVCC1gVK+zzAA4Ty9bJ88sW59QPoqFAAIIIIAAAnEX2GPKcOuaJe7lIP9+BMzchU3jewNDa2J7OzisH+EnYY5FIPYCvWPx5s0SRmlWrW+TU3/+kLzRuiWM5EkTAQQQQAABBBDwLDCqpU7+9KV3yrjhjZ7TKHRg698PKbSaddUWyNVvHXTGTEux9d5CbTHULqVm0BmzjQUBBLYLmGlbrFgx9IBRT/ncq+vltF8+Kh1dPdtzwCsEEEAAAQQQQKCKAvU1WfmvMw6Q3SYHP+ciAWMVK9acOmNaBe0pKrYHhlMk0ziOuQurWzWcPUYCFQ0Y1eW+51bJ+df8R7qZnzFGHxOyigACCCCAQDIFcma+xR9+4u3y7t3GhVJAAsZQWPsnum3uwlzvoDPb7i80o5JmapkapT8W7xBwL1DxgFGzePv85XLRDU9KOJ1f3SNwBAIIIIAAAgikT0A7WX3nY3vJkXtPDK3wBIwB0tY097m3sE9XUuYuDBCZpBAYLGAHjKEOejPwtPYX89w/P0VL40Ac3iOAAAIIIIBA6ALasnjJR/cMNVgMvRAJPUGmYWzvtBTWoDP2fYZmTkMWBBConkBFA0YtpgaNjXU5+fq187mnsXr1zpkRQAABBBBInYDes/i9U/YOrRtq6kC9FFjnLjQtg9vvL9zWjdQEh5maYAce8pI9jkEAgcECFRn0ZvBptw6Ec87v5zF6aiEc1iGAAAIIIIBAoAI6GupPPrVPKAPcFMpo6rukmrkLt89ZaE9ob+4tbJpgBp3JFiJjHQIIREzA7pJatYBRPXTKjQtMSyPzNEbs00F2EEAAAQQQSJCAzrN4mWlZDHrqjFJE6QgY7bkLzbQU1pyFW1sLczroTN2wUjxsQwCBGAhEImBUp87uHrnin4vkuvuXxoCNLCKAAAIIIIBAnAROPmSanPWBGVKbq2yrVqICxlxDkUFnJpu5C+vi9HEgrwgg4EIgMgGjned5S96Qb9/4tKxY12av4hkBBBBAAAEEEPAkMGFEo3z7pNmyz/RRno73e1AcA8ZM/aitgaHVWmi6keqgM0N07sKxzF3o9wPB8QjEUCByAaMatm3pkqvuWizXm9bGLuZrjOHHiiwjgAACCCBQXYEaMwrqx02r4umH72wG2av42H69hY9swGjmLsw279AbDFpBoelCagWGtc29+ecFAgggEMmA0a6Wpas3yi9MN9V7n1tlr+IZAQQQQAABBBAoKTBnt3Fypul+Om1s9Sdtr3rAWDPEBIGTzb2FW4NBDQytewubJkomW71AumQFshEBBCIlEOmA0ZZ65pX1cvVdL8qDC9fYq3hGAAEEEEAAAQT6CRw8c4x89vC3yR47Du+3vppvKhUwanfR7BAz6Iw1Z+H2KSqyDdXpiltNc86NAALBCsQiYLSLvGRlq9zw4Mty+/zlpttqt72aZwQQQAABBBBIqYDO6axzO3/s4KkyfXxL5BQCDRizdSYg1LkLt99XaI1K2qxzFzZEruxkCAEEkiEQq4DRJt/c0SV3PLlcbnv8NabisFF4RgABBBBAIEUCOkXGMftOkiP2mihN9dHtWuklYNSpKHqDQqsr6bZBZ5rGM3dhij7jFBWBqAjEMmDsi/f6m5vl3mdXySOL1sj8pevk/7V3J/Bezfkfx7+VtJA9EpKsTQYZFAaRJkR2WbIM8TDEWMdWUTHDkK0au6xDljL2PVuIIetUEpN1FP1LUpLO//s+M6fuvd3f/Z3z/Z3z+53fOa/vw/Xr3t853/P9Ps/53Xs+57st+JmWx5o+/BsBBBBAAIEsCDRv2sR03mBVs/2mrU23zdcy66zWsiqqVThgbGwarbD2f7uQ/q/FUGML/Ulnll+pKupGIRFAIB8CVR8w1jxNWsvx46/nmn99Psd8bLuvfvLNXPPZtz+amd8vMJ5Xc0v+jQACCCCAAAJpFGjUyJjWKzU37dZoaTqs1cpsZLuZ/mq9lc1Ga7cq+xqKcfjMfaynDQLtpDMKCmu0FqpraaPGTeM4BHkggAACiQpkKmAsJPXzosXmP7Pn28DxJ/Pt3J/M7HkLzRz79f38n83c+YvMPNvFVUt5LLDjIhf8vNgstK2UC23wqf0W/eL5S3ssst8vtlHnYrvMh1b60L8VhHp6tQfWv0kIIIAAAghkXUABnf3PX49P/25s/2dXsDCN7f/07+WaNDZa0mK5Jo1M0+Uam+Xt98vb1sHmTRub5na8YUu7xIW6kLZqsZxZqUVTs/IKy5tV7NcarZrZQLGZabNKC3+/rDtSPwQQQKBaBHIRMFbLyaCcCCCAAAIIIIAAAggggECaBIKAsXGaCkVZEEAAAQQQQAABBBBAAAEE0iNAwJiec0FJEEAAAQQQQAABBBBAAIFUCRAwpup0UBgEEEAAAQQQQAABBBBAID0CBIzpOReUBAEEEEAAAQQQQAABBBBIlQABY6pOB4VBAAEEEEAAAQQQQAABBNIjQMCYnnNBSRBAAAEEEEAAAQQQQACBVAkQMKbqdFAYBBBAAAEEEEAAAQQQQCA9AgSM6TkXlAQBBBBAAAEEEEAAAQQQSJUAAWOqTgeFQQABBBBAAAEEEEAAAQTSI0DAmJ5zQUkQQAABBBBAAAEEEEAAgVQJEDCm6nRQGAQQQAABBBBAAAEEEEAgPQIEjOk5F5QEAQQQQAABBBBAAAEEEEiVAAFjqk4HhUEAAQQQQAABBBBAAAEE0iNAwJiec0FJEEAAAQQQQAABBBBAAIFUCRAwpup0UBgEEEAAAQQQQAABBBBAID0CBIzpOReUBAEEEEAAAQQQQAABBBBIlQABY6pOB4VBAAEEEEAAAQQQQAABBNIjQMCYnnNBSRBAAAEEEEAAAQQQQACBVAkQMKbqdFAYBBBAAAEEEEAAAQQQQCA9AgSM6TkXlAQBBBBAAAEEEEAAAQQQSJUAAWOqTgeFQQABBBBAAAEEEEAAAQTSI0DAmJ5zQUkQQAABBBBAAAEEEEAAgVQJEDCm6nRQGAQQQAABBBBAAAEEEEAgPQK5DxgnTZqUnrNBSRBAAAEEEEAAAQQQQCA1ApMnT05NWSpVkNwGjFOmTDFHHnmk2XzzzStlz3ERQAABBBBAAAEEEEAgxQKdOnXyYwbFDnlNjYKKezYF/87yq54SXHzxxeaee+4xixcv9quak6pn+bRSNwQQQAABBBBAAAEEYhdo1Oi/4VLjxo3NYYcdZgYMGGA222yz2I+Txgxt3f3K5yZgrC9QDE4MAWMgwSsCCCCAAAIIIIAAAggEAkHAGHyfp8AxNwFjQ4FicOIJGAMJXhFAAAEEEEAAAQQQQCAQqBswBj/PQ+CY+YAxTKAYnHACxkCCVwQQQAABBBBAAAEEEAgECgWMwftZDhwzGzBGCRSDE03AGEjwigACCCCAAAIIIIAAAoFAsYAx2C6LgWPmAkaXQDE4wQSMgQSvCCCAAAIIIIAAAgggEAiEDRiD7bMUOGYmYCwlUAxOLAFjIMErAggggAACCCCAAAIIBAJRA8ZgvywEjlUfMMYRKAYnlIAxkOAVAQQQQAABBBBAAAEEAgHXgDHYv5oDx6oNGBUoDh061Nx7771L1lEMTojrKwGjqxz7IYAAAggggAACCCCQXYFSA8ZARoHjoYceagYOHFg16zhWXcCYRKAYnEACxkCCVwQQQAABBBBAAAEEEAgE4goYg/yqKXCsmoAxyUAxOHEEjIEErwgggAACCCCAAAIIIBAIxB0wBvlWQ+CY+oCxHIFicMIIGAMJXhFAAAEEEEAAAQQQQCAQSCpgDPJPc+CY2oCxnIFicKIIGAMJXhFAAAEEEEAAAQQQQCAQSDpgDI6TxsAxdQFjJQLF4AQRMAYSvCKAAAIIIIAAAggggEAgUK6AMThemgLH1ASMlQwUgxPDKwIIIIAAAggggAACCCCQFoE0BI4VDxgJFNNyOVIOBBBAAAEEEEAAAQQQSKNAJQPHigWMChSHDBliRo8eHds6imk8uZQJAQQQQAABBBBAAAEEEIhDQIFjnz59zKBBg8q2jmPZA0YCxTguFfJAAAEEEEAAAQQQQACBvAqUM3AsW8BIoJjXy5l6I4AAAggggAACCCCAQBIC5Qgcg4CxcRIVqJmnZiBdvHhxzR/xbwQQQAABBBBAAAEEEEAAgRIEFGOVY7WHRkEZ7cG84N9JvE6aNMkMHjzY3H///QSQSQCTJwIIIIAAAggggAACCGRaQC2LBx98sLnwwgtNx44dE61r0MJYtoAxqA2BYyDBKwIIIIAAAggggAACCCBQXKCcgWJQmooFjEEBCBwDCV4RQAABBBBAAAEEEEAAgWUFKhEoBqWoeMAYFITAMZDgFQEEEEAAAQQQQAABBBAwppKBYuCfmoAxKFAlA8eEh28GVeQVAQQQQAABBBBAAAEEqkjABk1lLW0aAsWgwqkLGIOCVSJwJGAM9HlFAAEEEEAAAQQQQACBQKBcAWOaAsUadfej5SUhc9KzpAYHDvuqwHHIkCHmvvvuS3xWVQLGsGeF7RBAAAEEEEAAAQQQyI9A0gGjAsVDDjnEDBo0KPFZT6OetdS2MNatSDkCRwLGuup8jwACCCCAAAIIIIAAAkkFjGkOFIOzXjUBY1DgJANHAsZAmVcEEEAAAQQQQAABBBAIBOIOGKshUKxR93R3SQ0KWvc1icCRgLGuMt8jgAACCCCAAAIIIIBAXAFjNQWKwVmvuhbGoODBa5yBIwFjoMorAggggAACCCCAAAIIBAKlBozVGCjWqHt1tjAGFQhe4wgcCRgDTV4RQAABBBBAAAEEEEAgEHANGKs5UKxR92wEjEGFSgkcCRgDRV4RQAABBBBAAAEEEEAgEIgaMGYhUKxR92wFjEHFXAJHAsZAj1cEEEAAAQQQQAABBBAIBMIGjFkKFGvUPZsBY1BBBY5Dhw41o0ePLrqOIwFjoMYrAggggAACCCCAAAIIBALFAkYFin369DEDBw5M3TqKQR1cX23dsx0wBjBhAkcCxkCLVwQQQAABBBBAAAEEEAgECgWMWQ4Ua9Q9HwFjUOGGAkcCxkCJVwQQQAABBBBAAAEEEAgE6gaMeQgUa9Q9XwFjUPH6AkcCxkCHVwQQQAABBBBAAAEEEAgEgoAxT4FijbrnM2AMACZPnmyGDBnij3H85Zdfgh/zigACCCCAAAIIIIAAAgj4Ak2aNPHHKA4aNMhsttlmuVKxwXK+A8bgbCtwzNvJD+rOKwIIIIAAAggggAACCBQWyHOsQMBY+LrgHQQQQAABBBBAAAEEEEAg1wJBwNg41wpUHgEEEEAAAQQQQAABBBBAoKAAAWNBGt5AAAEEEEAAAQQQQAABBPItQMCY7/NP7RFAAAEEEEAAAQQQQACBggIEjAVpeAMBBBBAAAEEEEAAAQQQyLcAAWO+zz+1RwABBBBAAAEEEEAAAQQKChAwFqThDQQQQAABBBBAAAEEEEAg3wIEjPk+/9QeAQQQQAABBBBAAAEEECgoQMBYkIY3EEAAAQQQQAABBBBAAIF8CxAw5vv8U3sEEEAAAQQQQAABBBBAoKAAAWNBGt5AAAEEEEAAAQQQQAABBPItQMCY7/NP7RFAAAEEEEAAAQQQQACBggIEjAVpeAMBBBBAAAEEEEAAAQQQyLcAAWO+zz+1RwABBBBAAAEEEEAAAQQKChAwFqThDQQQQAABBBBAAAEEEEAg3wIEjPk+/9QeAQQQQAABBBBAAAEEECgoQMBYkIY3EEAAAQQQQAABBBBAAIF8CxAw5vv8U3sEEEAAAQQQQAABBBBAoKAAAWNBGt5AAAEEEEAAAQQQQAABBPItQMCY7/NP7RFAAAEEEEAAAQQQQACBggIEjAVpeAMBBBBAAAEEEEAAAQQQyLcAAWO+zz+1RwABBBBAAAEEEEAAAQQKCixX8B3eQCDHAt99953p0qVLRQQaNWpkmjdvblZcccUlX61btzabbrrpkq8OHTqYJk2aVKR8HLQ6BSp5TZci1rdvX3PRRReVkgX7RhT44osvTLdu3SLule7N27VrZx577DHTokWLdBc0w6UbP368OfrooyPVUOdrwoQJpmXLlpH2Y2MEEIhXgIAxXk9yy4jAokWLzLRp01Jbm9VXX9306tXL9O7d2/Ts2dMPLFNbWAqWCoG0X9OFkN55551Cb/HzhAR+/vnnVP/+c6m2fp/PnDnTKHAkVUbgxx9/dLquZs2aRcBYmVPGURFYIkCX1CUU/AOB6hFQa9Edd9xhDjroINOmTRszYMAA8/3331dPBSgpAggggAACCCCAQFUI0MJYFaeJQiJQWGDevHnmkksuMddff70ZPHiwOfnkkwtvzDupEZg/f77RuYuSWrVqZZo1axZlF7ZFAAEEEEAAAQRKEiBgLImPnRFIj4BaHfv3728+/PBDM2LECNO4MR0I0nN2apdk8eLFpmPHjmb69Om13yjy3Z133mk0po+EAAIIIIAAAgiUS4A7ynJJcxwEyiRw3XXX+V1VNWaNlE6BX375JXKwmM6aUCoEEEAAAQQQyLoAAWPWzzD1y6XA2LFjzWWXXZbLulNpBBBAAAEEEEAAgfgECBjjsyQnBFIlMHToUDN58uRUlYnCIIAAAggggAACCFSXAAFjdZ0vSotAaIGffvqJCXBCa7EhAggggAACCCCAQH0CBIz1qfAzBDIiMG7cOKNFuEkIIIAAAggggAACCLgIEDC6qLEPAlUi4HmeGTNmTJWUlmIigAACCCCAAAIIpE2AZTXSdkYoT9ULrL322maLLbaIVI+FCxeamTNnmhkzZhgtj6FZNONKDzzwgDn11FPjyo58EEAAAQQQQAABBHIkQMCYo5NNVcsj0L17d6P18lyTgsVXXnnFz0PB3pw5c1yz8vebOHFiSfuzMwIS+P3vf18RiM6dO1fkuHk+aKtWrWI936NGjXLmjPO6W2GFFZzLwY4IIIBAngUIGPN89ql7KgWaNGlidtllF/9rxIgRpl+/fubuu+92LusPP/xgfvzxR9OyZUvnPNgx3wJ9+/Y1t956a74RclT7NdZYI9bz/cILL5hPP/00suCGG24YazkiF4AdEEAAAQR8AcYwciEgkGKB5s2bm9tuu80ccMABJZXym2++KWl/dkYAAQQQQAABBBDIpwABYz7PO7WuIoHlllvO3HPPPWbLLbd0LjUBozMdOyKAAAIIIIAAArkWoEtqrk8/la8WgeWXX96ccMIJzusqzps3r6xV/f77781//vOfJV/qFtu6dWvTtm1b/2uttdYyjRtn43mV6jZlyhTz0UcfGc1Ku8kmm5iOHTsaxkuV9ZIr6WCLFy/2J5sKJp7S69y5c83KK6/sX7drrrmm/7raaquZRo0alXSsNO3MtVv8bOg6+Oqrr8zXX3/tfy1YsMCsssoqZtVVVzW6HoKvtHb5X7RokT+Zmh4a6neyrm397m3Tpo3RBG36nay6kBBAAIGGBAgYG9LhPQRSJKBxja5JNwZJJgVLDz/8sPnHP/5h3nrrLTN//vwGD6dxmrph0Wyyhx9+uNl///0TCbAUuGrW2ShJwWxDN38//fSTuf322819991nJk+ebL788stlsldQMWHCBKOxYPWln3/+ub4fF/3ZZ599VnQsWIsWLXzbopnleAOdw5dfftk8+eST/tekSZOMgsZiSdetrtmePXuaPfbYw+ywww6madOmxXZzer+S167GG66//vpO5a7WnaZOnepfC6+99pr/mVaAqEAx7MO2Zs2a+Z/3rbbaynTp0sVst912Zqeddmrwd0mcVpppW2V/9tlnzeuvv+4HtwoS9ftPD7IaSiq7/kboa7311jO77bab2XPPPU27du0a2q0q3nP5HNWsmB4W8fCvpgj/zr2A/YVCQgCB/wnYJ7H6C+v0ZScIScSxlDLZloTYy2RvRLwBAwZ4m222mZNTTV/7B9k74ogjvCeeeMKzs8TGVtarrroqctkeeuiheo8vwyuvvNKzT+RD5bntttuG2q6mQxz/tsFMveUv5fpJ6pqut6AJ/tBOvuLts88+nn0gEMu5sbOJenZ8sffqq6/GXupKXruffPJJrPXZYIMNnLztpDexlqNuZtOnT/fOOOMMr0OHDk7lK/Z51fVx/PHHezaAq3vo2L5/9913vWOOOSa2a7pmnTp16uRde+21nn0AGEt5n376aSfnzz//3On49gGb1759e6djysE+DEr03DlVip0QKLOA/Sz4KRt9woLa8IpAhgXUncglqZUrziekap254oorjGYwvPjii/1WNpdy1dxHT/E1E6yeaqsl9eOPP675dsX/rTGk9sbD2JtLv9Wh4gWiAJEE1Nrbo0cP061bN/PII4/4swZHyqDAxuquOGbMGL+lsVevXn7reoFNK/Zjrt1l6dUzQMt1bLTRRsY+BDI2QF52oxh+ouvjpptuMl27dvVb7dSKGVd65513zO677+6PbdfEaJoJO+704Ycf+mv42oDf2AcYxrVnRNzlCpOf/l5qiat///vfYTZfZhsNA9GyVmotJiGAgDEEjFwFCFSJwAcffOBUUgU6cSV1w7Qtiubss882s2fPjivbWvloDUpN8HPNNdcU7UpVa8eEvhk2bJixrZ/m22+/TegIZJuUgG4a9913X/+GXV31kkyPP/642WabbfwZjWfMmJHkoULnzbVbm8o+mDcyUbdiBVnlDIDGjRvnH/fSSy8t+ffaDTfc4F/Tzz33XO0KJvSdPkd6WLbrrrv6XV0TOkxs2ep3tYJp1wBds5Pbniamd+/esZWJjBCodgECxmo/g5Q/NwLvv/++U13VqlJq0viuM8880/Tp08f5iW2UMuhp+Wmnnea3NlZqhlfdXKrOZ511Vsk3eFHqzrbxCGgsre0W7I+tjSfHcLmMHTvWP+7EiRPD7ZDAVly7y6LOmjXLf3igz3M5A8WaJdGEOeedd57/u63mz8P+23bXN0ceeaQ58cQTjXp6lDuNHz/edO7c2eg1ren//u///N4Eah11SRq/rl4I6u1CQgCBpQIEjEst+BcCqRXQ5At6Ih41aUmOU089NeputbZXd1FNSqOuW+VOmphEE4wk1ZrZUH3s2KOK1LmhMvFeOAF1w9SEI1988UW4HWLeSpMT/fa3vzWjR4+OOedw2XHt1nZS19CgO3LtdyrznR0XaAYOHBj54Oeff7656667Iu8X5w56gLf33ns7t97FWZa6eek8K9BTd12XtOKKKxr1FFDrJAkBBGoLEDDW9uA7BFInoGnR1bLn0tKm/TTrnWtS1zrdeGsG1EolO6mDf4OSxBidQnXS7Jm33HJLobf5eYoFrr76an/m3WIz9SZdBV2vhx56qBk+fHjSh6qVP9duLQ5/9lvNxOzaQ6N2bvF9p/HfL730UugMNZ7ur3/9a+jtk9xQD/DUXVMzkKYl6fOmccQar+ySVlppJX+W3FJmI3c5LvsgUC0CBIzVcqYoZy4FNAbjkEMOMRrXFzVpaQd1v3JN6v6kgLOSXeuCsqsL1EEHHVSWrmTqNta/f//g0LxWkcDzzz9f0jWfRFU19svOzppE1svkybW7DInfBfTRRx9d9o0U/OTPf/5zqFKol0e/fv1CbVuujTRxUKm9V+Iqq7rn7rfffv5SOS55al3NZ555xuy4444uu7MPArkQIGDMxWmmktUioNZErYGmP16aaEULwGtMlEsaOXKk0Zpgrkljbcp1oxumjHbJDTN48OAwm5a0zWWXXWamTZtWUh7sXH4BdQPVAw496EhTCnoIlKN7LNdu7TNvl2PwZ/es/VO371ZffXWz+eabm0033dS0bt3aaE3OUtNTTz1l3n777aLZ3HvvvWbOnDlFt2toA5VfE5bpb4J6ncSxfqhmtrZLkzR02MTf03hUPUzU30yXtNpqqxlNHqR1M0kIIFBYYLnCb/EOAgi4CGjxet1URE16SqpFJcYxbwAAHz9JREFU4HWDWWrSTHx/+MMfnLNRkHr55Zc77x/s2LhxY6M/yHpCHkcXQS3noenwtaRHUimuFtWNN97YvPnmm0kVs6z52jUo/QcZ5Tjoyiuv7F8zUY6lz86BBx4Y20y2mlJfrQ6aQCOOCVLUtVvl05hc5Z1UiuvaTap85c5X465dz5/Gf6sr6ymnnOI/uKu7NJF+p2lyFAVzasF0fVChvxdbb711gzQ33nhjg+8XelNB4qBBg/xJYPTwsWbSxEh6yKKurgr8XK4d/a3SchvqBl6JJHOdI9cWZAX+mj1Zs+aSEEAgpID95UFCAIH/CdhpxJ0X+7UfuYrua28QSjqPdkpyz960O9dBC9ZrgXst/m1vKPyy2FlWPS2irIWb7c2F165dO+f8tfB62OSy+Hlc50/1X7hwYb1f9mbTqf524qN686t5nMC8rlG1XNN2Ztq6RS/6/YgRI5w8g3NtgwPPTuzk2QkvPDtW2NP1qmRvSL2vv/7asw9QvL322suzD0BKOs7f/va3onUJNqj0tRuUI45Xu46fk5t9MOR8eDsrqmcnMXE6rl0exbPr94U+tp2IxvnaOOqooxo8zkcffeRUB9ti5kVZ8N4GjZ4+B8FnIuyrDaQ9O36wwToEb+r3f9h8a25XXz302ezbt69Tfsq7TZs2np1JNSgarwggUEDAfl5qpwLb8WMEcilQLTfX9lO85A+m7W7k2S6kJZ8vOxPfkjxr5l/s3/oDbGeFDHV8O1mCd9JJJ3l2nKXTsXRjHyZV8qbbdi0uWEQFeMU863v/zjvvLJhnsTeq5ZqOGjDa1kVv3XXXdfKU8fbbbx86ONDN+29+8xvnY+lBic59mJTWazdM2etuU4mAUUFcfZ+hYj+zrU7+w626dSj2vR4GFMu7vvd33nnnBrO2XfGd8rUtZw3mW9+btrXRs11VIx/PTjRTX3bL/CyugFEPdOxMwJHLGfivs846nh2DuUz5+AECCCwrYD83fqJLaiDBKwJVLKB1ubTQfand3dQFz7bWRJZQdydNOGKDxlD7tmrVymiMpWa10xTt9ldUqP2CjQYMGFCxdbI0m55t5fSnXm/btq1Za621jOqjLmpa6+3jjz/2u8G1b98+KC6vCQqMGjXKefmME044wb/ew47nUjdjTcBkH3aYW2+9NXKt1AVQy+No2YtKpDxdu67r8Om8uswsrd/BmsXUtkxGOrXFxgC6jH3VWoIus32q27R+t40ZMyZSHTSTdTnHAGqN3ptuuilSGYON7UMb/29VksMagmPxikCWBAgYs3Q2qUtuBbQEhKYV10LzpYzH0FiUqFOlK2AaN26cHzhFPQG2m59/8xx1jI4mipg0aZI/tijqMV23V3A4bNgwc8ABBzQYmLvcqLmWKe/7aXzaX/7yFycGPai47rrrjMbZRknNmjXzb1a1NqqWsIiaVF6Nw9UYuXKlPF67H3zwQWRercO3xx57RN5PO2hWaq3fd/PNN0faXw+aGkoa1x416QGcyuOSevTo4RQwuhzLZZ9zzz3XaB1Ll2Rbuv1gkYd5Lnrsk3eBaH8p865F/RFIqYBunO+44w6z5ZZb+gvdK4CLmjRxiFopoya19ilodE2a2dFlf03UUK507LHH+gGq1tUrtRW3XGXOw3E0M2KxFpr6HGyXNH+ij6jBYpCX9rvnnnuMArGoyXZV9mdljLqf6/Z5vXZdWhi1rEIpgbwmmYk72TG0kbPUBGOuQZXLovV6eFeONGTIEKO/Fy5JvQO07iXBoose+yBgTPkecaKNAAJlEbDjRPwpxu2YGqNuUmGT1nqMOnW7/viqW18pSbNRDh06NHI+umHX4tdJJxmqJYqUPgHNcOiSjjvuOKPumaUkXbdqKbzkkksiZ6Ny9+zZM/J+UXfI87XbuXNns+aaa0YiO/jggyNtX3fj7777ru6PSv5eLdou6ZxzzvFnplb3TXVRDZs22mgjM3v27LCb+9uVEmSHPZBm7b7wwgvDbl5rOy0noiETa6+9dq2f8w0CCIQXIGAMb8WWCFSNgLokaZyVph0/+eSTQ5Vba4JFTWpdjKPFTd08dXNrJzMIXQQ7C6l5/fXXTdeuXUPvE3XDfffd1yjwJqVTwCVgVFc9BXpxJLXeafH1qGNwXdeMi1LmvF+7Wi6inEld+ZM4r3ZCJ6dqqNfJBRdcYIYPH+4vraFxs2EDOy1tk6akcfWuLYtaO1PrLEZ9eJCm+lMWBNIgQJfUNJwFyoBAAgK6ie3fv3/orklqmYyaNN4ljqSuXF26dImclboYJZVatGjh27mOBUqqXOT7XwG7/IV5//33I3N07949tm5pHTp0MN26dYtchvfee8/MnDkz8n5hd+DaDSsVz3aa7OqII45w6h5drASuAWOQr50d2X94qNa1Qw45xNxwww3+xFzB+9Xw6hosbrXVVv74eoLFajjLlDHtAgSMaT9DlA+BEgX++Mc/GrscQ4O56OZbN7FRkoI8zTgXV9KMqVGTyzilsMfQBEJx1i/scdkunIBaDVySuqPGmVzy08Mc1/KHKTvXbhil0reZNm2a3yV50003dV48vlgp9EAibMtgQ3nZ9XXN/fff7/fk0Hg+DSc48sgj/dmqNYmYXb+1od2r8j21rq6xxhpVWXYKjUDaBOiSmrYzQnmqXmDVVVf1Z9N0qYi6EWnqfU2MoenZ7bpvRn/oS02DBg0yhx9+uGnSpEm9Wb311luRu9VpvI7Gu8SVdPMVNbnMhBj2GOpuSEqvwJQpUyIXTq3F6qoZZ9pvv/2cstNnO6nEtRuvrH7XTZ061fzrX/+q9eUy4VLUkqllUEtdjB07NuquDW6vsuvLrlfpb6dxjnaNUbPTTjv542t32GGHWALVBguR8JtnnHGGee211wr+3Uv48GSPQKYECBgzdTqpTBoE1FIW1xgpjUH8+9//7j/FdrlBDjwUfI4ePdoPGoOf1XydMWNGzW9D/9slyAudeYgNNTufxj26znZZ6BDbbLON0RTspPQKuDxIUau4umvGmVZYYQWjh0RawzRKSqpLKtdulLOwdFstb6EHZxMnTjQaHx08tNPvzrlz5y7dsAL/0gQ2jzzySKKtgFqW6eWXX/a/NC5Xk0Kp+7aWGdlzzz2d1qasAFWtQ7755ptGk+VoKQ4SAgiUJkDAWJofeyOQqIBaBNVtSONjNCuoAlG1QrokLSqtVsb6kmvAWF9e5fyZpo/XjV3cizDvuuuu5axG6o+19dZbF7x24i68Ap4wySXgSmqWROUbNWB0CXjDuHDthlEy/ji+F154wUyYMMG88cYbRt3b9YAujUnjuzUbrwLHciVN4qNWTX2pZV7jH9VT5Ve/+lW5ihDLcS666CK/V0HHjh1jyY9MEMirAAFjXs889a4qAbWgKWjUWEONT3JJ7777rr/QeH0LU1drwCgHBQ5xB4zrrbeeC3Fm99FNout1lxSKS8CVZMCo7opRkkvAGyZ/rt2GlTRR1hVXXOGPOYw6u23DOSf77tlnn+13Ia3ErM1yUg8VjYHs06ePHzhqqYpqSFpfWA9ax48fT9fUajhhlDG1Akx6k9pTQ8EQWFbg9NNPN3vvvfeyb4T8ibq31peSunmt71hx/yzKUhxhj13qzIRhj8N27gIu12xSAWPbtm0jV8Ql4A1zEK7dZZXUcqhlNtRSt8suu/jdO6spWFSN1Mo3cuRIc80118SylNGySsV/ot+16unSqVMnc8opp6S2RbZuTdSKfOWVV9b9Md8jgEAEAQLGCFhsikClBXTTcOONNzoXo9CYQ3U/qtaURMC4zjrrVCtHbsodZTHyAEWtDUkkl3XrkhoXx7Vb+wyr98T2229vDj74YL/rae13q++7U0891R9jedpppxmNn61E0u9crY2oCaQ09rEakrrTljIPQDXUkTIikKQAAWOSuuSNQAICaiVp06aNU86azKG+pAkOqjUlETDSSpP+q0HjV6Om5ZdfPuouobaPOn5RmSb1mePaXXrK9IBMs31q8pOkUxxLX4Qtox4KXHXVVf5M2gMHDjSrrLJK2F1j3e6xxx4zxxxzTKx5JpXZggUL/K6pSfy9SKrM5ItAmgQIGNN0NigLAiEFXCce0CLO9d1oV/NaVXHfAGiiobXWWivkmWCzSgm0bt068qG//vrryPuE2cEl3yQ+c1y7S8+WWpO15EmhXhVLt3T/l4JEBaQDBgww77//vtHEYuVMuoaGDBlivvjiC/Pwww+b/v37G60JWc6kcY233HJLOQ/pfCwtsaFAm4QAAtEFmPQmuhl7IFBxAQWMzz//vFM5NE183RnjXG6+dfA0THwQ9zIJK664IpMjOF1Z5d3JJeD66quvEimkS8Do+plrqAJcu0t1LrjgAhPXOq3qcqxAbJNNNvG/9O/g+5q/f7T0RSWSuqZqrUZ9KWkt36effto899xz5pVXXvEDyiTLpWUrNJt3Ui34dcuu+moZFJekFlk56VySEEAgvAABY3grtkQgNQKlTN6hZSjqBowuN99bbLGF0cyrJAQqIeAScLkEdmHq5hKIunzmwpSFbYzfqljqJCc777yz30KpZUr0uy7utV6TPE/t2rUz/fr18790HD0kVOAYrLM4efJkE+ekP5rASS2cBx10UJLV8vPu3bu3ue+++/y1IceNGxf5eOphc+yxxxrNlltN5zRyRdkBgZgF6JIaMyjZIVAOAZcxU0G56lvH0eXm+8svvwyy5BWBsgu4BFyzZs0yP/zwQ6xlnTNnjtFX1OTymYt6jLxuf9dddzkHRJpF9cUXX/S/NCv1VlttFTqwWLRoUSrJ27dvb/r27WtuuOEGo+Vf9Lv7uuuuMz179jRNmzaNpcyaPTXppLUgH3zwQdOsWTOj5UVcWzS1xIZmmyUhgEB4AQLG8FZsiUBqBKKu+Vaz4PW1TuoJetT03Xff+WsgRt2P7RGIQ6BuK3nYPLW8QpzJNT/X8sdZ9qzmdffddztVTV0V1dVfrYsuSQ8kqiHpb8CJJ57or8ur5Wnk1bVr15KKnuRYURXsqKOOMloWKphcSMMhtDala1KX5Y8//th1d/ZDIHcCBIy5O+VUuNoF1Lr4zDPPOFejvmn3N9hgA9OhQ4fIeb7wwguR92EHBOIQ6N69u782XdS84p6gwyU/dYXbbbfdohad7UMIqPVs6tSpIbasvYkemqmVrJRuikmtrVm7pPF+p/GZhx9+uNGEMA899JBxWSJGJUqyx8kJJ5xgbrvttmXGlivoc/m7pfIGXVPj7JqrfEkIZFWAMYxZPbPUK7MCZ511lqmvW2mYCutmqNCSHLvvvnvkNR41sYLWNyOVRyCtXd7KU/vaR1GXVHUXnDhxYu03inynsVxajy2O2SQnTZrk32gXOeQyb2+99dZmtdVWW+bn/KB0AddxqloeotR1DT/66KPSK1AjBwVw119/fY2fFP+nWuFcry2tq6gW1i5dupiov2sULP/yyy/LBHXFS1x8C01UozWI6yZNODR8+HDTq1evum+F+l5jOrW/1rYkIYBAwwK0MDbsw7sIpEpAiw/feuutzmXSchGaer++pBabqEk3J+qaGlc68MAD/RsD3RyE+Ro5cmRch66KfLQIOWmpQI8ePZZ+E+FfpXyGah7GpXVR+7uWu+ax+Xf9Aq4Bo4KkUpKWtlArXZzpiSeeME899VSkL01wU0rSwwyNd4yamjdvXvBvS9S8omy/1157Gf3dcE3nnXdeokuvuJaL/RBImwABY9rOCOVBoB4BrSmmNb6GDh1az7vhf9RQUKgucsH4kLA5/vjjj2bYsGFhN29wO7X8jBkzpsFtar6pwFfrrOUpuczGmWWf3/3ud07VGzVqVMnjb7/55htz++23Ox2fgNGJLdROWmvWJZW6zILWI4y7e+PGG28cuSpvvfVW5H3q7uCyzm99Qx3q5pvU91dffbXRkjIuSX/DjjvuuNjPnUtZ2AeBNAsQMKb57FC23AtoUL4G9q+77rrmnHPOKdlDa2UVSurip7EsUZMC2WeffTbqbrW2V1cuTWoQJe25556mkjcpUcoa17bqQkVaKqAlD1zWAtVEH+pKHbXbXXBkdQnXEgIuY9Z0M66ZOEnJCLgGbaWu2ZjELKEuAePNN99cMqxLTwb9japU0rGHDBnifHjNipu33irOWOyYWwHGMOb21FPxpATeeOMN079/f6fsNRBfrUjBl8sNaaEDt23b1micYkNJ3XM0Jf3ixYsb2qzWexq3ounONU25ujLVN9ak1g51vlE3Ls1OGLVr6/HHH18np+x/+/bbb/trkMmbZPwJSgYMGODUhU43iXqAom6lLVu2DM05d+5cfx03tYi7JI3HKmViFZdj5mkfdbt3Sa+//rrp1q2by67+EhVvvvmm074N7eQSMOrvjz4TF198cUNZF3xPvVlcZpmtZMCoypxyyil+i7/r2sDnnnuuPxZSE8CREECgAQH7VI6EAAL/E7Ddmjz7ccnU15/+9KdQ59e2nDjXe8cdd/TeeeedUMf59NNPvZNOOsmza2pFPt7666/v2Vaeose56qqrIudtZwksmm+pG9iWLc8G1pHLFlyTdnIK74477vDsbLm+t+2G5tkJiDy7NplnA/56i1fKNW0fBNSbZxp+KEvbndDZslOnTp4NxENV5dVXX/XsTbzzsWxrqGcfsIQ6Vlqv3VCFr7ORvQl3Mttwww3r5FT8W/sAyulYNuDxpk+fXvwAdbZ4/PHHPbseoNMx9Xlu1apVnRyXfrtw4ULPTlLmlPe11167NKOQ/7LdMz39bgl+z0R5tT1Nih5Fv6Oi5Bls+/nnnxfNWxvo81nK71XbY8GzD0tDHYuNEMiLgP0c1k55qTj1RCCMQCk31/aT5fRHMcn91ltvPW/27Nlhqu7ZWSdL+qNrxxZ6dhIC76KLLvLsk2rPPvH27Ppknp123bNdKj077suzLTueHS/p7DR69OhQdUnzTffqq6/uXP+GrhW7GHe9NqVc02kOGFVZBc8NmYR5z0564tmFzf3rVdfqggULPN2o2pYnTzffdkbWko+hz0PYlOZrN2wdgu3KGTAq6AtzvuvbRg8e7KQ5QbEbfLUtcd6FF17o2dZi5+OpDApwGnqIoGPUV9ZiP1O+dvZPTw/mwiQ7aY+3/fbbOx2radOmnn6/FEtJB4w6vl2Cw6kOgaceupEQQGCpgP1s1E5L3+JfCCBQys21/WSV9Acr7v1146CWqCjJdk1NVR1qmuy0006hq5Lmm247li0R4zwGjLrhVr1rXidp+7cdc9tgYFD3ok7ztVu3rMW+L2fAqLKU0uJsZ/v0Dj30UM/OUOrZoQGeWrCV1KNBQZXtiuwHYnbMd2zXm11b1z9Gff9TAKuAzPV61gM8O17XGzt2rGe7ay55cKgHInaMvGdnuvb22GMPT9u5HkMPCMOkcgSMdmiD17p1a+e62MlzQgfZYerMNghUu4D9veAnJr0JJHhFIKMCJ598ctGxi3WrrtlY0zgxh2ZG1Yx4WUiVHvOTBcOgDhoTqCVe0jr+yHat9MvH2MXgjCX7esABBzgfwAZS5t577zWaVEvjvm2wZmy3UWO7nfqTj/32t781tsXZacKjQoWyvT8KveWvm+syGVmQocaYawbX/fff32y55ZZmlVVWMVq/UMtgbLTRRv5EZ08++aS/hmKwT9TXE088MeouiW2vNSgvv/xy5/x/+OEH069fP2ZNdRZkx6wKEDBm9cxSLwSsQJ8+fZyWvVBgppsmO34mVY621cVonbAspL333jsL1UhNHXSjaFtRIk1gU47Ca0IdlUs36qTyCNgWwsiTbxUqmW0dMAoi9JpUmjNnToNZa0IxBXdxJQXFcSUtSVFsMrW4jhU2n6OPPrqkB57PPfecufHGG8Meju0QyIUAAWMuTjOVzKPAaaedZjTVu56MuyQFiw888EBqbnT1FFsz4WUlKZhXYE6KT0AtKLfddpvfKhRfru456bNnx1eaX//61+6ZsGdkAV0HCmSqJRULGO1EXP5DhxVWWCFVVZLziBEjUlWmoDB2LGJJvwe0nNVnn30WZMcrArkXIGDM/SUAQNYE1N1ILXH6suMXS6qenfXUaNkLdamrZFJ3quHDh1eyCLEfe8011zR2gobY8817hlpf8fnnnzfyrWTS8g7jxo0zdnxXJYuR22Nfeumlxk4sVRX1DxOYbL755v6SR/r9noa09tpr+11d1bU1jUnrnZ555pnORdPyOeqaSkIAgf8KEDByJSCQIYHDDjvMTJkyxah1Ma6khdEnTJhg7GQzcWUZOh8FvHaWQPPggw8aO6tq6P2qZcNhw4YZu6xDtRS3asqpcWZaF69z584VKfM222xj/vnPf5oddtihIsfnoMYPFh999FGz0korlYVDvQX0ebaz7UY+nl0WJ9Q+++23n9H6n+3atQu1fVIbbbfddv717bJOZFJlqi9frXnavn37+t4K9TM7WZy56aabQm3LRghkXYCAMetnmPplXkCTMmg83Pjx4/2JNewSGrHXWU/qn332WWOXyijbDdg666xjHnroIf+YpbaUxg4SU4ZqLdCEEy43mTEVIbPZ6KZaN9dqZShXq4zGK9r1To1dPsafICWzuFVSsa5du/qfL3XpTDJp/Kw+x2eccYbTWEM9kAubNIZbAWalxkAfc8wx5sUXX/QnBApb5kptp8+jJigqJZ111lnGLq9TShbsi0AmBAgYM3EaqUTeBDTbolpRNE7DTrtuHnnkkcRbMzQeS619n3zyidEf0aRuwjU5iLqTTZ061fTu3Tvzp1azpb700ktm8ODBTjebmQcqoYK6YbziiivMtGnT/PGvzZo1KyG3wrvqs6DA1K55Zy677DJ/BsrCW/NOOQXs2oLmww8/NGqdiztp9tTTTz/d2OUqlkz8ouNFTer2P2nSpNC72SU9/N/5dqF606NHj9D7lbLhXnvt5T8IGTVqVFVd3/vss09J5/777783xx9/fCl07ItAtgSqfZ0Qyo9AnAJ2VjzPTjDgvJaT/e1Q8r72ZsSzrYXeFlts4e28887escce69kJBjx7k+DNmzcvzuo65aXFzQcNGuRtu+22JS9ercWvu3Xr5i+c3tCaZFELqjXGop4L2xoR9TCxbm9bD7xzzjnH69ChQ+Syq66F1mEs5Zq2wVCsdaxUZralwDv//PM920rjL5ge9dqoub2uWds1z7MPUUItWh61ztV47Raqo23pc7qWbZfeQlk6/fzxxx/3evXqVdKag7oG1l9/fc8u3bBkTcOahZk8ebJTXUeOHFkzm0j/ti2Unn2I59nhA07Hrnld1/y3fgfZZZm89957L1J5Cm2sdSBr5h/23/rclpKmT59e8t/zuAxKqQf7IlAJAfs59dOSGTFUiOCHvCKAAAJRBL799luj8R7qlmVvmMzMmTPNjBkzjA1sl8lG3UtXXXVVf+ZIjTFTF6vddtvNqAsqqbaAXGfNmuV/2QWp/VcbUPtrpqnFN/hSC5ddrNqoO7K+kmpJq1266v5O16i6WdvFxP0WKFnrZ1pCoW7SODi16shYM0OqVad79+7+dVx3W75Pv8BXX33ld3fXOFeNNdXvrEWLFi1TcI2bVndT9QJQ91aNSdVXWtf7VAXUA0STPqnFUvXSmHYbMNVbP/VU0eRQWm9Sk9gEX/odsuuuu9LjYZkrgh8gkD8Be8/mx4oEjPk799QYgbIJzJ8/f0ngqCnh1d1U3bh0o0JCII0CWqNOgaNmSdTYNwWKBOBpPFPxlkkPCmbPnu2fd51vjdvWg4KsjJ/Wda1rWl/6tx7aKVhkaZ94ryNyQyBrAgSMWTuj1AcBBBBAAAEEEEAAAQQQiEkgCBh5zB8TKNkggAACCCCAAAIIIIAAAlkTIGDM2hmlPggggAACCCCAAAIIIIBATAIEjDFBkg0CCCCAAAIIIIAAAgggkDUBAsasnVHqgwACCCCAAAIIIIAAAgjEJEDAGBMk2SCAAAIIIIAAAggggAACWRMgYMzaGaU+CCCAAAIIIIAAAggggEBMAgSMMUGSDQIIIIAAAggggAACCCCQNQECxqydUeqDAAIIIIAAAggggAACCMQkQMAYEyTZIIAAAggggAACCCCAAAJZEyBgzNoZpT4IIIAAAggggAACCCCAQEwCBIwxQZINAggggAACCCCAAAIIIJA1AQLGrJ1R6oMAAggggAACCCCAAAIIxCRAwBgTJNkggAACCCCAAAIIIIAAAlkTIGDM2hmlPggggAACCCCAAAIIIIBATAIEjDFBkg0CCCCAAAIIIIAAAgggkDUBAsasnVHqgwACCCCAAAIIIIAAAgjEJEDAGBMk2SCAAAIIIIAAAggggAACWRMgYMzaGaU+CCCAAAIIIIAAAggggEBMAgSMMUGSDQIIIIAAAggggAACCCCQNQECxqydUeqDAAIIIIAAAggggAACCMQkQMAYEyTZIIAAAggggAACCCCAAAJZEyBgzNoZpT4IIIAAAggggAACCCCAQEwCBIwxQZINAggggAACCCCAAAIIIJA1AQLGrJ1R6oMAAggggAACCCCAAAIIxCRAwBgTJNkggAACCCCAAAIIIIAAAlkTIGDM2hmlPggggAACCCCAAAIIIIBATAIEjDFBkg0CCCCAAAIIIIAAAgggkDUBAsasnVHqgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJRd4P8BKj7Yn5c3MfkAAAAASUVORK5CYII=)"],"metadata":{"id":"ZLy-BBzWQeEg"}},{"cell_type":"markdown","source":["## 토큰 분류를 위한 사용자 정의 모델 만들기"],"metadata":{"id":"2cSDj1_vTZMP"}},{"cell_type":"code","source":["import torch.nn as nn\n","from transformers import XLMRobertaConfig\n","from transformers.modeling_outputs import TokenClassifierOutput\n","from transformers.models.roberta.modeling_roberta import RobertaModel\n","from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel\n","\n","class XLMRobertaForTokenClassification(RobertaPreTrainedModel):\n","  config_class = XLMRobertaConfig\n","\n","  def __init__(self, config):\n","    super().__init__(config)\n","    self.num_labels = config.num_labels\n","    # 모델 바디를 로드\n","    self.roberta = RobertaModel(config, add_pooling_layer=False)\n","    # 토큰 분류 헤드를 준비\n","    self.dropout = nn.Dropout(config.hidden_dropout_prob)\n","    self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n","    # 가중치를 로드하고 초기화\n","    self.init_weights()\n","\n","  def forward(self, input_ids=None, attention_mask=None, token_type_ids=None, labels=None, **kwargs):\n","    # 모델 바디를 사용해 인코더 표현을 얻음\n","    outputs = self.roberta(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, **kwargs)\n","    # 인코더 표현을 헤드에 통과\n","    sequence_output = self.dropout(outputs[0])\n","    logits = self.classifier(sequence_output)\n","    # 손실을 계산\n","    loss = None\n","    if labels is not None:\n","      loss_fct = nn.CrossEntropyLoss()\n","      loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n","    # 모델 출력 객체를 반환\n","    return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)"],"metadata":{"id":"dUoKNNdjTWk3","executionInfo":{"status":"ok","timestamp":1675821080583,"user_tz":-540,"elapsed":1798,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":["- config 클래스는 새로운 모델을 초기화할 때 표준 XLM-R 설정을 사용하도록 도와줌\n","- super() 메서드로 RobertaPreTrainedModel 클래스의 초기화 함수를 호출\n","  - 사전 훈련된 가중치의 초기화나 로딩을 처리\n","- add_pooling_layer=False로 지정하여 [CLS] 토큰에 해당하는 은닉 상태 외에 모든 은닉 상태가 반환되도록 함\n","  - add_pooling_layer의 기본값은 True로 첫 번째 토큰의 은닉 상태만 밀집 층과 활성화 함수에 통과시켜 출력\n","- RobertaPreTrainedModel 클래스에서 상속된 init_weights() 메서드를 호출해 가중치를 초기화\n","  - 모델 바디에 사전훈련된 가중치가 로드되고 토큰 분류 헤드의 가중치가 랜덤하게 초기화 될 것"],"metadata":{"id":"9CHPVLJsjJp6"}},{"cell_type":"code","source":["# [tags를 정의했던 위 코드]\n","# tags = panx_ch[\"de\"][\"train\"].features[\"ner_tags\"].feature\n","# ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'], id=None)\n","\n","index2tag = {idx: tag for idx, tag in enumerate(tags.names)}\n","tag2index = {tag: idx for idx, tag in enumerate(tags.names)}"],"metadata":{"id":"Bic1OxxBkJGU","executionInfo":{"status":"ok","timestamp":1675821080583,"user_tz":-540,"elapsed":5,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoConfig\n","\n","xlmr_config = AutoConfig.from_pretrained(xlmr_model_name, num_labels=tags.num_classes, id2label=index2tag, label2id=tag2index)"],"metadata":{"id":"kU7yY0YGkWAX","executionInfo":{"status":"ok","timestamp":1675821081789,"user_tz":-540,"elapsed":1210,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":["AutoConfig 클래스는 모델 구조의 청사진을 가짐. AutoModel.from_pretrained(model_ckpt)로 모델을 로드할 때 모델에 연관된 설정 파일이 자동으로 다운로드 됨. but, 클래스 개수나 레이블 이름 등을 수정하고자 한다면 커스터마이징 하려는 매개변수로 이 설정 파일을 먼저 로드해야 함"],"metadata":{"id":"oH2i5fvHleg-"}},{"cell_type":"code","source":["# from_pretrained() 함수를 사용해 모델 가중치를 로드\n","import torch\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","xlmr_model = (XLMRobertaForTokenClassification.from_pretrained(xlmr_model_name, config=xlmr_config).to(device))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":156,"referenced_widgets":["ee35beb51277432a80c6c507ea941e81","9977e174493d4522b4ad8643c0a7b534","1ba89974c87e4c6a818f6a85c03b55c8","96861e3ec0124949a8dfbbd9a3f8ae4d","52f2ce5e3e4246b8a503b21ad531e9ed","5d26e3b64c4444f3a772c33f95906e7e","42c224b56c9c4b548abd21c97122ad41","e8c85c704de8418eb3363b8ad9a6146c","e8eb5682d2334e1d8833a209f2dbd826","464ca3b6b1694d93becbabba2bc5f2e6","11c5f0454f774718a6d4efb4ba63cae7"]},"id":"g5F0vtmhl9hX","executionInfo":{"status":"ok","timestamp":1675821105942,"user_tz":-540,"elapsed":23379,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"cb75f3fd-ab52-4ab9-b170-291b0cfe1aa3"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/1.12G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ee35beb51277432a80c6c507ea941e81"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaForTokenClassification: ['lm_head.layer_norm.weight', 'roberta.pooler.dense.bias', 'lm_head.layer_norm.bias', 'roberta.pooler.dense.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.dense.bias', 'lm_head.decoder.weight']\n","- This IS expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing XLMRobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of XLMRobertaForTokenClassification were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['classifier.weight', 'roberta.embeddings.position_ids', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"markdown","source":["from_pretrained() 함수를 사용해 모델 가중치를 로드. 모델 클래스에 사전 훈련된 가중치를 로드하는 코드는 없지만 RobertaPreTrainedModel을 사용했으니 로드 가능"],"metadata":{"id":"EqiB00h8m8pK"}},{"cell_type":"code","source":["# 토크나이저와 모델을 바르게 초기화했는지 확인\n","input_ids = xlmr_tokenizer.encode(text, return_tensors=\"pt\")\n","print(input_ids)\n","pd.DataFrame([xlmr_tokens, input_ids[0].numpy()], index=[\"Tokens\", \"Input IDs\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"ywUyGhtVnfNX","executionInfo":{"status":"ok","timestamp":1675821105943,"user_tz":-540,"elapsed":27,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"a3238c2f-a11a-4307-deab-4205a539a027"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[    0, 21763, 37456, 15555,  5161,     7, 10086,  5753,    38,     2]])\n"]},{"output_type":"execute_result","data":{"text/plain":["             0      1      2      3      4  5      6      7   8     9\n","Tokens     <s>  ▁Jack  ▁Spar    row  ▁love  s   ▁Net  ▁York   !  </s>\n","Input IDs    0  21763  37456  15555   5161  7  10086   5753  38     2"],"text/html":["\n","  <div id=\"df-2b945e09-e171-4d8f-be85-2c2f93fb0306\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁Jack</td>\n","      <td>▁Spar</td>\n","      <td>row</td>\n","      <td>▁love</td>\n","      <td>s</td>\n","      <td>▁Net</td>\n","      <td>▁York</td>\n","      <td>!</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>Input IDs</th>\n","      <td>0</td>\n","      <td>21763</td>\n","      <td>37456</td>\n","      <td>15555</td>\n","      <td>5161</td>\n","      <td>7</td>\n","      <td>10086</td>\n","      <td>5753</td>\n","      <td>38</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b945e09-e171-4d8f-be85-2c2f93fb0306')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-2b945e09-e171-4d8f-be85-2c2f93fb0306 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-2b945e09-e171-4d8f-be85-2c2f93fb0306');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["# 모델에 입력 ID를 전달하고 argmax 함수로 토큰마다 확률이 가장 높은 클래스를 선택해 예측\n","outputs = xlmr_model(input_ids.to(device)).logits\n","predictions = torch.argmax(outputs, dim=-1)\n","print(f\"시퀀스에 있는 토큰 개수: {len(xlmr_tokens)}\")\n","print(f\"출력 크기: {outputs.shape}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vlbSyjXyoJTG","executionInfo":{"status":"ok","timestamp":1675821109156,"user_tz":-540,"elapsed":3235,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"df541869-115e-4290-ee75-45c55501a5d8"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["시퀀스에 있는 토큰 개수: 10\n","출력 크기: torch.Size([1, 10, 7])\n"]}]},{"cell_type":"markdown","source":["로짓의 크기는 [batch_size, num_tokens, num_tags] 이고 이는 각 토큰에는 일곱 개의 NER 태그와 로짓이 주어졌다는 의미."],"metadata":{"id":"_PU1GEl7ncS2"}},{"cell_type":"code","source":["print(predictions[0])\n","preds = [tags.names[p] for p in predictions[0].cpu().numpy()]\n","pd.DataFrame([xlmr_tokens, preds], index=[\"Tokens\", \"Tags\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":129},"id":"5ZgZTLSOoxQ3","executionInfo":{"status":"ok","timestamp":1675821109156,"user_tz":-540,"elapsed":36,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"d7891b06-b22f-4248-9c7a-406ecfa12a69"},"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([5, 1, 1, 1, 5, 1, 3, 3, 1, 5], device='cuda:0')\n"]},{"output_type":"execute_result","data":{"text/plain":["            0      1      2      3      4      5      6      7      8      9\n","Tokens    <s>  ▁Jack  ▁Spar    row  ▁love      s   ▁Net  ▁York      !   </s>\n","Tags    B-LOC  B-PER  B-PER  B-PER  B-LOC  B-PER  B-ORG  B-ORG  B-PER  B-LOC"],"text/html":["\n","  <div id=\"df-1ca4f218-e22a-49cd-b30e-b46e248b4d8b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁Jack</td>\n","      <td>▁Spar</td>\n","      <td>row</td>\n","      <td>▁love</td>\n","      <td>s</td>\n","      <td>▁Net</td>\n","      <td>▁York</td>\n","      <td>!</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>Tags</th>\n","      <td>B-LOC</td>\n","      <td>B-PER</td>\n","      <td>B-PER</td>\n","      <td>B-PER</td>\n","      <td>B-LOC</td>\n","      <td>B-PER</td>\n","      <td>B-ORG</td>\n","      <td>B-ORG</td>\n","      <td>B-PER</td>\n","      <td>B-LOC</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ca4f218-e22a-49cd-b30e-b46e248b4d8b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-1ca4f218-e22a-49cd-b30e-b46e248b4d8b button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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return_tensors=\"pt\").input_ids.to(device)\n","  # 가능한 일곱 개의 클래스에 대한 로짓을 출력\n","  outputs = model(input_ids)[0]\n","  # argmax 함수로 토큰마다 가장 가능성이 높은 클래스를 선택\n","  predictions = torch.argmax(outputs, dim=2)\n","  # 데이터프레임으로 변환\n","  preds = [tags.names[p] for p in predictions[0].cpu().numpy()]\n","  return pd.DataFrame([tokens, preds], index=[\"Tokens\", \"Tags\"])"],"metadata":{"id":"RVr30Mhop-PV","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":31,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":["# NER 작업을 위해 텍스트 토큰화\n","샘플을 인코딩하고 처리하는 토크나이저와 모델을 준비했으므로 다음 단계로 미세 튜닝을 위해 XLM-R 모델에 전달할 전체 데이터셋을 토큰화. 데이터셋은 map() 연산으로 Dataset 객체를 빠르게 토큰화"],"metadata":{"id":"4OcVZdPvp6Zv"}},{"cell_type":"code","source":["# [de_example 정의했던 코드]\n","# de_example = panx_de[\"train\"][0]\n","# pd.DataFrame([de_example[\"tokens\"], de_example[\"ner_tags_str\"]], ['Tokens', 'Tags'])\n","\n","words, labels = de_example[\"tokens\"], de_example[\"ner_tags\"]"],"metadata":{"id":"BauOi1Cvsk6n","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":30,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["# 각 단어를 토큰화, 토크나이저에 is_split_into_words 매개변수를 사용해 입력 문장이 이미 단어로 나눠졌다는 사실을 전달\n","\n","tokenized_input = xlmr_tokenizer(de_example[\"tokens\"], is_split_into_words=True)\n","print(tokenized_input)\n","tokens = xlmr_tokenizer.convert_ids_to_tokens(tokenized_input[\"input_ids\"])\n","pd.DataFrame([tokens], index=[\"Tokens\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":147},"id":"rYXQzYcqtBGA","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":30,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"9efee2f0-01ab-4924-9b3a-2a718a581cb6"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': [0, 70101, 176581, 19, 142, 122, 2290, 708, 1505, 18363, 18, 23, 122, 127474, 15439, 13787, 14, 15263, 18917, 663, 6947, 19, 6, 5, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"]},{"output_type":"execute_result","data":{"text/plain":["         0       1           2  3    4     5     6   7    8      9   ...   15  \\\n","Tokens  <s>  ▁2.000  ▁Einwohner  n  ▁an  ▁der  ▁Dan  zi  ger  ▁Buch  ...  ▁Wo   \n","\n","       16   17      18   19    20 21 22 23    24  \n","Tokens  i  wod  schaft  ▁Po  mmer  n  ▁  .  </s>  \n","\n","[1 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-daa72771-c98f-40b5-8d70-99b82a849472\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>...</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁2.000</td>\n","      <td>▁Einwohner</td>\n","      <td>n</td>\n","      <td>▁an</td>\n","      <td>▁der</td>\n","      <td>▁Dan</td>\n","      <td>zi</td>\n","      <td>ger</td>\n","      <td>▁Buch</td>\n","      <td>...</td>\n","      <td>▁Wo</td>\n","      <td>i</td>\n","      <td>wod</td>\n","      <td>schaft</td>\n","      <td>▁Po</td>\n","      <td>mmer</td>\n","      <td>n</td>\n","      <td>▁</td>\n","      <td>.</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1 rows × 25 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-daa72771-c98f-40b5-8d70-99b82a849472')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-daa72771-c98f-40b5-8d70-99b82a849472');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["'Einwohnern'을 두 개의 부분 단어 '_Einwohner'와 'n'으로 나눔. '_Einwohner'만 B-LOC 레이블에 연결해야한다는 규칙을 따라야 하므로 첫 번째 부분단어 이후의 부분단어는 마스킹 <br>\n","이 작업에 도움이되는 **word_idx()** 함수가 존재"],"metadata":{"id":"tlkzUfpsuhnQ"}},{"cell_type":"code","source":["word_ids = tokenized_input.word_ids()\n","pd.DataFrame([tokens, word_ids], index=[\"Tokens\", \"Word IDs\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":141},"id":"qRM2SXAAu8L-","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":27,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"fa1f0f28-c7dc-4ed9-bbbb-53a9ac316bae"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            0       1           2  3    4     5     6   7    8      9   ...  \\\n","Tokens     <s>  ▁2.000  ▁Einwohner  n  ▁an  ▁der  ▁Dan  zi  ger  ▁Buch  ...   \n","Word IDs  None       0           1  1    2     3     4   4    4      5  ...   \n","\n","           15 16   17      18   19    20  21  22  23    24  \n","Tokens    ▁Wo  i  wod  schaft  ▁Po  mmer   n   ▁   .  </s>  \n","Word IDs    9  9    9       9   10    10  10  11  11  None  \n","\n","[2 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-ae702f10-1d79-4394-8bfb-1261becedeaa\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>...</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁2.000</td>\n","      <td>▁Einwohner</td>\n","      <td>n</td>\n","      <td>▁an</td>\n","      <td>▁der</td>\n","      <td>▁Dan</td>\n","      <td>zi</td>\n","      <td>ger</td>\n","      <td>▁Buch</td>\n","      <td>...</td>\n","      <td>▁Wo</td>\n","      <td>i</td>\n","      <td>wod</td>\n","      <td>schaft</td>\n","      <td>▁Po</td>\n","      <td>mmer</td>\n","      <td>n</td>\n","      <td>▁</td>\n","      <td>.</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>Word IDs</th>\n","      <td>None</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>...</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>None</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 25 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae702f10-1d79-4394-8bfb-1261becedeaa')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-ae702f10-1d79-4394-8bfb-1261becedeaa');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":27}]},{"cell_type":"markdown","source":["word_ids는 각 부분단어를 words 리스트에 있는 해당 단어의 인덱스에 매핑. <s>와 </s> 같은 특수 토큰과 부분단어의 레이블을 -100으로 설정해 훈련하는 동안 마스킹 처리"],"metadata":{"id":"PJLzO46q1xyj"}},{"cell_type":"code","source":["previous_word_idx = None\n","label_ids = []\n","\n","for word_idx in word_ids:\n","  if word_idx is None or word_idx == previous_word_idx:\n","    label_ids.append(-100)\n","  elif word_idx != previous_word_idx:\n","    label_ids.append(labels[word_idx])\n","  previous_word_idx = word_idx\n","\n","labels = [index2tag[l] if l != -100 else \"IGN\" for l in label_ids]\n","index = [\"Tokens\", \"Word IDs\", \"Label IDs\", \"Labels\"]\n","\n","pd.DataFrame([tokens, word_ids, label_ids, labels], index=index)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"7-zfZERZ1wV3","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":26,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"1eddd254-07af-4424-ff1c-1d5a19654005"},"execution_count":28,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             0       1           2     3    4     5      6     7     8   \\\n","Tokens      <s>  ▁2.000  ▁Einwohner     n  ▁an  ▁der   ▁Dan    zi   ger   \n","Word IDs   None       0           1     1    2     3      4     4     4   \n","Label IDs  -100       0           0  -100    0     0      5  -100  -100   \n","Labels      IGN       O           O   IGN    O     O  B-LOC   IGN   IGN   \n","\n","              9   ...     15    16    17      18     19    20    21  22    23  \\\n","Tokens     ▁Buch  ...    ▁Wo     i   wod  schaft    ▁Po  mmer     n   ▁     .   \n","Word IDs       5  ...      9     9     9       9     10    10    10  11    11   \n","Label IDs      6  ...      5  -100  -100    -100      6  -100  -100   0  -100   \n","Labels     I-LOC  ...  B-LOC   IGN   IGN     IGN  I-LOC   IGN   IGN   O   IGN   \n","\n","             24  \n","Tokens     </s>  \n","Word IDs   None  \n","Label IDs  -100  \n","Labels      IGN  \n","\n","[4 rows x 25 columns]"],"text/html":["\n","  <div id=\"df-ea973f6c-fa75-46b0-85d4-baf08d4ad2a6\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>...</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","      <th>18</th>\n","      <th>19</th>\n","      <th>20</th>\n","      <th>21</th>\n","      <th>22</th>\n","      <th>23</th>\n","      <th>24</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁2.000</td>\n","      <td>▁Einwohner</td>\n","      <td>n</td>\n","      <td>▁an</td>\n","      <td>▁der</td>\n","      <td>▁Dan</td>\n","      <td>zi</td>\n","      <td>ger</td>\n","      <td>▁Buch</td>\n","      <td>...</td>\n","      <td>▁Wo</td>\n","      <td>i</td>\n","      <td>wod</td>\n","      <td>schaft</td>\n","      <td>▁Po</td>\n","      <td>mmer</td>\n","      <td>n</td>\n","      <td>▁</td>\n","      <td>.</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>Word IDs</th>\n","      <td>None</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>...</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>9</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>10</td>\n","      <td>11</td>\n","      <td>11</td>\n","      <td>None</td>\n","    </tr>\n","    <tr>\n","      <th>Label IDs</th>\n","      <td>-100</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>-100</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>5</td>\n","      <td>-100</td>\n","      <td>-100</td>\n","      <td>6</td>\n","      <td>...</td>\n","      <td>5</td>\n","      <td>-100</td>\n","      <td>-100</td>\n","      <td>-100</td>\n","      <td>6</td>\n","      <td>-100</td>\n","      <td>-100</td>\n","      <td>0</td>\n","      <td>-100</td>\n","      <td>-100</td>\n","    </tr>\n","    <tr>\n","      <th>Labels</th>\n","      <td>IGN</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>IGN</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-LOC</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>I-LOC</td>\n","      <td>...</td>\n","      <td>B-LOC</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>I-LOC</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>O</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>4 rows × 25 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea973f6c-fa75-46b0-85d4-baf08d4ad2a6')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 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is_split_into_words=True)\n","  \n","  labels = []\n","  for idx, label in enumerate(examples[\"ner_tags\"]):\n","    word_ids = tokenized_inputs.word_ids(batch_index=idx)\n","    previous_word_idx = None\n","    label_ids = []\n","    for word_idx in word_ids:\n","      if word_idx is None or word_idx == previous_word_idx:\n","        label_ids.append(-100)\n","      else:\n","        label_ids.append(label[word_idx])\n","      previous_word_idx = word_idx\n","    labels.append(label_ids)\n","  tokenized_inputs[\"labels\"] = labels\n","  return tokenized_inputs"],"metadata":{"id":"bmWsLeKjV-z2","executionInfo":{"status":"ok","timestamp":1675821109157,"user_tz":-540,"elapsed":25,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["def encode_panx_dataset(corpus):\n","  return corpus.map(tokenize_and_align_labels, batched=True, remove_columns=['langs', 'ner_tags', 'tokens'])"],"metadata":{"id":"1uvxuo0WXLfZ","executionInfo":{"status":"ok","timestamp":1675821109158,"user_tz":-540,"elapsed":26,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":30,"outputs":[]},{"cell_type":"code","source":["panx_de_encoded = encode_panx_dataset(panx_ch[\"de\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["0bf41f331c6347ce8234765a9fcafec9","2692a1f9e28f4c64a1a4fd2550891c13","4148c5ec326741988d19b1d8d0267a0b","df6cd3aa244640d2a78b48112e80bccb","8de43e7ba0a642c3adf25b2638faa5d5","c382f6836bbd400b858ea39956e2e5db","48aa12534f9843e3bbf5921c28653b58","d4c1121465e2434a8892e778a06942b3","1ed3f14248cd455b91a76bd2aa11889f","a9b1f253f089466d96411c9e7562df44","8a43e1fa5c354852b8218c5e8db69fb6","1c0d5ec20f0d42c6ad7fa2885c9be26c","7dece95e5cff46e282161be507beead5","b32512787c384ba682b283f5208eae16","92f6ba0adb9b44d5b4162c4ba62a02b4","e04c275119b64865bfd35e99aabee6d1","afe3bdd6130d47fba48a2841ef258e04","4ac033289560420ab03259fb6ffd20b1","10e064dff423490ca81202ace809a920","ab51d2a5d4e64ab8839ee72a5005d2e4","62201e8a763a4c07bf742defdf11deb3","c2f406629c894c3ab7d1ab0c573de517","0dbf0628de1a4e17a020f36300fd0bb9","96f57e7e3c4c4b16b4c1d4668a9458fc","5a6cd25a886d4a728eb5bedbfa77d991","bc2b0a8fcd8347acbbc58154d21c7b3f","c279a65ea1944618b945a9810ca3043f","c1603434742540d48ab49ec46046c0f0","e81dbce463ea47cc9f5bee10ffbb4def","933e4839e16f4bd8a87c246634bf1173","8d12cf18d4414d538ce8c242299c93b3","d4c960f718754d26b339d4e11371409f","60342e3d9d264b76bcedb75e75606b89"]},"id":"DDKva6SaXZ0i","executionInfo":{"status":"ok","timestamp":1675821111509,"user_tz":-540,"elapsed":2377,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"dae1d3e6-3916-4e4e-c8dd-55062fa0306f"},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/13 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0bf41f331c6347ce8234765a9fcafec9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c0d5ec20f0d42c6ad7fa2885c9be26c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0dbf0628de1a4e17a020f36300fd0bb9"}},"metadata":{}}]},{"cell_type":"markdown","source":["# 성능 측정\n","NER 모델 평가는 텍스트 분류 모델 평가와 비슷. 일반적으로 정밀도, 재현율, F1-점수의 결과를 보고. 유일한 차이는 예측 하나를 정확하다고 판단하기 위해 한 개체명에 있는 모든 단어가 올바르게 예측되어야 한다는 점 <br>\n","<br>\n","seqeval 라이브러리를 사용"],"metadata":{"id":"G19YlRxrX2do"}},{"cell_type":"code","source":["!pip install seqeval"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fPg8R9AaYzo9","executionInfo":{"status":"ok","timestamp":1675821119967,"user_tz":-540,"elapsed":8465,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"374f35ae-a34d-4e8f-d33c-8449201699fb"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting seqeval\n","  Downloading seqeval-1.2.2.tar.gz (43 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 KB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.21.6)\n","Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.8/dist-packages (from seqeval) (1.0.2)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.7.3)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (3.1.0)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=0.21.3->seqeval) (1.2.0)\n","Building wheels for collected packages: seqeval\n","  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16179 sha256=c763e2631784352be3bf6f1d8e2dc9e8819e6ff8c1582528886bfa3a8a170802\n","  Stored in directory: /root/.cache/pip/wheels/ad/5c/ba/05fa33fa5855777b7d686e843ec07452f22a66a138e290e732\n","Successfully built seqeval\n","Installing collected packages: seqeval\n","Successfully installed seqeval-1.2.2\n"]}]},{"cell_type":"code","source":["from seqeval.metrics import classification_report\n","\n","y_true = [[\"O\", \"O\", \"O\", \"B-MISC\", \"I-MISC\", \"I-MISC\", \"O\"], [\"B-PER\", \"I-PER\", \"O\"]]\n","y_pred = [[\"O\", \"O\", \"B-MISC\", \"I-MISC\", \"I-MISC\", \"I-MISC\", \"O\"], [\"B-PER\", \"I-PER\", \"O\"]]\n","\n","print(classification_report(y_true, y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Os-Bhu0EYL-8","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":406,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"179b1fe3-9138-4606-ffda-79454d01bf50"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","        MISC       0.00      0.00      0.00         1\n","         PER       1.00      1.00      1.00         1\n","\n","   micro avg       0.50      0.50      0.50         2\n","   macro avg       0.50      0.50      0.50         2\n","weighted avg       0.50      0.50      0.50         2\n","\n"]}]},{"cell_type":"markdown","source":["seqeval은 리스트의 리스트로 구성된 예측과 레이블을 입력받음. 이런 지표를 훈련 과정에 통합하려면 모델 출력을 seqeval가 기대하는 리스트로 변환하는 함수가 필요. 이를 위해 연속된 부분단어의 레이블 ID는 무시하는 작업이 필요"],"metadata":{"id":"i0OmBlByZSDh"}},{"cell_type":"code","source":["import numpy as np\n","\n","def align_predictions(predictions, label_ids):\n","  preds = np.argmax(predictions, axis=2)\n","  batch_size, seq_len = preds.shape\n","  labels_list, preds_list = [], []\n","\n","  for batch_idx in range(batch_size):\n","    example_labels, example_preds = [], []\n","    for seq_idx in range(seq_len):\n","      # 레이블 IDs = -100 무시\n","      if label_ids[batch_idx, seq_idx] != -100:\n","        example_labels.append(index2tag[label_ids[batch_idx][seq_idx]])\n","        example_preds.append(index2tag[preds[batch_idx][seq_idx]])\n","\n","    labels_list.append(example_labels)\n","    preds_list.append(example_preds)\n","\n","  return preds_list, labels_list"],"metadata":{"id":"4jybU_ppZhRQ","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":3,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":34,"outputs":[]},{"cell_type":"markdown","source":["# XLM-RoBERTa 미세튜닝\n","PAN-X의 독일어 서브셋에 베이스 모델을 미세튜닝하고 프랑스어, 이태리어, 영어에서 제로샷 교차 언어 성능을 평가. 트랜스포머스 Trainer를 사용해 훈련 루프 처리"],"metadata":{"id":"U6UChFKpa9iq"}},{"cell_type":"code","source":["from transformers import TrainingArguments\n","\n","num_epochs = 3\n","batch_size = 24\n","logging_steps = len(panx_de_encoded[\"train\"])\n","model_name = f\"{xlmr_model_name}-finetuned-panx-de\"\n","training_args = TrainingArguments(output_dir=model_name, \n","                                  log_level=\"error\",\n","                                  num_train_epochs=num_epochs,\n","                                  per_device_train_batch_size=batch_size,\n","                                  per_device_eval_batch_size=batch_size,\n","                                  evaluation_strategy=\"epoch\",\n","                                  save_steps=1e6,\n","                                  weight_decay=0.01,\n","                                  disable_tqdm=False,\n","                                  logging_steps=logging_steps,\n","                                  push_to_hub=False)"],"metadata":{"id":"XdWVVYu5bJ9a","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":3,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":["검증 세트에서 평가 지표를 어떻게 계산해야 할지 Trainer로 전달해야 함 "],"metadata":{"id":"gg6J8oMzcT5y"}},{"cell_type":"code","source":["from seqeval.metrics import f1_score\n","\n","def compute_metrics(eval_pred):\n","  y_pred, y_true = align_predictions(eval_pred.predictions, eval_pred.label_ids)\n","  return {\"f1\": f1_score(y_true, y_pred)}"],"metadata":{"id":"i39ycBuhcC9e","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":3,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":["배치에서 가장 큰 시퀀스 길이로 입력 시퀀스를 패딩하도록 데이터 콜레이터(data collator)를 정의. 트랜스포머스는 토큰 분류를 위해 입력과 레이블을 패딩하는 전용 데이터 콜레이터를 제공"],"metadata":{"id":"Lhwpoa-Pcd1s"}},{"cell_type":"code","source":["from transformers import DataCollatorForTokenClassification\n","\n","data_collator = DataCollatorForTokenClassification(xlmr_tokenizer)"],"metadata":{"id":"lLfASUe6cmtH","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":2,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":["레이블도 시퀀스이기에 레이블 패딩도 필수"],"metadata":{"id":"F5mwIWiPcz8e"}},{"cell_type":"markdown","source":["Trainer를 위해 매번 새로운 모델을 만들지 않도록 model_init() 함수 제작. train() 메서드를 호출시 이 함수가 호출되어 훈련되지 않은 모델을 로드"],"metadata":{"id":"OiTxzDyAc4NX"}},{"cell_type":"code","source":["def model_init():\n","  return (XLMRobertaForTokenClassification.from_pretrained(xlmr_model_name, config=xlmr_config).to(device))"],"metadata":{"id":"fHxrwxLYdChA","executionInfo":{"status":"ok","timestamp":1675821120368,"user_tz":-540,"elapsed":2,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":38,"outputs":[]},{"cell_type":"markdown","source":["Encoding된 데이터셋과 함께 모든 정보를 Trainer에 전달"],"metadata":{"id":"MPGRlJimdoLF"}},{"cell_type":"code","source":["from transformers import Trainer\n","\n","trainer = Trainer(model_init=model_init,\n","                  args=training_args,\n","                  data_collator=data_collator,\n","                  compute_metrics=compute_metrics,\n","                  train_dataset=panx_de_encoded[\"train\"],\n","                  eval_dataset=panx_de_encoded[\"validation\"],\n","                  tokenizer=xlmr_tokenizer)"],"metadata":{"id":"FkzonrDedOZY","executionInfo":{"status":"ok","timestamp":1675821123421,"user_tz":-540,"elapsed":3055,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":39,"outputs":[]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":259},"id":"dp6ZZb8RdswR","executionInfo":{"status":"ok","timestamp":1675821640752,"user_tz":-540,"elapsed":517333,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"ffbdff70-13ec-4e23-afff-4cbbcfcb0248"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1575' max='1575' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1575/1575 08:33, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>0.162139</td>\n","      <td>0.820606</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.137930</td>\n","      <td>0.848582</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.135794</td>\n","      <td>0.863830</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1575, training_loss=0.1560986812531002, metrics={'train_runtime': 513.6597, 'train_samples_per_second': 73.473, 'train_steps_per_second': 3.066, 'total_flos': 863012377186080.0, 'train_loss': 0.1560986812531002, 'epoch': 3.0})"]},"metadata":{},"execution_count":40}]},{"cell_type":"code","source":["text_de = \"Jeff Dean ist ein Informatiker bei Google in Kalifornien\"\n","tag_text(text_de, tags, trainer.model, xlmr_tokenizer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"KhAcv-0Tg570","executionInfo":{"status":"ok","timestamp":1675821640752,"user_tz":-540,"elapsed":27,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"a3967115-e433-4c8b-a517-e363936451c8"},"execution_count":41,"outputs":[{"output_type":"execute_result","data":{"text/plain":["         0      1      2      3     4     5           6    7     8        9   \\\n","Tokens  <s>  ▁Jeff    ▁De     an  ▁ist  ▁ein  ▁Informati  ker  ▁bei  ▁Google   \n","Tags      O  B-PER  I-PER  I-PER     O     O           O    O     O    B-ORG   \n","\n","         10          11     12     13  \n","Tokens  ▁in  ▁Kaliforni     en   </s>  \n","Tags      O       B-LOC  I-LOC  I-PER  "],"text/html":["\n","  <div id=\"df-dfe2ea94-2537-41a4-8080-808ee66e69ac\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Tokens</th>\n","      <td>&lt;s&gt;</td>\n","      <td>▁Jeff</td>\n","      <td>▁De</td>\n","      <td>an</td>\n","      <td>▁ist</td>\n","      <td>▁ein</td>\n","      <td>▁Informati</td>\n","      <td>ker</td>\n","      <td>▁bei</td>\n","      <td>▁Google</td>\n","      <td>▁in</td>\n","      <td>▁Kaliforni</td>\n","      <td>en</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>Tags</th>\n","      <td>O</td>\n","      <td>B-PER</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-ORG</td>\n","      <td>O</td>\n","      <td>B-LOC</td>\n","      <td>I-LOC</td>\n","      <td>I-PER</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dfe2ea94-2537-41a4-8080-808ee66e69ac')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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NER에 0 클래스 또는 O개체명이 일반 클래스처럼 포함될 때가 존재\n","\n","<br>\n","\n","모델의 성능이 기대에 못 미칠 때 오류를 살펴보면, 유용한 통찰을 얻고 코드만 봐서는 찾기 힘든 버그를 발견하게 됨.<br>\n","오류 분석을 위한 방법으로 **손실이 가장 큰 검증 샘플을 살펴보는 것**. 샘플 시퀀스의 토큰마다 손실을 계산"],"metadata":{"id":"kNA8C7eHhPfM"}},{"cell_type":"code","source":["from torch.nn.functional import cross_entropy\n","\n","def forward_pass_with_label(batch):\n","  # 리스트의 딕셔너리를 데이터 콜레이터에 적합한 딕셔너리의 리스트로 변환\n","  features = [dict(zip(batch, t)) for t in zip(*batch.values())]\n","  # 입력과 레이블을 패딩하고 모든 텐서를 장치에 배치\n","  batch = data_collator(features)\n","  input_ids = batch[\"input_ids\"].to(device)\n","  attention_mask = batch[\"attention_mask\"].to(device)\n","  labels = batch[\"labels\"].to(device)\n","  with torch.no_grad():\n","    # 데이터를 모델에 전달\n","    output = trainer.model(input_ids, attention_mask)\n","    # logit.size: [batch_size, sequence_length, classes]\n","    # 마지막 축을 따라 가장 큰 로짓 값을 가진 클래스를 선택\n","    predicted_label = torch.argmax(output.logits, axis=-1).cpu().numpy()\n","  # 배치 차원을 펼친 다음 토큰마다 손실을 계산\n","  loss = cross_entropy(output.logits.view(-1, 7), labels.view(-1), reduction=\"none\")\n","  # 배치 차원을 다시 만들고 넘파이 배열로 변환\n","  loss = loss.view(len(input_ids), -1).cpu().numpy()\n","\n","  return {\"loss\":loss, \"predicted_label\": predicted_label}"],"metadata":{"id":"6vAZdu1Nh1GH","executionInfo":{"status":"ok","timestamp":1675821640752,"user_tz":-540,"elapsed":7,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":42,"outputs":[]},{"cell_type":"code","source":["valid_set = panx_de_encoded[\"validation\"]\n","valid_set = valid_set.map(forward_pass_with_label, batched=True, batch_size=32)\n","df = valid_set.to_pandas()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":86,"referenced_widgets":["4cc6e9d90ff749db80a80877c4ceb88b","898cd3c6298d42dc9d0bd51adcee9069","c3597b8ec6734c35b028daea5e4a3bbb","31d42d93cd534bb09fc0d962b63ae09e","84ef7a7c8fc342d48da529843f65f2fa","e1fadba579f04906bcf21ff823e366f9","b9c6e83d7b8f4ec48de17c75776fbf59","17d21e082a4e460c956158a271ad48ee","42c79b0e7b364da98319a4cd8522eb13","fbe1408c5e624cc9becac343bcbdfff0","0a17783cd6264c85b3d75c6cbfc189b3"]},"id":"7HHXhKiNq0h5","executionInfo":{"status":"ok","timestamp":1675821669245,"user_tz":-540,"elapsed":28500,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"8a803f54-b85a-4955-8245-fb8242ef7fb7"},"execution_count":43,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.fingerprint:Parameter 'function'=<function forward_pass_with_label at 0x7f05029198b0> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/197 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cc6e9d90ff749db80a80877c4ceb88b"}},"metadata":{}}]},{"cell_type":"markdown","source":["- 토큰과 레이블이 여전히 ID로 인코딩됐으니 결과를 알아보기 쉽게 토큰과 레이블을 다시 문자로 변환\n","- 필터링에 사용하기 위해 -100인 패딩 토큰은 특별한 레이블인 IGN으로 할당\n","- loss와 predicted_label을 입력과 같은 길이로 잘라 패딩을 모두 제거"],"metadata":{"id":"SyIXWTwergT8"}},{"cell_type":"code","source":["index2tag[-100] = \"IGN\"\n","df[\"input_tokens\"] = df[\"input_ids\"].apply(lambda x: xlmr_tokenizer.convert_ids_to_tokens(x))\n","df[\"predicted_label\"] = df[\"predicted_label\"].apply(lambda x: [index2tag[i] for i in x])\n","df[\"labels\"] = df[\"labels\"].apply(lambda x: [index2tag[i] for i in x])\n","df[\"loss\"] = df.apply(lambda x: x['loss'][:len(x['input_ids'])], axis=1)\n","df[\"predicted_label\"] = df.apply(lambda x: x['predicted_label'][:len(x['input_ids'])], axis=1)\n","df.head(1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81},"id":"e_mgBFFrrdlO","executionInfo":{"status":"ok","timestamp":1675821669847,"user_tz":-540,"elapsed":621,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"934416ba-20b9-4270-d69a-2ee7580e6e07"},"execution_count":44,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                            input_ids         attention_mask  \\\n","0  [0, 10699, 11, 15, 16104, 1388, 2]  [1, 1, 1, 1, 1, 1, 1]   \n","\n","                                        labels  \\\n","0  [IGN, B-ORG, IGN, I-ORG, I-ORG, I-ORG, IGN]   \n","\n","                                                loss  \\\n","0  [0.0, 0.01121267, 0.0, 0.012447174, 0.01018930...   \n","\n","                                     predicted_label  \\\n","0  [I-ORG, B-ORG, I-ORG, I-ORG, I-ORG, I-ORG, I-ORG]   \n","\n","                                 input_tokens  \n","0  [<s>, ▁Ham, a, ▁(, ▁Unternehmen, ▁), </s>]  "],"text/html":["\n","  <div id=\"df-8abcdd95-7bd2-43dd-8b2f-2393033b416e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>attention_mask</th>\n","      <th>labels</th>\n","      <th>loss</th>\n","      <th>predicted_label</th>\n","      <th>input_tokens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[0, 10699, 11, 15, 16104, 1388, 2]</td>\n","      <td>[1, 1, 1, 1, 1, 1, 1]</td>\n","      <td>[IGN, B-ORG, IGN, I-ORG, I-ORG, I-ORG, IGN]</td>\n","      <td>[0.0, 0.01121267, 0.0, 0.012447174, 0.01018930...</td>\n","      <td>[I-ORG, B-ORG, I-ORG, I-ORG, I-ORG, I-ORG, I-ORG]</td>\n","      <td>[&lt;s&gt;, ▁Ham, a, ▁(, ▁Unternehmen, ▁), &lt;/s&gt;]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8abcdd95-7bd2-43dd-8b2f-2393033b416e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":44}]},{"cell_type":"markdown","source":["pandas.Series.explode() 함수를 사용하여 한줄 코드로 리스트에 있는 각 원소를 하나의 행으로 만들 수 있음. IGN으로 표시된 패딩 토큰의 손실이 0이므로 이를 제외"],"metadata":{"id":"HQu-saGms15V"}},{"cell_type":"code","source":["df_tokens = df.apply(pd.Series.explode)\n","df_tokens = df_tokens.query(\"labels != 'IGN'\")\n","df_tokens[\"loss\"] = df_tokens[\"loss\"].astype(float).round(2)\n","df_tokens.head(7)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":269},"id":"rYMEatIIs9Ye","executionInfo":{"status":"ok","timestamp":1675821715442,"user_tz":-540,"elapsed":408,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"4105c8c6-2760-4259-c2cb-95e01609f71b"},"execution_count":46,"outputs":[{"output_type":"execute_result","data":{"text/plain":["  input_ids attention_mask labels  loss predicted_label  input_tokens\n","0     10699              1  B-ORG  0.01           B-ORG          ▁Ham\n","0        15              1  I-ORG  0.01           I-ORG            ▁(\n","0     16104              1  I-ORG  0.01           I-ORG  ▁Unternehmen\n","0      1388              1  I-ORG  0.01           I-ORG            ▁)\n","1     56530              1      O  0.00               O           ▁WE\n","1     83982              1  B-ORG  2.90           B-PER          ▁Luz\n","1        10              1  I-ORG  2.83           I-PER            ▁a"],"text/html":["\n","  <div id=\"df-e6e1f7fc-dcee-4419-a0b7-d892afdd7ab1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>attention_mask</th>\n","      <th>labels</th>\n","      <th>loss</th>\n","      <th>predicted_label</th>\n","      <th>input_tokens</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>10699</td>\n","      <td>1</td>\n","      <td>B-ORG</td>\n","      <td>0.01</td>\n","      <td>B-ORG</td>\n","      <td>▁Ham</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>15</td>\n","      <td>1</td>\n","      <td>I-ORG</td>\n","      <td>0.01</td>\n","      <td>I-ORG</td>\n","      <td>▁(</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>16104</td>\n","      <td>1</td>\n","      <td>I-ORG</td>\n","      <td>0.01</td>\n","      <td>I-ORG</td>\n","      <td>▁Unternehmen</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1388</td>\n","      <td>1</td>\n","      <td>I-ORG</td>\n","      <td>0.01</td>\n","      <td>I-ORG</td>\n","      <td>▁)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>56530</td>\n","      <td>1</td>\n","      <td>O</td>\n","      <td>0.00</td>\n","      <td>O</td>\n","      <td>▁WE</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>83982</td>\n","      <td>1</td>\n","      <td>B-ORG</td>\n","      <td>2.90</td>\n","      <td>B-PER</td>\n","      <td>▁Luz</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>I-ORG</td>\n","      <td>2.83</td>\n","      <td>I-PER</td>\n","      <td>▁a</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6e1f7fc-dcee-4419-a0b7-d892afdd7ab1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":46}]},{"cell_type":"markdown","source":["데이터를 이런 형태로 마들면 입력 토큰을 기준으로 토큰 개수, 토큰 손실의 평균과 합을 계산하기 쉬움. "],"metadata":{"id":"ayD509cXtQBN"}},{"cell_type":"code","source":["# 손실의 총합을 기준으로 정렬해 검증 세트에서 누적 손실이 가장 큰 토큰 확인\n","(\n","    df_tokens.groupby(\"input_tokens\")[[\"loss\"]].agg([\"count\", \"mean\", \"sum\"]).droplevel(level=0, axis=1).sort_values(by=\"sum\", ascending=False).reset_index().round(2).head(10).T\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"AfNhDvEptchO","executionInfo":{"status":"ok","timestamp":1675821857551,"user_tz":-540,"elapsed":535,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"f89f88d1-7118-480e-cf17-999749e51a01"},"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                  0       1       2       3      4      5      6      7  \\\n","input_tokens      ▁     ▁in    ▁der    ▁von   ▁und     ▁/    ▁''     ▁(   \n","count          6066     989    1388     808   1171    163   2898    246   \n","mean           0.03    0.14    0.09    0.13   0.07   0.46   0.02   0.28   \n","sum           203.8  143.31  129.16  105.29  81.04  75.43  70.93  69.69   \n","\n","                  8      9  \n","input_tokens     ▁)     ▁A  \n","count           246    125  \n","mean           0.26   0.46  \n","sum           63.74  57.43  "],"text/html":["\n","  <div id=\"df-78004492-4a03-4e3c-abfc-454eeab8bc17\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>input_tokens</th>\n","      <td>▁</td>\n","      <td>▁in</td>\n","      <td>▁der</td>\n","      <td>▁von</td>\n","      <td>▁und</td>\n","      <td>▁/</td>\n","      <td>▁''</td>\n","      <td>▁(</td>\n","      <td>▁)</td>\n","      <td>▁A</td>\n","    </tr>\n","    <tr>\n","      <th>count</th>\n","      <td>6066</td>\n","      <td>989</td>\n","      <td>1388</td>\n","      <td>808</td>\n","      <td>1171</td>\n","      <td>163</td>\n","      <td>2898</td>\n","      <td>246</td>\n","      <td>246</td>\n","      <td>125</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.03</td>\n","      <td>0.14</td>\n","      <td>0.09</td>\n","      <td>0.13</td>\n","      <td>0.07</td>\n","      <td>0.46</td>\n","      <td>0.02</td>\n","      <td>0.28</td>\n","      <td>0.26</td>\n","      <td>0.46</td>\n","    </tr>\n","    <tr>\n","      <th>sum</th>\n","      <td>203.8</td>\n","      <td>143.31</td>\n","      <td>129.16</td>\n","      <td>105.29</td>\n","      <td>81.04</td>\n","      <td>75.43</td>\n","      <td>70.93</td>\n","      <td>69.69</td>\n","      <td>63.74</td>\n","      <td>57.43</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78004492-4a03-4e3c-abfc-454eeab8bc17')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":48}]},{"cell_type":"markdown","source":["- 공백 토큰의 손실 총합이 가장 큼. 하지만 평균 손실은 다른 토큰에 비해 훨씬 낮아 모델이 이 토큰을 구분하는 데 큰 힘을 들이지 않는다는 의미\n","- 'in', 'von', 'der', 'und' 같은 단어가 비교적 자주 등장. 개체명과 함께 등장하거나 개체명의 일부인 경우가 많아 모델이 혼동하기 쉬움\n","- 단어 시작 부분의 괄호, 슬래시, 대문자는 드물지만 평균 손실이 매우 높음"],"metadata":{"id":"50cIVFjwxFYb"}},{"cell_type":"markdown","source":["레이블ID로 그룹핑해 각 클래스에 대한 손실도 확인 가능"],"metadata":{"id":"BsTGa7MmxdAq"}},{"cell_type":"code","source":["(\n","    df_tokens.groupby(\"labels\")[[\"loss\"]].agg([\"count\", \"mean\", \"sum\"]).droplevel(level=0, axis=1).sort_values(by=\"mean\", ascending=False).reset_index().round(2).T\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"IYFkJNjHxcRA","executionInfo":{"status":"ok","timestamp":1675822050165,"user_tz":-540,"elapsed":533,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"52ab611b-889d-48f7-99b6-e23570e39959"},"execution_count":49,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              0       1        2        3       4       5        6\n","labels    B-ORG   I-LOC    I-ORG    B-LOC   B-PER   I-PER        O\n","count      2683    1462     3820     3172    2893    4139    43648\n","mean       0.62    0.61     0.48     0.33    0.27    0.19     0.03\n","sum     1675.56  897.82  1826.57  1035.34  792.12  786.43  1337.71"],"text/html":["\n","  <div id=\"df-20e6ae97-3b18-41d5-ac11-6c31640137a9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>labels</th>\n","      <td>B-ORG</td>\n","      <td>I-LOC</td>\n","      <td>I-ORG</td>\n","      <td>B-LOC</td>\n","      <td>B-PER</td>\n","      <td>I-PER</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>count</th>\n","      <td>2683</td>\n","      <td>1462</td>\n","      <td>3820</td>\n","      <td>3172</td>\n","      <td>2893</td>\n","      <td>4139</td>\n","      <td>43648</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.62</td>\n","      <td>0.61</td>\n","      <td>0.48</td>\n","      <td>0.33</td>\n","      <td>0.27</td>\n","      <td>0.19</td>\n","      <td>0.03</td>\n","    </tr>\n","    <tr>\n","      <th>sum</th>\n","      <td>1675.56</td>\n","      <td>897.82</td>\n","      <td>1826.57</td>\n","      <td>1035.34</td>\n","      <td>792.12</td>\n","      <td>786.43</td>\n","      <td>1337.71</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-20e6ae97-3b18-41d5-ac11-6c31640137a9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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plot_confusion_matrix(y_preds, y_true, labels):\n","  cm = confusion_matrix(y_true, y_preds, normalize=\"true\")\n","  fig, ax = plt.subplots(figsize=(6,6))\n","  disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n","  disp.plot(cmap=\"Blues\", values_format=\".2f\", ax=ax, colorbar=False)\n","  plt.title(\"Normalized confusion matrix\")\n","  plt.show()\n","\n","plot_confusion_matrix(df_tokens[\"labels\"], df_tokens[\"predicted_label\"], tags.names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":404},"id":"Rr_DPaOWyCBJ","executionInfo":{"status":"ok","timestamp":1675822306178,"user_tz":-540,"elapsed":588,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"737d11c2-6a7d-4c47-8477-7d67570ab268"},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x432 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAGDCAYAAAA1cVfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3gU5drH8e+dhAAJpEACJIGAcKSodJSOgIJgAUVFUFTQ46F4jseCSFEJCBakCIod9VCUjqCAFJGiBoGEJggWICAJkCBEQIFk93n/2EmymzKEmGyS1/tzXXuxO/PMzG8fZueestkRYwxKKaVUXnyKO4BSSqmSTQuFUkopW1oolFJK2dJCoZRSypYWCqWUUra0UCillLKlhUL9vyEi60Xkn9bz+0RkdSHPv5aIGBHxK8z5XmKZIiIfisgpEdnyF+bTXkT2F2a24iIi0SJyVkR8izvL34UWCpVvInJIRE6ISKDbsH+KyPpijJUrY8wcY0zX4s5RCNoBXYDqxpjrCjoTY8wmY0y9wotVNKx17Ea7NsaYw8aYCsYYh7dy/d1poVCXyxf471+dibWnrOvfpdUEDhljzhV3kJLAm0dzKot+UNXlehUYKiIhuY0UkTYislVEUq1/27iNWy8i40XkG+APoLZ1KmeIiPwkImdE5AURqSMi34rI7yIyX0T8relDReRzEUm2TsV8LiLV88jRX0S+tp4Ps05VZDzSROQja1ywiMwQkSQROSoi4zJOaYiIr4hMFJEUETkA3GLXMSJSQ0QWW/lOisgb1nAfEXlWRBKsI7KZIhJsjcs4nfWgiBy2ljXKGvcw8D7Q2so9xv19uS3XiMg/rOc3i8heqy+PishQa3hHEfnVbZoG1v/HaRHZIyI93MZ9JCLTRWS5NZ/vRKROHu85I/8AETli/b8MEpFrRWSXNf833NrXEZF1Vv+kiMicjHVJRGYB0cBn1vsd5jb/h0XkMLDObZifiFQSkV9F5DZrHhVE5GcRecDu/0pdJmOMPvSRrwdwCLgRWAyMs4b9E1hvPa8EnALuB/yAvtbrytb49cBh4GprfBnAAEuBIGv4BeBLoDYQDOwFHrSmrwzcCQQAFYEFwKdu+dYD/7Se9we+zuU91AASge7W6yXAO0AgUAXYAgy0xg0C9lnTVAK+svL65TJfX2AnMMWaVzmgnTXuIeBn6z1VsPpvljWuljXP94DyQGOrDxrk9j5ye1/W9P+wnicB7a3noUAz63lH4FfreRkrz0jAH+gMnAHqWeM/Ak4C11n/T3OAuXmsExn537bec1fgPPCp1Z9RwAngeqv9P3CdSisLhAMbgdeyr2O5zH+m1a/l3Yb5WW26Ases5b0HLCzuz8r/t0exB9BH6XmQVSiuAVKtD7p7obgf2JJtmligv/V8PTA223gDtHV7HQc84/Z6kvuGJNu0TYBTbq/XY1MorI1M5vyBqtZGubxbm77AV9bzdcAgt3FdybtQtAaS8xj3JTDE7XU9IM3aCGds9Kq7jd8C9MntfeTxvtwLxWFgIBCUrU1HsgpFe2vD6uM2/hMgxnr+EfC+27ibgX15/B9k5I9yG3YSuMft9SLg8Tymvx3Ynn0dy2X+tXMZ5uc27HVgN3AUa8dEH4X30FNP6rIZY74HPgeGZxsVCSRkG5aAa68yw5FcZnnc7fmfubyuACAiASLyjnUK53dce6Mhkv9vv8wA9htjXrFe18S1d51knSI5jevooorb+3HPm/29uasBJBhj0nMZl71fEnAViapuw465Pf8D6z0XwJ24NuwJIrJBRFrnkeeIMcaZLZP7/9Pl5snv/2FVEZlrnRb7HZgNhF1i3pD7euPuXVw7MB8ZY07mY37qMmihUAU1GngEz41LIq6Nr7toXHt5Gf7KzxU/hWtvvKUxJgjoYA2XS00oIsOBusDDboOP4DqiCDPGhFiPIGPM1db4JFwFIEO0zSKOANGS+8XW7P0SDaTjuTHNr3O4Tr0BICLV3EcaY7YaY3riKnafAvPzyFNDPL9MkP3/qai8iGsdaGj9H/bD8/8vr/Ujz/XG2lF4F9fpqSEZ12tU4dFCoQrEGPMzMA94zG3wCqCuiNxrXWi8B7gK19FHYaiIa+/0tIhUwlWsLklEuls57zDG/On2HpKA1cAkEQmyLjrXEZHrrSbzgcdEpLqIhJLzCMrdFlyF5WURCRSRciLS1hr3CfCEiFwhIhVwbSzn5XH0cSk7gatFpImIlANi3N6nv7j+fiTYGJMG/A44c5nHd7iOEoaJSBkR6QjcBswtQJ7LVRE4C6SKSBTwdLbxx3Fdy7kcI3EVkodwfdli5mUcZap80EKh/oqxuC4wAmAd8t+Ka8//JDAMuNUYk1JIy3sN13WGFGAz8EU+p7sH1/WUHyTrm09vW+MewHVBdy+uC+8LgQhr3HvAKlwb53hcF6FzZVzf6b8N18Xaw8Cv1nIBPgBm4TpVdhDXxd7/5DN79uX8iKvf1wI/AV9na3I/cMg6rTMIuC+XeVy0snbH1ZdvAg8YY/YVJNNlGgM0w3WNazk5+/Ql4FnrVODQS81MRJoDT+LK7wBewVU07Iq6ukxiXQhSSimlcqVHFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLK1t/ylxjFv4KR8pWKO0aBNKoTXtwRCsRHLvk3cUplKs1rS2n9HunhhEOkpKTk2vV/z0JRvhJl22X/O5/SYe28wcUdoUDK+5fev3/SjZb3+fqU3l5Pd+T2N44lX4c2ed/uRE89KaWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStv6WNy4qqBuaRvPSIx3w9RFmrdnLa4viPMZXD6vAm493ITiwLL4+wpiZ37ImLoEyfj5MGdKJpnWq4DQw/P2NfPP9Ua9m/+q7H4iZuhiH09D31lY82u9Gj/EXLqbz+PjZ7N7/K6FBAbw55kFqRFQG4IefExk+cR5nz11AfITP332ScmXLeCX3l7F7GTl5EU6nk349WvPfB7tmy53GkDGz2LXvCKHBgbw/bgDRkZX5LfUcA4bPYMcPCfS5pSWvPN3bK3mzZx/hlv3xPLLvtLLPyJZ9u5V9QjFlL439vvbbvYyYtBCH08n9PdvwRP+cuQePnsWOfYepFBzIBy8+RHSkaz2f/OEqZi+LxdfHh5eH3sUNra/yavYvY/cyaspiHBl9/kCXHNkfHTObnfuPUCkokPfG9Sc6sjLrv9vHC28uIy3dQRk/X2L+czvtW9Qt1Gyl/ohCRKqLyFIR+UlEfhGRqSLiX9jL8fERXh3YkbvHLKPVv+dwZ/u61KsR6tHmqd7X8unXP3H9E3N5eOIXTBzYEYAHu14NQNv/fsIdoz9l3IB2ePPOoA6Hk2cnL2TmxIGsmzWcpWvj+fHgMY82c5dvJqRiAF/PfZZ/9u7Ii29/BkB6uoPHXpjFS0N78+Ws4SyY9m/K+HnnbnUOh5NnXl3AvNcG883cUSxeHcf+A0kebeYsiyWkYgBbF41mUJ9OjJm+FICy/n6MGHgLMY/d4ZWs2TkcToa9uoD5rw3mWyv7vmzZZ1vZty0azeBcso8pxuylsd8dDidPT5jPgqlD2Dz/WRbl0uezlsYSHFSe+CUxDL63EzGvu3LvO5DE4jXxxM4bxcJpQxj6ynwcXrxTncPhZPjEBcydMohvPhnJktVx7D+Yvc83ExIUwNaFzzOob0fGTl8GQKWQQOZMHMjGOSN44/l+DBkzq9DzlepCISICLAY+NcZcCdQFKgDjC3tZza+syoFjp0k4/jtp6U4Wb/qRm6+r7dnIQMUAV40KCijLsVPnAKhXoxKbdv0KQErqn6Seu0DTf1Qt7Ih52vFDArWiwqgZGYZ/GT963NCU1V/v9mizetNu7up2LQC3dGzMN3E/YYxh49b9NKgTyVX/iAIgNDgQX1/vrDbxexO4onoYtaJcue/o0pyVGz1zr9y4mz63tASgR+cmbNr6I8YYAsuXpVWTOpTzL56D5oJk35gte9lSlL0k9HvcnkPUrhFGrequ3L26NGPFhl3Zcu+ir5W7Z+embNi6H2MMKzbsoleXZpT1L0PNqDBq1wgjbs8hr2WP35tArerhmX1+e5dmOft8027uudl1u9LbOjVh0zZXnzeqV4Nq4cEA1K8dwfkLaVy4mFao+Up1oQA6A+eNMR8CGGMcwBPAQyISUJgLiqgcyNGUs5mvE0+eJaJyBY82L8/9jt7X1+P7GQOY//xtDHt3AwDfH0yh23VX4OsjRFcJokmdKkSFeU5blI4lpxJZJevoJyI8hGMpqZ5tUrLa+Pn5UjGwHKdSz3HgyAlEhPuefIvuD03krTlfei130onTRFbNyh1ZJYSk5NOebZJTiaoSkpk7qEJ5fks957WMeUk6cZqofGSPLKHZS2O/JyWnevZ51VCSkj3X88QTWW3cc+eYtkrOaYtSUvLpzP50LT8kx/KPJacSVdW9z8vl6PPPvtpBo7rVKetfuKeGS/s1iqsBjwsFxpjfReQw8A9gV65TFZE729fl43X7mL50O9fWq8bbT3SlzX/mMHvtXurWCOWrSfdwJPkMW/Yl4XCWjtvepzucbN19gM/ffZLy5fzp8/h0GtarQbtCPgeqlPpr9h1I4oXpy5g/dUihz7u0H1Hkm4j8S0S2icg2c/HspSfIJunkOY+jgMjKFUg66Tmffl2u4tNvfgJg6/5jlCvjS+Wg8jichlEzvqbDE3O578XlBFcoyy+Jp/7aG7oM1cKDSTyRtbyk5NNUCwv2bBOW1SY93cGZc+cJDQ4kIjyElo3rUCmkAuXL+dOp1VV8/+OvXskdUSWExONZuRNPnCYiPMSzTXgwR0+czsz9+9k/qRQc6JV8diKqhHA0H9kTS2j20tjvEeHBnn1+/BQR4Z7reWSVrDbuuXNMeyLntEUpIjwksz9dyz+dY/nVwoM5ety9z89n9nniiVM8+Mz7vPH8/VxRPbzQ85X2QrEXaO4+QESCgGjgZ/fhxph3jTEtjDEtxP/yT/vE/3ScOhEhRFcJooyfD73a12XlloMebY4mn6VDo+oA1K0eSll/X1JS/6S8vx8BZV0Hbx0b1yDd4WT/Ee8Visb1ozn0awqHE09yMS2dZV9up0u7azzadGl3DQu/2ArA8vU7advsSkSE61vWZ98vSfx5/iLp6Q6+2/ELV9byzvWVpg2iOXAkmYTEFC6mpbNkTRzdOjT0aNOtfUPmLv8OgGXrdtC+RV3Em98UyENu2buX4uylod+bXVWTXw4nk3DUlXvxmni6d2jk0aZb+4Z8YuVeum47Ha515e7eoRGL18Rz4WIaCUdT+OVwMs2vruW17E0bRHPwSDIJ1mf00zXxdGufvc+vYd6KLYDrFFO7Fq7PaOqZP7j3yXd4bkgPWjaundvs/zIxpnScAsmNdTF7KzDNGDNTRHyBt4HfjTFP5TWdT3C0Kdvu6cteXpfmNXnx4fb4+vgw58u9TFqwjRH3tmTHzydYueUg9WqEMvXRzgSW88cYw+j/fcNXO45Qo0pFFsX0xOk0JP12jsde/5IjyWcK9J6PzBtcoOnWxe4lZtoSHE4n99zSksce6MrE91fQqH40Xdtdw/kLaTw+bjbf/3SUkKAApsc8QM3IMAAWr9rG9NlrQaBzq6sYNaTHZS+/vH/Bvim15ps9jJqyCKfTcO9trXhywE289M5ymjSIpnuHhpy/kMaQmJns/vFXQoICeG/cAGpFuXI3vX00Z86dJy0tnaAKASycNoR6tSMuO0NBN38Z2R1W9qdyyT7YLfv7btmb5JK9fgGyF/TTXdz97utTsF5f/c0eRk5eiMNhuK9HK4Y+1I0X3/6cJg2iufn6Rpy/kMag0TPZtf8IoUGBzBg/gFrVXbknfvAFc5Ztxs/XhxefvJMuba8uUIb0An5bas23e3h2ymKcTid9b3X1+cvvLqdJ/Wi6ZfT5mFns/tH1FfZ3X+hPragwJn2wimkz13BFjawjiQVThxBeqeJlLb9Dm+uIj9uWa8eX6kIBICI1gDeB+riOkFYAQ40xF/KapqCFoiQoaKEobgUtFCVB8e/jF1xp/XQXtFCUBAUtFMXNrlCU9ovZGGOOALcVdw6llPr/qrRfo1BKKVXEtFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSyVepvhVoQjeqEs25+6bz3dI3+s4o7QoEcnflAcUcosHJlSu/9vp3O0nrX7NLrQnrpvGe20+S9rugRhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStn6W94KtaC+2vwDz09djNNp6HtrK/59/40e4y9cTOe/42aze/+vhAYF8NbYB6kRUZkjSSfpeN/L1I4OB6DZ1bV45eneXs3euXEULz3QEh8fYfZXPzJ12W6P8VGVA5k+uD3Bgf74+ghjP4lj7Y5fqRFWgdhJd/BzYioA235OZuiMWK/lXrf5B55/bTEOh5N7b2vFfx7o4jH+wsV0HnthNrv2HSE0OJB3XnD1eYZfj/3G9fe9xNCHuzP43s5eyw2w9tu9jJi0EIfTyf092/BE/67ZsqcxePQsduw7TKXgQD548SGiI13ZJ3+4itnLYvH18eHloXdxQ+urvJr9y9i9jJi8CKfTSb8erXn8wZzZh4yZxU6r32eMG0B0ZGV+Sz3HgOEz2P5DAn1uackEL6/npbnPv9r8A6OnLsZhs315fNxsduWxfanjtn15uZD73WtHFCLiEJEdIrJTROJFpE0e7WJE5KjV9nsR6ZHL8IxHiIh0FJFU6/U+EZlYFPkdDiejJi9k9sSBfDV7OJ+ujefHg8c82nzy+WaCKwbwzbxneeSejox/67PMcTWjKrPmo2Gs+WiY14uEjwgTBrSi9yuraTN0Cb3a1KZeVLBHm6fuaMzSzQfpNGIZj0xbz6sPtcocd+j4GTqOWEbHEcu8WiQcDicjJy5gzqSBbPh4BJ+ujWd/9j7/LJbgiuWJXfAc/7qnI+Pe/MxjfMy0T+ncyrsfeHBlf3rCfBZMHcLm+c+yaHUc+w4kebSZtTSW4KDyxC+JYfC9nYh5fSkA+w4ksXhNPLHzRrFw2hCGvjIfh8N792F2OJwMe3UB818bzLdzR7E4l+yzl8USUjGAbYtGM7hPJ8ZMd2Uv6+/HiIG3MOaxO7yW1z13ae7zZycvZJa1fVmay/Zlbrbty4tu25daUZVZ/dEwVn80rNCLBHj31NOfxpgmxpjGwAjgJZu2U4wxTYC7gQ9ExMd9uNvjtDV8k9W+KXCriLQt7PDbf0igVvUwakaF4V/Gj543NmXV15575au/3s3d3a8F4JaOjfk67ieMzQ3LvaXZP8I4eOwMCSfOkuZwsiT2AN1bRHu0MQYqlvcHoGKAP8dO/VkcUT1s35tArerhbn3ejFWbPPv8i03f07v7dQDc2qkxm7b9mNnnKzfsIjqyMvWuqOb17HF7DlG7Rhi1qruy9+rSjBUbdnm0WblxF31vaQlAz85N2bB1P8YYVmzYRa8uzSjrX4aaUWHUrhFG3J5DXssevzeBK6qHUcvq9zu6NGflRs9+X7lxN32s7D06N2HjVle/B5YvS6smdSjr7/2TFaW5z3fksn1ZXYK2L8V1jSIIOHWpRsaYH4B0ICw/MzXG/AnsAKL+UrpcHEtOJbJKaObriPAQjiWn5tnGz8+XoMBynEo9B8DhpN/oOuBV7vz363y385fCjmcrIjSAoyfPZb5OPPkHEaGBHm0mLNrO3e3qsPuN3swb1oXhH23OHBcdXoGvXurBsue706peVa/lPpacSlTVkMzXuff5aSKrevb5b6nnOPfHBabP/pKnHurmtbzukpJTiaqatb5EVg0lKVv2xBNZbfz8fAmqUJ7fUs/lnLZKzmmLUtKJ09mWH0JS8mnPNsmpRFZx/d+4Zy9OpbrPk1OJcNu+VAsPybH8Y25tctu+3FSE2xdvlv3yIrIDKAdEAJc8YSwiLQEnkGwNekJE+lnPTxljOmVrHwpcCWwstNSFoErlYLYsGk2l4EB27TvCQyNn8NWs4VQMLFfc0TL1alObTzb+xJvL99DiynDeGtKBtsOWcPz0HzT+zwJOnb1A4ysqM+upG2j79BLO/JlW3JFtTZyxkn/16UhgQNnijqJUkcrYvoRa25eHR85gXSFvX4rj1FN9oBswU0Qkj7ZPWEVlInCPyTq+cj/15F4k2ovITuAosMoYcyz7DEXkXyKyTUS2nUxJuezw1cKDSTyRdRCUlHyaauHBebZJT3fw+7nzhAYHUtbfj0rBrj34RvVrUCuyMgeOnLjsDAWVdOoPoipnHUFEVg4g6ZTn3l+/TlfyaewhALb9lEzZMr5UrliOi+lOTp29AMDOgyc5ePx36kQEeSV3tfBgjh7P2pPNvc9DSDzu2eeVggOJ35vAC9OXcW2vMbw3fwPT/reGDxZ6b/8hIjyYo8ez1pfE46eIyJY9skpWm/R0B7+f/ZNKwYE5pz2Rc9qiFFElJNvyTxMRHuLZJjyYxBOu/xv37MWpVPd5eDBJbtuXY8mncyy/mlub7NuXULftS80i2L4Uy6knY0wsrtNJ4SIyPuPitFuTjILQ3hizKR+z3GRd+7gaeFhEmuSyzHeNMS2MMS0qh+XrTJaHJvWjOXgkhcOJJ7mYls7Stdvp2vYajzZd217DgpVbAVi+fidtm12JiHDy1NnMC2MJR1M4+GtK5jctvGH7LynUrhZEdHgFyvj6cEfr2qyMO+LR5teUc1x/TQQAdSODKefvS8rv56lcsSw+Vj2vWaUCdaoFcej4Ga/kbtIgmoO/Jrv1eTw3tfPs85vaX8P8lVsA+PyrnbRr7urzpW/9l62LR7N18Wge6X09jz3YhYfu6uCV3ADNrqrJL4eTSTiawsW0dBaviad7h0Yebbq1b8gny78DYOm67XS4ti4iQvcOjVi8Jp4LF9NIOJrCL4eTaX51La9lb9ogmgNHkklIdGVfsiaO7h0a5sg+18q+bN0O2rdwZS9OpbnPG+eyfemSbfvSpRi3L8Xy9VgRqQ/4AieNMaOAUYUxX2PMQRF5GXgG6FsY88zg5+fLuCfv5N4n38bpdHLPLS2pVzuCV99fQeP60XRtdw19bm3FYy/Mpu094wgJCuDNmAcA2LzzFya+vxI/Px98fHx4aejdhAZ5b+/L4TQ889FmFozoiq+P8PH6n9j/62mG39WUHQdT+CLuCM/P3sKUR9oy6OarMcbw6Fuu+tymQTWG392UtHQnTgNPzYjl9LmLXsnt5+fLi0/eSd8n3sLhcNLn1lbUqx3BhPdW0Lh+DW5q35C+t7biP2Nn0/ruFwgJCuDtsQ96Jdul+Pn5MmFYb+58bDoOh+G+Hq1oUCeCF9/+nCYNorn5+kbc37MNg0bPpNkdMYQGBTJj/AAAGtSJ4PYbm9Kq93j8fH14dVhvfH29t0/n5+fLK0Pv5u7H3sThNNx7Wyvq147gpXeW06RBNN07NKRfj9YMjplJizvHEBIUwPvjBmRO3+T20Zw5d560tHRWbNjNwmlDqF87wiu5S3Ofv/Dkndx3ie3Lf/PYvkxy2768XATbF/HWVXMRcQAZl/EFGGmMWZ5LuxjgrDFmYi7DHyHregXA7UAtYKgx5larXXngZ6CtMeZQblmaNGtu1m367i+8m+JTo/+s4o5QIEdnPlDcEQqsXBnf4o5QYE5n8X/rriB8fIr36OSvOHchvbgjFMgN7VuyIz4u14732hGFMSZfnzZjTIzN8NzGHQLWu7X7kyL41pNSSv1d6U94KKWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUslUs98wubj4ipfb2lkmzSsY9oS9X1S4xxR2hwE59Nba4IxRYab2laGm9hStAgH/p3Lb4SN7rih5RKKWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsqWFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsqWFQimllC0tFEoppWxpobgMX8bu5bq7X6DFnWN47X+rc4y/cDGNh0d9QIs7x9DloYkcTjwJwG+p5+g5eBrRHZ9i2KvzvR0bgHWxe2lzzzha3jWWaTPX5Bh/4WIajzz7IS3vGku3hydxOMmVPX5PAp0feIXOD7xCp/tfZsX6nV7NfcO1/2DL/x4jbvZ/ebxv+xzja1QN5tNJ/fn6/SF8NmUAkWFBHuMrBpTl+/lPMeGxW7wVOdPab/dy7Z1jaXZHDFM+yn19eWjEBzS7I4Yb+7+aub4ATP5wFc3uiOHaO8fyZexeb8YGSm/20vwZXRu7l+vueoHmvfLO/tDID2jeaww3DnDLfvocPQZPo8b1RZe92AqFiJzNY3iMiBwVkR0i8r2I9MhleMYjREQ6ikiq9XqfiEwsirwOh5Nhry5g/muD+XbuKBavjmPfgSSPNrOXxRJSMYBti0YzuE8nxkxfCkBZfz9GDLyFMY/dURTRLsnhcDJ80gI+njyITZ+MZMmaOPYf9Mz+8WebCakYwHcLn2dgn468MH0ZAPXrRLD6g6Gsm/kMc6cMZuiEeaSnO7yS28dHePW/t3L38Fm06v8Gd97QkHo1wz3ajB10E3NX76DdP99kwsz1PP/IjR7jRz7UmdhdCV7J687hcPL0hPksmDqEzfOfZVEu68uspbEEB5UnfkkMg+/tRMzrrvVl34EkFvNydhQAACAASURBVK+JJ3beKBZOG8LQV+bjcDg1ez5yl+bP6LAJC5g/dTCx80axaFXe2eMWj2Zw307EvGFlL+vHyIG3MLYIs5fUI4opxpgmwN3AByLi4z7c7XHaGr7Jat8UuFVE2hZ2oPi9CVxRPYxaUWH4l/Hjji7NWblxt0eblRt30+eWlgD06NyEjVt/xBhDYPmytGpSh7L+foUdK19c2cMzs99+YzO+yJb9i0276X3zdQDc1qkJX29zZQ8o54+fny8A5y+mI4jXcjevX50Dib+RkHSKtHQHi9ft5ua29T3a1KtVhU3xBwDYtP0g3d3GN64bQZXQCqzb+rPXMmeI23OI2jXCqFXd1ee9ujRjxYZdHm1WbtxFX2t96dm5KRu27scYw4oNu+jVpRll/ctQMyqM2jXCiNtzSLNfQmn+jMbt8czeq2vO7Cs2ZGXvmVv2skWXvaQWCgCMMT8A6UBYPtv/CewAogo7S9KJ00RVDc18HVklhKTk055tklOJrBICgJ+fL0EVyvNb6rnCjnLZjiWfzswFruzHklM92iQlpxJVNSt7xQrlMrPH7TlEh3tfpGO/l3h1WO/MwlHUIsIqcvREVs7E5N+JyHZqac8vx7i1w1UA3Nq+AUGB5QgNKo+IMG5wN557a5VXsmbn6k+39aVqKEnZ+jzxRFYb9/Ulx7RVck5blEpr9tL8GU1Kzl9298+oN7OX6EIhIi0BJ5BsDXrC7bTTV7m0DwWuBDbmMu5fIrJNRLalpCRnH61sNL+6Fhs/HsmqD4YydeYazl9IK+5ImZ57axVtG9Viw7uDadu4FkeTU3E4DP/seS1rvvuJxJTfizuiUqVe8RxnXdoTItIPOAPcY4wxIgKuU0+5XYNoLyI7cRWJ14wxx7I3MMa8C7wL0Kx5C3O5gSKqhHD0+KnM14knThMRHuLZJjyYRGuvJj3dwe9n/6RScODlLqrQVQsPIfFE1t5J4onTVAsP9mgTER7M0eOniaziyn7m7Pkc2evWqkZgQFn2HUiiSYPoIs+dlHKGqCpZOSPDg0jKtuE/dvIMD4yeC0BgOX9u63AVv587z7VX16B1w5o83PNaAsv7U8bPl3N/XmTMezkv5BcFV3+6rS/HTxGRrc8jq7jaZF9fckx7Iue0mj2X3KX4MxoRnr/sR48XT/ZiP6IQkfEZRwlugzOuRbQ3xmzKx2w2GWMaA1cDD4tIk8LO2bRBNAeOJJOQmMLFtHSWrImje4eGHm26tW/I3OXfAbBs3Q7at6iLVeCKVVb2k1xMS+fTtfHc1N4z+03trmH+ii0AfPbVDto1vxIRISHxZObF6yNJv/FzwnFqRFTySu74fUepE1WJ6GohlPHzpVfnhqz8dp9Hm0pBAZl9/MR97ZmzcjsA/xq/iIZ9JtO47xSee2sV81bv9FqRAGh2VU1+OZxMwlHX+rJ4TTzdOzTyaNOtfUM+sdaXpeu20+Fa1/rSvUMjFq+J58LFNBKOpvDL4WSaX11Ls19Caf6MNrvKyp7R56vj6JbtM9q9Q1b2pV7OXuxHFMaYUcCoQprXQRF5GXgG6FsY88zg5+fLK0Pv5u7H3sThNNx7Wyvq147gpXeW06RBNN07NKRfj9YMjplJizvHEBIUwPvjBmRO3+T20Zw5d560tHRWbNjNwmlDqF87ojAj2mZ/6am76PP4mzicTvre6sr+yrvLadwgmm7tG3Lvba3595hZtLxrLCFBAbzzQn8Atuz8hddnrcXPzxcfEV4e2pvKIRW8ktvhdDJs2nIWTXgAXx8f5qyMZ9+hZEYM6MyO/UdZ+e1+2jWpxfOPdMEYw7e7Enh66udeyXYpfn6+TBjWmzsfm47DYbivRysa1Ingxbc/p0mDaG6+vhH392zDoNEzaXZHDKFBgcwY71pfGtSJ4PYbm9Kq93j8fH14dVhvfH29t09XWrOX9s/ohKfv5i4r+323WX3+znKaumUfNHomzXuNITQogPfHZ2Vv3DMr+/INu1lUyNnFmMs+C1M4CxY5a4zJscURkRjgbPZTTNbwR8i6XgFwO1ALGGqMudVqVx74GWhrjDmU27KbNW9hvo7d+tffRDFIdxbP/9dfVbVLTHFHKLBTX40t7gh/O85Sup4DlIADlAJp2+pa4uO25Zq+2I4ocisS1vAYm+G5jTsErHdr9ydF8K0npZT6uyr2axRKKaVKNi0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWUrWK/Z3ZxKa03WvT3K521vTTfTrRy3w+LO0KB7X/33uKOUCChgf7FHaHASvNtXPNSOrc6SimlvCbPIwoReR2bHW9jzGNFkkgppVSJYnfqaZvXUiillCqx8iwUxpj/ub8WkQBjzB9FH0kppVRJcslrFCLSWkT2Avus141F5M0iT6aUUqpEyM/F7NeAm4CTAMaYnUCHogyllFKq5MjXt56MMUeyDXIUQRallFIlUH7+juKIiLQBjIiUAf4L/FC0sZRSSpUU+TmiGAQ8CkQBiUAT67VSSqm/gUseURhjUoD7vJBFKaVUCZSfbz3VFpHPRCRZRE6IyFIRqe2NcEoppYpffk49fQzMByKASGAB8ElRhlJKKVVy5KdQBBhjZhlj0q3HbKBcUQdTSilVMtj91lMl6+lKERkOzMX120/3ACu8kE0ppVQJYHcxOw5XYRDr9UC3cQYYUVShlFJKlRx2v/V0hTeDKKWUKpnydeMiEbkGuAq3axPGmJlFFUoppVTJcclCISKjgY64CsUKoDvwNaCFQiml/gbyc0RxF9AY2G6MGSAiVYHZRRurZPoydi8jJy/C6XTSr0dr/vtgV4/xFy6mMWTMLHbtO0JocCDvjxtAdGRl1n+3j7HTl5GWnk4ZPz9iHutJhxb1vJp97bd7GTFpIQ6nk/t7tuGJ/jmzDx49ix37DlMpOJAPXnyI6MjKAEz+cBWzl8Xi6+PDy0Pv4obWV2nufOjcOIqXHmiJj48w+6sfmbpst8f4qMqBTB/cnuBAf3x9hLGfxLF2x6/UCKtA7KQ7+DkxFYBtPyczdEasV7Nv3LKPcW98isPppPfNLRl47w0e47fs/IXx05ey/0ASU57rR/frG2eOW7xqK2/OXgvAkH430uuma72Wu6Cf0d9SzzFg+Ax2/JBAn1ta8srTvb2W2T37qCmLcWRkf6BLjuyPjpnNzv1HqBQUyHvj+mduX154cxlp6Q7K+PkS85/bad+ibqFmy8/XY/80xjiBdBEJAk4ANS41kYg4RGSHiOwUkXjr96LyattORLaIyD7r8S+3cTEictSa114R6Ztt2ietaXZby5ps/SZVoXI4nDzz6gLmvTaYb+aOYvHqOPYfSPJoM2dZLCEVA9i6aDSD+nRizPSlAFQKCWTOpIFs+ngk00f3Y0jMrMKOd8nsT0+Yz4KpQ9g8/1kWrY5jX7bss5bGEhxUnvglMQy+txMxr7uy7zuQxOI18cTOG8XCaUMY+sp8HA6n5r4EHxEmDGhF71dW02boEnq1qU29qGCPNk/d0Zilmw/SacQyHpm2nlcfapU57tDxM3QcsYyOI5Z5vUg4HE5ipi7m/ZcfYeWHw/h83XZ+OnTMo01k1VBeeaYPt93Q1GP46d//4PWZq1k4/b8sevO/vD5zNalnvHMbm7/yGS3r78eIgbcQ89gdXsmancPhZPjEBcydMohvPhnJktVx7D+YPftmQoIC2LrweQb17cjY6csAa/sycSAb54zgjef7MWRM4W9f8lMotolICPAerm9CxQP5WXP/NMY0McY0xvUNqZdyayQi1XD9Ud8gY0x9oB0wUERucWs2xRjTBOgJvJNRCERkENAVaGWMaQhci6uQlc9HvssSvzeBK6qHUSsqDP8yftzRpTkrN3ruIa7cuJs+t7QEoEfnJmza+iPGGBrVq0FEuGsjUb92BOcvpHHhYlphR8xT3J5D1K4RRq3qruy9ujRjxYZd2bLvoq+VvWfnpmzYuh9jDCs27KJXl2aU9S9DzagwatcII27PIc19Cc3+EcbBY2dIOHGWNIeTJbEH6N4i2qONMVCxvD8AFQP8OXbqT6/ls7Nr32FqRlUmOrIy/mX8uKVzU778do9Hm+rVKlG/TiTiIx7DN23dR9vmdQkJCiC4YgBtm9dl45Z9Xsn9Vz6jgeXL0qpJHcr55+uybaGL35tArerhmdlv79IsZ/ZNu7nn5usAuK1TEzZty9q+VCvi7cslC4UxZogx5rQx5m2gC/CgMWbAZS4nCDiVx7hHgY+MMfHW8lKAYcDwXLL8BPwBhFqDRgGDjTGnrfEXjTEvG2N+v8x8l5R04jSRVUMzX0dWCSEp+bRnm+RUoqqEAODn50tQhfL8lnrOo81n63bQqF51yvoX+kFPnpKSU4lyz141lKTkVI82iSey2rhnzzFtlZzTau6cIkIDOHoy6/8+8eQfRIQGerSZsGg7d7erw+43ejNvWBeGf7Q5c1x0eAW+eqkHy57vTqt6Vb2WG+BYSioR1noMUC0smOP57LvjKalEhLtNGx7C8RQvrS+F9BktDknJpzNzQUZ2z347lpxKVFX37OVybl++2kGjuoW/fbH7g7tmduMyNuw2yovIDlzflIoAOufR7mrgf9mGbbOG55bpJ2PMCes0WAVjzMFL5Cgx9h1IYuz0ZSyYNqS4o6gSoFeb2nyy8SfeXL6HFleG89aQDrQdtoTjp/+g8X8WcOrsBRpfUZlZT91A26eXcOZP7x2FqtJn34EkXpi+jPlTC3/7YndEMcnmMTEf88449VQf6AbMFBG51ER5eEJE9gDfAeNzayAiN1nXMQ7ldj1ERP4lIttEZFtKSvJlB4ioEkLi8ayDosQTpz32nAAiwoM5esK1B5Oe7uD3s39SKdi1F5l4/BQPDHuP6aPv54rq4Ze9/L8iIjyYo+7Zj5/KPBWWIbJKVhv37DmmPZFzWs2dU9KpP4iqnHUEEVk5gKRTnnt//TpdyaexhwDY9lMyZcv4UrliOS6mOzl19gIAOw+e5ODx36kTEeSt6FQLCybpRNae+LGUVKrms++qhgV77MUfSz5N1TAvrS9/8TNanCLCQzJzQUZ2z36rFh7M0ePu2c9nbV9OnOLBZ97njeeLZvuSZ6EwxnSyeeR1dJDXvGKBMCBcRMZbG/Qd1ui9QPNskzQH3E+KTjHGXA3cCcwQkXLW6aWzInKFtYxV1nWM7wH/XDK8a4xpYYxpERZ2+R3ZtEE0B44kk5CYwsW0dJasiaNbh4Yebbq1b8jc5d8BsGzdDtq3qIuIkHrmD/o++TbPP9qDlo29/8O7za6qyS+Hk0k46sq+eE083Ts0ypH9Eyv70nXb6XCtK3v3Do1YvCaeCxfTSDiawi+Hk2l+dS3NfQnbf0mhdrUgosMrUMbXhzta12ZlnOeNIn9NOcf110QAUDcymHL+vqT8fp7KFcviY+1T1axSgTrVgjh0/IzXsjesX4NDR1M4knSSi2npLF+3nRta5zjAz1X7a+vzzbYfST3zB6ln/uCbbT/S/tr6RZzY5a98Rotb0wbRHDySTEKiq88/XRNPt/bZs1/DvBVbANcppnYtrszcvtz75Ds8N6Toti9ijCmaGYucNcZUsJ7Xx/W3F1WNMY5s7SJwHSn0MMbsEJHKwBfAWGPMZyISA5w1xky02i8FVhhj3hGRIUAPoI8x5rR1xLIGGGeMWZ9XtmbNW5hNsVsv+z2t+WYPo6Yswuk03HtbK54ccBMvvbOcJg2i6d6hIecvpDEkZia7f/yVkKAA3hs3gFpRYUz64Aum/m8NtWtkFagF0x4lvFLFy87g61OwlXr1N3sYOXkhDofhvh6tGPpQN158+3OaNIjm5usbcf5CGoNGz2TX/iOEBgUyY/wAalUPA2DiB18wZ9lm/Hx9ePHJO+nSNn8bjcJQEnJX7vthgaa7sUl1xj9wHb4+wsfrf2Lyp7sYfldTdhxM4Yu4I9SLCmbKI20JLFcGYwwxH29j/e5EbruuJsPvbkpauhOngVcWbmdVfPa7EefP/nfvLdB06zf/wPg3P8XhMNzV/TqG9LuR1z78goZ1q3ND22vYte8wQ57/iN/P/klZfz/CQiuy8sNhACxY+R1vz/kSgMH33chd3a+77OWHBubY18uXgn5GAZrePpoz586TlpZOUIUAFk4bQr3aEZedoaDb1DXf7uHZKYtxOp30vdWV/eV3l9OkfjTdMrKPmcXuH38lNCiAd1/ob21fVjFt5hqucN++TB1y2duXDm2uIz5uW64bmKIsFA4g47K9ACONMcvzaNsB1ymtilbb14wxb1njYvAsFM1xfUuqAa7fnBoK/BO4AJwFvsFVKPK8glbQQlESFLRQqIIraKEoCQpaKIpbQQtFSVBU29SiZlcoiuy7YMYY38touxHXV1tzGxeT7XUc4P7Xaq9aD6WUUkUgP3e4ExHpJyLPW6+jReTyjyWVUkqVSvn5g7s3gdZAxl9EnwGmF1kipZRSJUp+Tj21NMY0E5HtAMaYUyJSek8gKqWUuiz5OaJIExFfXBeOEZFwwHs/mqOUUqpY5adQTAOWAFVEZDyur7m+WKSplFJKlRiXPPVkjJkjInHADbi+unq7MeaHIk+mlFKqRMjPjYuicf0Q32fuw4wxh4symFJKqZIhPxezl+O6PiG4fuDvCmA/ufxon1JKqf9/8nPqyeMHR6xfcNWfP1VKqb+J/FzM9mD9vHjLIsiilFKqBMrPNYon3V76AM2AxCJLpJRSqkTJzzUK958gTMd1zWJR0cRRSilV0tgWCusP7SoaY4Z6KY9SSqkSJs9rFCLiZ907oq0X8yillCph7I4otuC6HrFDRJYBC4DMezkaYxYXcTallFIlQH6uUZQDTgKdyfp7CgNooVBKqb8Bu0JRxfrG0/dkFYgMpfMWTkoppS6bXaHwBSrgWSAyaKFQSqm/CbtCkWSMGeu1JF5WWm89XVrvxytSSjscSJz5QHFHKLBqPacUd4QCOb7syUs3KqFK75qeN7u/zP7/+H6VUkpdJrtCcYPXUiillCqx8iwUxpjfvBlEKaVUyXTZPwqolFLq70ULhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULS0USimlbGmhUEopZUsLhVJKKVtaKJRSStnSQqGUUsqWFgqllFK2tFAopZSypYVCKaWULbtboaps1sbuZeSkRTicTu7v2ZrHH+zqMf7CxTQGx8xi574jhAYH8sH4AURHVgZgykermb0sFl8fH1566i5uaN2gVGT/7fQ5+o+Ywfa9CfS9tSUTnu7t3dzf7mXEpIVW7jY80T+X3KNnsWPfYSoFB/LBiw9l9vnkD1dl9vnLQ+/ihtZXeTX7us0/8Nxri3E4nNx3Wyv+80CXbNnT+c8Ls9ll9fk7LzxIdERl4vcm8PQr8wDXrW+HPtyNm69v7LXcNzSvxUsDO+PrI8xatZvXFmzxGF+jShCvP34TYcEBnDpznoGvLifx5FkAFoy9k2vrR7B571H6xCzxWuYM62L38mxGn/dozWM5+jyNf4/N6vN3x/V39fmeBIa+Mhdw9fnTD3fn5o7e6/OM7KOs7P1ssu/cd4RKbtnXb9nHuDeXkZbmoEwZX0b/+3bat6hbqNm8dkQhImdtxrUTkS0iss96/MttXIyIHBWRHSKyV0T6Zpv2SWua3SKyU0Qmi0iZws7vcDgZNmEB86cOJnbeKBatimPfgSSPNrOXxRJSMYC4xaMZ3LcTMW8sBWDfgSQWr47j27kjWTB1ME9PmI/D4SzsiEWSvWxZP0YOvIWxj93htbzuuZ+eMJ8FU4ewef6zLFqdM/espbEEB5UnfkkMg+/tRMzrbn2+Jp7YeaNYOG0IQ1/xfp+PmLiAjycNZOPHI1iyNp79B495tPn4s1hCKpZn84LnGHhPR8a9+RkA9WtHsGrGU3z5v2F8MnkQT78yn/R0h1dy+/gIrw65kbufX0SrQR9y5/X1qVejskebsQ9fz9wv99Lu0f8x4ZNveX5A+8xxry/ayqCJK7ySNTuHw8nwSQv4ePIgNn0ykiVr4th/0HN9+fizzYRUDOC7hc8zsE9HXpi+DID6dSJY/cFQ1s18hrlTBjN0wjyv9XlG9mcmLeCTyYP4+pORLM4l+5zPNhNcMYAt2bJXDg5k9qsD2TBnBK8/149Hx8wq9HzFfupJRKoBHwODjDH1gXbAQBG5xa3ZFGNME6An8E5GIRCRQUBXoJUxpiFwLXACKF/YOeP2JHBF9TBqRYXhX8aPXl2bs3Ljbo82Kzbsps8tLQHo2bkJG7f+iDGGlRt306trc8r6l6FmVBhXVA8jbk9CYUcskuyB5cvSqkkdypb1/sFn3J5D1K4RRq3qVu4uzVixYZdHm5Ubd9E3M3dTNmzdjzGGFRt20atLs8w+r10jjLg9h7yWffveBK6oHk5Nq89vv7EZqzZ59vmqTd/Tu/t1ANzaqTFfb3P1eUA5f/z8fAE4fzEd8eLd65vXrcaBxFMkHEslLd3J4o37uLl1HY829aIrs2nnYQA27TxC91b/yBy3cedhzvyZ5r3AbuKtPq/l1udfZFvPv9i0m943u/r8tk5N8u5zvNjpuWS/I4/s97hl32Rlb1ivBtXCgwHXTsb5C2lcuFi4/wfFXiiAR4GPjDHxAMaYFGAYMDx7Q2PMT8AfQKg1aBQw2Bhz2hp/0RjzsjHm98IOmZR8mqiqoZmvI6uEkJR8OlubVKKqhgDg5+dLUIXy/JZ6Ll/TFqW/kr04uTK55a4aSlJyqkebxBNZbTz7PNu0VXJOW5SSklOJtPoTICI8JMfyk5JPE+mWvWJgucw+j99ziA73vUSn+19mwrDemRuxohZRuSJHU85kvk5MOUtE5YoebfYcTObWtlcCcGubKwkKKEtoxXJeyWfnWPJpIqtk9XlklRCO5ehzz/W8YoWsPo/bc4gO975Ix34v8aoX+zwje5Rb9ogqOdeXYzbZM3z+1Q4a1qtOWf/CPalSEgrF1UBctmHbrOEeRKQZ8JMx5oSIBAEVjDEH87MQEfmXiGwTkW0pKcl/ObRSRanZ1bXYOGcEX8x4imkz13L+QvHspefmuffX0/aa6mx4/X7aNqzO0ZQzOJymuGP9Zc2vrsXGj0ey6oOhTJ25pkT1eX7sO5DE2DeXMfGZewp93iWhUOTHEyKyB/gOGJ9bAxG5ybqOcUhE2mQfb4x51xjTwhjTIiws/LIDRISHcPT4qczXiSdOExEekq1NMEePu/bU09Md/H72TyoFB+Zr2qL0V7IXJ1cmt9zHTxFhHWJniKyS1cazz7NNeyLntEUpIjyYxONZR21JyadzLD8iPIREt+xnzp3P0ed1a1UjsHzZHNdmikrSyTNEhWUdQUSGVSDp5BmPNsd+O8cD45dx/X9mMe5/XwPw+7kLXslnp1p4CIknsvo88cTpzFMyGbKv52fO5tHnAd7rc3BlP+qWPelEzvWlmk32xBOn6D/8fd547n6uqH7527dL8XqhEJHx1gZ9hzVoL9A8W7PmwB6311OMMVcDdwIzRKScdXrprIhcAWCMWWVdx/ge8C/s3M2uiubAkWQSjqZwMS2dxavj6Na+oUeb7h0aMnf5dwAsXbeD9i3qIiJ0a9+QxavjuHAxjYSjKRw4kkzzq2sWdsQiyV6cml1Vk18Ou+VeE0/3Do082nRr35BPMnNvp8O1rtzdOzRi8Zr4zD7/5XAyza+u5bXsTRpEc+DXZBIST3IxLZ1P18bTtd01Hm26tr+G+Std3yj6/KudtG1+JSJCQuLJzAupR5J+4+fDx6kRUckrueN/PEadyFCiqwZTxs+HXh3qs3LzLx5tKgWVz7xu8kTvlsxZ/b1Xsl1K0wbWeu7W5zdlW89vancN81e4+vyzr3bQLq8+T/Ben+eWfUke2eflkj31zB/c+9Q7PDukBy0b1y6SfGKMdw4ZReSsMaZCLsMjcB0p9DDG7BCRysAXwFhjzGciEgOcNcZMtNovBVYYY94RkSFAD6CPMea0uLZsa4Bxxpj1eWVp1ryF+Wbz1st+D2u+2cPIyYtwOA333daKpx66iRffWU7TBtF079CQ8xfSGDR6Jrt//JXQoADeHz+AWlFhAEz6YBVzPtuMn68P45/sRZc2Oc6sFam/kr1xz9GcOXeetLR0gioGsGjaEOrXjris5Re06Kz+Zg8jJy/E4TDc16MVQx/qxotvf06TBtHcfH2jzNy79h8hNCiQGeMHUKu6K/fED75gzjJXn7/45J10aVuwPr+QVrBvv6z9dg/PT12Cw+Gk762teLx/V155bwVN6tfgpvauPv/32Nl8/+OvhAQF8M7YB6kZFcaClVt5ffZayvj54iPCkwNuovv1jS69wFxU6znlsqfp0uIKXhzYCV8fH+as3s2ked8xol9bdvx0jJXf/UKPtnV5vn97DIZvv/+Vp6d/yUVrI7tiQh+urFGJwHJl+O3MeR57bRXr4g9ddobjy5687GnA1efPvbYYh9PV50/0v4lX3l1O4wbRdMvo8zGz2J3R5y/0p1ZUGAtWbuH1WWvxy+jzh7pxcwH7vKC7V2u/3eP6aq/Tyb1W9pffXU4Tt+yPWtlD3bJP/nAV02au4YoaWUcS818bQnilinkvLBfXt72O+LhtucYv9kJhjesATAIq4urn14wxb1njYvAsFM1xfUuqAWCAocA/gQvAWeAbXIUizyuXBS0UquCK++jkryhooSgJClIoSoKCFoqSoLSu6XaFwmvfecyrSFjjNuL6amtu42KyvY4D6rkNetV6KKWUKgKl5WK2UkqpYqKFQimllC0tFEoppWxpoVBKKWVLC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsqWFQimllC0tFEoppWxpoVBKKWVLgM7FhwAAGZlJREFUC4VSSilbWiiUUkrZ0kKhlFLKlhYKpZRStrRQKKWUsuW1O9yVJELpvjWn8q4yvqV3f+rY0ieKO0KBVL3hueKOUGC/rR9X3BEKXen9BCillPIKLRRKKaVsaaFQSillSwuFUkopW1oolFJK2dJCoZRSypYWCqWUUra0UCillLKlhUIppZQtLRRKKaVsaaFQSillSwuFUkopW1oolFJK2dJCoZRSypYWCqWUUra0UCillLKlhUIppZQtLRRKKaVs/S1vhVpQa7/dy4hJC3E4ndzfsw1P9O/qMf7CxTQGj57Fjn2HqRQcyAcvPkR0ZGUAJn+4itnLYvH18eHloXdxQ+urNPv/49wAX8buZcTkRTidTvr1aM3jD+bMPmTMLHbuO0JocCAzxg0gOrIyv6We4//au/fwKMqzj+PfOwnHQA6YgOEMViVQICaooBJFhIIgIApWsB57CXh6pQJ9gQrxgGiEqlVpbdEWRKtQ4gsWKGBpgQoqSTgJBg+RYEOEoEDEE7C53z9mck6GgJtdVu7PdeUiO/PMzG+HJ3vvzOw+c9v/vsjmD/L4+aCLSZ84MqC5Ada88wEPPp2Bz1fM6Gt6cu/N/SplP869jyxgm5v9hUduoW3CWWTvzGPiE68DoKpMuGMAV1/ePWC5+150LjPvHUR4WBgvL8vk6VfXVZjfpkUMz/56OHExkRws+oYxMxaxt7AIgANrHmFn7j4A/rv/EKOmLAhYboC3Nu5kyuzFbl+vvr+MSyvrLy/NcPvLoa+5dfKLbN6Zx42D66a/BOSIQkR8IrJFRLaKSLaIXFJDuzQRmVDN9GEisk1EPhCR7SIyrNL8CSKS425jk4jc7O/n4PMVMzF9IYueuYt3Fv6GxauyyMktqNDm5SUbiY5qRPYbaYwb1Ye0Z5cAkJNbQMbqbDa+PpW//e4uJjyxEJ+v2N8Rf3TZQzV3SfZJTy5i4dPj2PDaVDKqyb5g6UZimjYmc/F0xv28Dw8972RvUD+CyWMG8dB91wYsb3k+XzGTZy3i1dljWPfqZN54K5tdn35eoc2rb24kpmkj3ln0IGNuuIJH57wJQKeOCax88QH+OW8Sf/3tWCY+sZDjx30ByR0WJjx5/zWMmDSPnrc8w3V9u3F+u/gKbR6+awCvrdzMZbc/S/q8fzHtzrIX42+/P0bqL58j9ZfPBbxI+HzFTEpfxMJnxrHx9aksXllzf8nKmM64G/uQ9pzbXxpEMGXMIB6uw/4SqFNP36pqkqp2ByYDM2u7oIh0B2YBQ1U1ERgCzBKRbu78sUA/4CJVTQL6AuLvJ5C1Yzcd28TRvnUc9etFMLxfMsvXbqvQZsW6bdw46GIAhl55AWs37UJVWb52G8P7JdOgfj3atYqjY5s4snbs9nfEH132UM0NkL0zjw6t42jfysl+bb8UVqzbXin7dn7uZh9yZRLrNn2IqhLZqAE9k86hQf3gHPBv3plHh9bxtHOzD7sqmZXrK2Zfuf59Rg68CIDBfbrzn0wne+OG9YmICAfgu6PHEb//JdYsJbE1uflfkldwkGPHfWSs2cbVlyVWaHN+u+asz84FYP3mXAZemljdqgIua0fF/jK8f9X+snxtWX8ZWl1/aVB3/SUY1yiigIMn0X4C8Jiqfgrg/jsTmOjOnwKMU9Uid36Rqs7zY14ACgoP06pFbOnjli1iKSg8XKHN3v1lbSIiwolq0ogvD39dddnmVZetS6GaPVRzAxTsP1Rp+zEUFB6q2KbwMC2bxwAVswdbQeFhWraIKX2cEB9TZd8VFB6iZbn93jSyYWn27B27SR09kz6/eJz0SSNLC0ddS4iLIn9/Wc69hUUkxEVXaLPjk88ZnOqcghzcuzNRkQ2JjWoEQMP6Eax54S5WzRlTpcDUtYLC2vWXVi2C018C9ZalkYhsARoCCcCVJ7FsF5wjivIygbtFJApoqqq5/olpjPmhkru0Z90rk/lw9+fc98grXNmzMw0b1At2LAAenLOC9PuvYdTAZDZs3U3+/sP4ihWAbjfMouBAEe0SYln61B3szN3H7r1fBjnx6SHQp546AQOA+SKBPCgFEblTRDJFJLPwQOFJL58QH03+vrIDob37DpIQX/HdSsvmZW2OH/dRdORbmkVHVl12f9Vl61KoZg/V3AAJzWMqbf8QCfExFdvER7N3v/OusXz2YEuIj2bvvrJ3swWFh6rsu4T4GPaW2+9fff1dlezntT+byEYNqpxrrysFB4po1bwsZ8v4KAoOVDwS+vyLr7j5wVe5/JfP8+jc1QAUHfmudHmAvIKD/GfLp3Q7NyEgucHZn7XpL/n7gtNfAn7qSVU3AnFAvIjMcC9Ab/FYZCeQUmlaCrDDPd10REQ61mK7f1TVHqraIz4u/kTNq0ju3I5P9hSSl3+Ao8eOk7E6m4Gp3Sq0GdC7K39d9i4AS9ZsJvXC8xARBqZ2I2N1Nt8fPUZe/gE+2VNISpf2J53hVIVq9lDNDXBBYltyPyskb6+T/Y3VWQxM7Vol+2tu9qVrttC7h5M92JIS25L730Ly9n7B0WPH+b+3sul/2U8rtOnf+6csXPEeAH//11YuTTkXESFv7xelF68/K/iSj/fso01Cs4Dkzs7J55zWZ9H27FjqRYQz/MpurHg7p0KbZtGNS/fx+NGX88qKLACimzSkfr3w0jYXd23Lrt37A5IbILmz219K+vqqLAb0rthfBqaW9ZclAe4vAb9aJiKdgHDgC1WdCkw9wSKzgEUiskZVd4tIe5zrEte782cCz4vIDapaJCJNgOGqOt+fuSMiwkmfNJLr7nsen08ZPaQnieck8Ngf/k5SYluuvrwbvxh6CWOnzyf52jRioyJ5ccZtACSek8Cwqy6g58gZRISH8eSkkYSHB65Gh2r2UM1dkv2JCSMYcd8cfMXKqGt60qljAjNfWEZSYlsGpnblpiG9GJc2nx7XPURMVGPmPnpb6fJJw6bz1dffcezYcZav3c7ffncXnToG5h1uREQ4j/3qOm4c/3t8vmJuHOxkf+JPy0nq1Iaf9e7KqME9uefhBfQc8QgxUY154eFbAHhvay7PLniLehHhhInw+AMjOCumSUBy+3zFTHr6TRbPupXwMOGV5dnk7N7P5Nv7siUnnxUbcrgsqQPT7uyPKmzYupuJTy8FnIvcT00YSnGxEhYmPP3KOnblnfyZh1MVERFO+sQRXO/2l9HXuH39hWVcUK6/jJ0+n5ThDxEb1Zi5M8r6S/ehZf1l2drtLPZzfxFV9dvKatyIiA8ouYQvwBRVXVZNuzTgfuBIyTRVbS0iw4GHgHrAMWC6qma4ywjOhe073HnHgNmqWuPn21JSeujb72b64ZmZM0Fxcd3/jdSVYwH8SLA/nX3VtGBHOGVf/vvRYEc4JZf2vJDsrMxqD1ECckShqrX62IOqpgFp1UzPADJqWEaBdPfHGGOMn9kQHsYYYzxZoTDGGOPJCoUxxhhPViiMMcZ4skJhjDHGkxUKY4wxnqxQGGOM8WSFwhhjjCcrFMYYYzxZoTDGGOPJCoUxxhhPViiMMcZ4skJhjDHGkxUKY4wxnqxQGGOM8WSFwhhjjCcrFMYYYzxZoTDGGOMpILdCNf4TiHuc1wXn1uahKSwsdLM3CKvVXYhPOwfXzgh2hFMWe+E9wY5wSr7ftafGeXZEYYwxxpMVCmOMMZ6sUBhjjPFkhcIYY4wnKxTGGGM8WaEwxhjjyQqFMcYYT1YojDHGeLJCYYwxxpMVCmOMMZ6sUBhjjPFkhcIYY4wnKxTGGGM8WaEwxhjjyQqFMcYYT1YojDHGeLJCYYwxxpMVCmOMMZ7sVqgn4a0NO5k8+2/4iov5xdBLGH9r/wrzvz96jHHTX2ZLzh6aRUfy0mO307blWQD89s8rWbB0I+FhYTw+4Xr69uoc2OwbdzJl9mI3ey/uv6Wa7GkvszXnM2KjI3lpxm2l2Z/6y6rS7DMfuJ6+vRIDlzuU97llD3j2UM0N0LdXIjMfuJ7wsDBeXrKBp+etrjC/zdmxPDvtJuJimnCw6BvGTJvH3v2HAEi7Zyj9L+sCwJMv/oM3Vmf7NVtQjihE5EgN09NEZEI104eJyDYR+UBEtovIsErzJ4hIjohsEZFNInKzvzP7fMVMTF/Iomfu4p2Fv2HxqixycgsqtHl5yUaioxqR/UYa40b1Ie3ZJQDk5BaQsTqbja9P5W+/u4sJTyzE5yv2d0TP7JPSF7HwmXFsfH0qi1dWzb5g6UZimjYmK2M6427sQ9pz5bKvymLDa1NY9Mw4JqYHLnuo73PLHtjsoZobnPuyPzlpJCP+Zw49Rz7Kdf1TOL/D2RXaPPw/1/Lasve4bNRM0ueuYNrdQwDof2kXunVqQ+/Rj3PVrbO456a+NI1s6N98fl1bHRCR7sAsYKiqJgJDgFki0s2dPxboB1ykqklAX0D8nSNrx246tomjfes46teLYHi/ZJav3VahzYp127hx0MUADL3yAtZu2oWqsnztNob3S6ZB/Xq0axVHxzZxZO3Y7e+IHtnz6NA6jvat3Oz9U1ixbnuFNsvXbufnpdmTWLfpQ1SVFeu2M7x/Smn2Dq3jyNqRF6DcobzPLXugs4dqboCULu3J/ewAeflfcOy4j4zV2Vx9ebcKbc7vmMD6zF0ArM/8kIGpXZ3pHc5mw+aP8fmK+ea7o+z4KN/vR/2nfaEAJgCPqeqnAO6/M4GJ7vwpwDhVLXLnF6nqPH+HKCg8TKsWsaWPW7aIpaDwcIU2e/eXtYmICCeqSSO+PPx11WWbV122LhUUHqq0/RgKCg9VanOYVi1igMrZT7xs3eUO5X1u2SGw2UM1N0BCfDT5+w6W5dx3kIT46AptdnyYz+A+SQAM7tOdqCaNiI2O5P2P8rmqVyKNGtSjWXQkvXucV+G5+EMoXKPognNEUV4mcLeIRAFNVTU38LGMMSZwHnzmDdInjWDU4IvZsPlj8vcdxOcr5l/v5pDcuR0rX3qAAwePsGn7p/iK/XvaLBSOKPxCRO4UkUwRySw8UHjSy9em4rdsXtbm+HEfRUe+pVl0ZNVl91ddti4lxMdU2v4hEuJjKrWJJn+fc6RQMfuJl6273KG8zy07BDZ7qOaG2h0NfX7gMDdPmsvlNz3Bo3PeBKDoyLcAzP7zSlJHP87we55DED7J2+/XfEEtFCIyw70AvcWj2U4gpdK0FGCHe7rpiIh0PNG2VPWPqtpDVXvEx8WfdNbkzu34ZE8hefkHOHrsOBmrsxmYWvEc4oDeXfnrsncBWLJmM6kXnoeIMDC1Gxmrs/n+6DHy8g/wyZ5CUrq0P+kMpyq5c1tyPyuXfVUWA3p3rdBmYGpXXivNvoXePZzsA3p3JWNVVmn23M8KSenSLkC5Q3mfW/ZAZw/V3ADZO/M4p208bVueRb2IcIb3S2bFuorXV5pFRyLiXH4df+vPeOXNdwDnQnhsdCQAXX7Ski7ntmTNuzl+zRfUU0+qOhWYeoJms4BFIrJGVXeLSHuc6xLXu/NnAs+LyA2qWiQiTYDhqjrfn1kjIsJJnzSS6+57Hp9PGT2kJ4nnJPDYH/5OUmJbrr68G78Yegljp88n+do0YqMieXHGbQAknpPAsKsuoOfIGUSEh/HkpJGEhweuRkdEhJM+cQTX3zcHX7Ey+ho3+wvLuCCxLQNTu3LTkF6MnT6flOEPERvVmLkVsifT64bHiAgPI33SiIBlD/l9btkDmj1Uc0PJJxMXsvh3dxMeLryy9B1ycj9n8phBbPlgDyvWbeeylHOZdvcQVGHD5o+ZmL4QgHoR4Sz/4/0AfPX1d9w5bZ7fP7ElqurXFdZqoyJHVLVJNdPTgPuB0o/PqmprERkOPATUA44B01U1w11GcC5s3+HOOwbMVtUFNW0/JaWHvv1upv+eUAAF4//LH0reCRnzYxd74T3BjnBKvt+1kOJv9lf7hxqUQhFsVigCzwqFOVP8GAvFGXMx2xhjzKmxQmGMMcaTFQpjjDGerFAYY4zxZIXCGGOMJysUxhhjPFmhMMYY48kKhTHGGE9WKIwxxniyQmGMMcaTFQpjjDGerFAYY4zxZIXCGGOMJysUxhhjPFmhMMYY48kKhTHGGE9WKIwxxniyQmGMMcbTGXkrVBEpBPLqcBNxwIE6XH9dCdXcELrZQzU3WPZgqMvc7VQ1vroZZ2ShqGsikqmqPYKd42SFam4I3eyhmhssezAEK7edejLGGOPJCoUxxhhPVijqxh+DHeAUhWpuCN3soZobLHswBCW3XaMwxhjjyY4ojDHGeLJC4Sci0lpElojIRyLyiYg8IyL1g5TFJyJbRGSriGSLyCU1tEsTkXy37fsiMqSa6SU/MSJyhYgcdh/niMisOnwOR0Ilc233t9v2MhF5z82SIyJ31vDcdorIjZWW/ZW7zHZ3W78VkXp+yF/tvj5d85Zb/8n08wnVTB8mIttE5AM347BK8ye4+beIyCYRudlf2cttw6ufnz6ZVdV+fuAPIMB7wG3u43DgReDJIOU5Uu73nwFra2iXBkxwf0/E+Xx2WPnpldpfAfzd/b0RkANcWtfP4XTPfBL7+2xgD5DsPo4DsoBB1Ty3c4EioJ77eCzwDyDGfVwf+F8gqg739WmZ94f083LTugMfAx3cxx3cx93K5V9ZkheIAm4JRj8/HTLbEYV/XAl8p6p/BlBVHzAeuF1EGgc1mdNZDp6okap+ABzHeTE4IVX9FtgCtPpB6X6A0zSz1/6+G/iLqma7eQ4Ak3BeQCtQ1Y+Ab4BYd9JUYJyqHnLnH1XVx1W1yM/5QzVvrfp5OROAx1T1UwD335nARHf+FJz8Re78IlWd58e8pyJomSP8sRJDF5x3WqVUtUhE9gA/AbYFOE8jEdkCNAQScAqZJxG5GCgGCt1J40XkJvf3g6rap1L7WJx3kev8lvoknUaZa7u/uwCV/3Az3ekViEgy8JGq7heRKKBJyQtEAJ3ueU+6n5fTBah8GjITuNvN31RVc/0T02+CltmOKH6cvlXVJFXtBAwA5ouI1NB2vPvHNgu4Qd1jVuApdx1JlV5we4vIViAfWKmqn9fZs6jZ6Zb5ZPb3iYwXkR3Au8CM6hqIyM/cc9C7va6HBEgw8/pzvxsPVij8YyeQUn6CW+Hb4pxDDBpV3YhzaiZeRGaUXOgt16TkxbW3qq6vxSrXq2p3nHc3d4hIUl3kLhFqmU+wv6v0E/fxjnKPn1LVLsB1wIsi0tA9lXBERDq421ipqknA+zjn/v0i1PKWV4t+XlmNz61c/o51kbU6p3tmKxT+8U+gccknDEQkHJiNc373m2AGE5FOOBfXv1DVqSXvuH/oet3TCo8Dv/6h6zrBdkIq8wn29/PArSWFSkTOAp4A0qvJuhTntMIt7qSZwO9FJMZdVnBOufhNqOUt7xT6+Sxgsoi0d5dvj3OOf7Y7fybwvPuGDxFpUhefeipxume2axR+oKoqItcCc0TkQZwCvBznPzEYGpV7ZyI4n3zwneQ6yp/vBxhWTZs/ABNEpL2q7j6FnP4WrMy12t+qWuDm+5OINHXbPq2qb9aw3oeBV0XkT8DvgUjgXRH5HjgCvA1s9tNzqCIE8p5MP/+NiNxf8kBVW4vIr4E3xfnI7jFgkqqWrO/3QBNgk4gcc+fPrrzSOnbaZLZvZhtjjPFkp56MMcZ4skJhjDHGkxUKY4wxnqxQGGOM8WSFwhhjjCcrFOaMImUjjr4vIot+yFhcIvIXEbne/X2uiHT2aHvFqXwr2f02c5WxrGqaXqlNjaPC1tC+2hFLjbFCYc40JcM+/BQ4ijPiZikROaXvFqnqL1V1p0eTK4BgD7dhzCmxQmHOZOuBn7jv9teLyFJgp4iEi8iT4oznv01ExoDz7WIReU5EdonIW0DzkhWJyL9FpIf7+wBx7o+wVUT+6X6DdizuGFUi0ltE4kVksbuNTSJyqbvsWSKySkR2iMhcnC+SeRKR/xORLHeZOyvNe8qd/k8RiXennSMi/3CXWe9+q9mYGtk3s80ZyT1yGIhzzwSAZOCnqvqp+2J7WFUvFJEGwNsisgq4ADgf6Ay0wBl756VK640H/gSkuutqpqpfisgfcO49MMtt9yrOOEn/EZG2OPcRSASmA/9R1YdFZBBwRy2ezu3uNhrhfCt3sap+gfPN6ExVHS8i09x134Nz3+WxqvqROCPwzuHkRl41ZxgrFOZMU37Yh/U4N5i6BHiv3LDY/YFuJdcfgGic4clTgb+6w0TsFZE11ay/J7Cu3D0Dvqwhx1VAZykb7DRKRJq42xjuLrtMRGpzj4X73CFkANq4Wb/AGYL9dXf6AiDD3cYlwKJy225Qi22YM5gVCnOm+bbywGvuC+bX5ScB96rqykrtrvZjjjCgp6p+V02WWhORK3CKTi9V/UZE/k3Ng++pu91D/hhk0Zw57BqFMVWtBMa5A68hIueJSCTODY9ucK9hJAB9qln2HSBV3CG2RaSZO/0roGm5dquAe0seSNnQ5+uAUe60gZTdMa4m0Tg3afrGvdbQs9y8MKDkqGgUzimtIuBTERnhbkNEpPsJtmHOcFYojKlqLs71h2wReR94Aefo+w3gI3fefGBj5QVVtRC4E+c0z1bKTv28CVxbcjEbuA/o4V4s30nZp68ewik0O3BOQe05QdZ/ABEi8gHOEOrvlJv3NXCR+xyuxBndFWA0zn05tuLcW2JoLfaJOYPZ6LHGGGM82RGFMcYYT1YojDHGeLJCYYwxxpMVCmOMMZ6sUBhjjPFkhcIYY4wnKxTGGGM8WaEwxhjj6f8BUBc+NYAmfzAAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["모델이 B-ORG와 I-ORG를 가장 많이 혼동하는 경향을 보임"],"metadata":{"id":"et1XMNfoyyVo"}},{"cell_type":"markdown","source":["토큰 수준에서의 오류를 조사했으니 높은 손실을 내는 시퀀스를 확인. <br>\n","explode() 함수를 적용하기 전의 원래 DataFrame을 사용하여 토큰당 손실을 합산해 총 손실을 계산"],"metadata":{"id":"jIxU_vpQy7nn"}},{"cell_type":"code","source":["def get_samples(df):\n","  for _, row in df.iterrows():\n","    labels, preds, tokens, losses = [], [], [], []\n","    for i, mask in enumerate(row[\"attention_mask\"]):\n","      if i not in {0, len(row[\"attention_mask\"])}:\n","        labels.append(row[\"labels\"][i])\n","        preds.append(row[\"predicted_label\"][i])\n","        tokens.append(row[\"input_tokens\"][i])\n","        losses.append(f\"{row['loss'][i]:.2f}\")\n","    df_tmp = pd.DataFrame({\"tokens\": tokens, \"labels\": labels, \"preds\": preds, \"losses\": losses}).T\n","    yield df_tmp\n","\n","df[\"total_loss\"] = df[\"loss\"].apply(sum)\n","df_tmp = df.sort_values(by=\"total_loss\", ascending=False).head(3)\n","\n","for sample in get_samples(df_tmp):\n","  display(sample)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":490},"id":"gQ8-n0qQzErD","executionInfo":{"status":"ok","timestamp":1675822882048,"user_tz":-540,"elapsed":789,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"14c60418-8e39-4c8b-83de-e1190849dea6"},"execution_count":54,"outputs":[{"output_type":"display_data","data":{"text/plain":["          0     1      2      3     4     5      6      7      8      9   \\\n","tokens    ▁'   ▁''     ▁Τ      Κ   ▁''    ▁'     ▁'    ▁''     ▁T    ▁''   \n","labels     O     O      O    IGN     O     O  B-LOC  I-LOC  I-LOC  I-LOC   \n","preds      O     O  B-ORG  B-ORG     O     O      O      O  B-ORG      O   \n","losses  0.00  0.00   3.44   0.00  0.00  0.00  10.46  10.07   8.27   9.45   \n","\n","           10    11     12     13    14     15     16    17    18  \n","tokens     ▁'    ri    ▁''     ▁'     k    ▁''     ▁'   ala  </s>  \n","labels  I-LOC   IGN  I-LOC  I-LOC   IGN  I-LOC  I-LOC   IGN   IGN  \n","preds       O     O      O      O     O      O      O     O     O  \n","losses  10.15  0.00   9.87  10.14  0.00  10.09  10.17  0.00  0.00  "],"text/html":["\n","  <div id=\"df-72930e0e-11fd-4ed7-92ec-d45d8be47482\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      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<td>I-LOC</td>\n","      <td>I-LOC</td>\n","      <td>IGN</td>\n","      <td>I-LOC</td>\n","      <td>I-LOC</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","    </tr>\n","    <tr>\n","      <th>preds</th>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-ORG</td>\n","      <td>B-ORG</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-ORG</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>losses</th>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>3.44</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>10.46</td>\n","      <td>10.07</td>\n","      <td>8.27</td>\n","      <td>9.45</td>\n","      <td>10.15</td>\n","      <td>0.00</td>\n","      <td>9.87</td>\n","      <td>10.14</td>\n","      <td>0.00</td>\n","      <td>10.09</td>\n","      <td>10.17</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72930e0e-11fd-4ed7-92ec-d45d8be47482')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 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\n","losses   8.55  0.00  0.00   8.53   7.80   8.47      7.80  0.00   9.04   9.79   \n","\n","              10     11          12     13      14     15     16    17  \n","tokens  ▁Gelände   ▁der  ▁Republika      n  ischen   ▁Gar     de  </s>  \n","labels     I-ORG  I-ORG       I-ORG    IGN     IGN  I-ORG    IGN   IGN  \n","preds          O      O       B-ORG  I-ORG   I-ORG  I-ORG  I-ORG     O  \n","losses      8.70   7.25        5.87   0.00    0.00   0.01   0.00  0.00  "],"text/html":["\n","  <div id=\"df-9c98675f-efbe-4a63-b85c-c60c864d67cd\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      <th>11</th>\n","      <th>12</th>\n","      <th>13</th>\n","      <th>14</th>\n","      <th>15</th>\n","      <th>16</th>\n","      <th>17</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>tokens</th>\n","      <td>▁''</td>\n","      <td>8</td>\n","      <td>.</td>\n","      <td>▁Juli</td>\n","      <td>▁''</td>\n","      <td>▁:</td>\n","      <td>▁Protest</td>\n","      <td>camp</td>\n","      <td>▁auf</td>\n","      <td>▁dem</td>\n","      <td>▁Gelände</td>\n","      <td>▁der</td>\n","      <td>▁Republika</td>\n","      <td>n</td>\n","      <td>ischen</td>\n","      <td>▁Gar</td>\n","      <td>de</td>\n","      <td>&lt;/s&gt;</td>\n","    </tr>\n","    <tr>\n","      <th>labels</th>\n","      <td>B-ORG</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>IGN</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","      <td>I-ORG</td>\n","      <td>IGN</td>\n","      <td>IGN</td>\n","    </tr>\n","    <tr>\n","      <th>preds</th>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>O</td>\n","      <td>B-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>O</td>\n","    </tr>\n","    <tr>\n","      <th>losses</th>\n","      <td>8.55</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>8.53</td>\n","      <td>7.80</td>\n","      <td>8.47</td>\n","      <td>7.80</td>\n","      <td>0.00</td>\n","      <td>9.04</td>\n","      <td>9.79</td>\n","      <td>8.70</td>\n","      <td>7.25</td>\n","      <td>5.87</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","      <td>0.01</td>\n","      <td>0.00</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c98675f-efbe-4a63-b85c-c60c864d67cd')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 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6.26      5.70    6.25         0.00      6.13   0.00     5.89   \n","\n","             7         8      9      10        11        12         13     14  \n","tokens  ization  ▁Mission    ▁in   ▁the  ▁Central  ▁African  ▁Republic   </s>  \n","labels      IGN     I-PER  I-PER  I-PER     I-PER     I-PER      I-PER    IGN  \n","preds     I-ORG     I-ORG  I-ORG  I-ORG     I-ORG     I-ORG      I-ORG  I-ORG  \n","losses     0.00      5.74   5.50   5.81      5.95      5.88       5.63   0.00  "],"text/html":["\n","  <div id=\"df-e5beb8ee-9da5-4be0-adb0-d8392d472a73\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: 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<td>I-PER</td>\n","      <td>IGN</td>\n","      <td>I-PER</td>\n","      <td>IGN</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>I-PER</td>\n","      <td>IGN</td>\n","    </tr>\n","    <tr>\n","      <th>preds</th>\n","      <td>B-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","      <td>I-ORG</td>\n","    </tr>\n","    <tr>\n","      <th>losses</th>\n","      <td>6.26</td>\n","      <td>5.70</td>\n","      <td>6.25</td>\n","      <td>0.00</td>\n","      <td>6.13</td>\n","      <td>0.00</td>\n","      <td>5.89</td>\n","      <td>0.00</td>\n","      <td>5.74</td>\n","      <td>5.50</td>\n","      <td>5.81</td>\n","      <td>5.95</td>\n","      <td>5.88</td>\n","      <td>5.63</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5beb8ee-9da5-4be0-adb0-d8392d472a73')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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predict() 메서드를 사용해 다른 언어로 전이되는 능력을 평가"],"metadata":{"id":"J2fULCVG11Ff"}},{"cell_type":"code","source":["def get_f1_score(trainer, dataset):\n","  return trainer.predict(dataset).metrics[\"test_f1\"]"],"metadata":{"id":"HilLTf3k19gR","executionInfo":{"status":"ok","timestamp":1675823275711,"user_tz":-540,"elapsed":514,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":55,"outputs":[]},{"cell_type":"markdown","source":["테스트 세트의 성능을 평가"],"metadata":{"id":"rAGw8lNS2gwz"}},{"cell_type":"code","source":["f1_scores = defaultdict(dict)\n","f1_scores[\"de\"][\"de\"] = get_f1_score(trainer, panx_de_encoded[\"test\"])\n","print(f\"[de] 데이터셋에서 [de] 모델의 F1 점수: {f1_scores['de']['de']:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"3Xxms1Eq2i85","executionInfo":{"status":"ok","timestamp":1675823425440,"user_tz":-540,"elapsed":18895,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"9b8c1ea4-a2a2-4d2d-f01e-af6e9398b8a6"},"execution_count":57,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[de] 데이터셋에서 [de] 모델의 F1 점수: 0.867\n"]}]},{"cell_type":"code","source":["# 프랑스어에서 점수 확인\n","text_fr = \"Jeff Dean est informaticien chez Google en californie\"\n","tag_text(text_fr, tags, trainer.model, xlmr_tokenizer)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"E_6LJ02q3DUC","executionInfo":{"status":"ok","timestamp":1675823477858,"user_tz":-540,"elapsed":722,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"567ca3c6-2f71-41d3-f85f-e38b489897fb"},"execution_count":58,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           0      1      2      3     4            5    6      7        8   \\\n","Tokens    <s>  ▁Jeff    ▁De     an  ▁est  ▁informatic  ien  ▁chez  ▁Google   \n","Tags    I-PER  B-PER  I-PER  I-PER     O            O    O      O    B-ORG   \n","\n","         9      10     11     12     13  \n","Tokens  ▁en  ▁cali    for    nie   </s>  \n","Tags      O  B-LOC  I-LOC  I-LOC  I-PER  "],"text/html":["\n","  <div id=\"df-04d07ca9-1024-4be4-a31b-069299405aa7\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","    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{f1_scores['de']['fr']:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130,"referenced_widgets":["2ea4ca5a4cae4e34a6110d3e9d88c651","91bda3f45e6c4e85a09ed28d5289c696","bfd5babd9bc84ddfa5c6128295166523","68fb9a2ada674f3d86c8af1f8ca42294","4ea838d6f2f045ff9aa15422ded0e4c9","bb67aae5d54d42c699ccbbc7d3e3d55b","c7c6dae6f18c471c97c554070b51bc4c","9db304751d0a4e25bca672129340ac4a","4e6377999bcd45fd947ffff3204aa56b","2247badab8cd4517bcf9e31b1d5714da","cd6a61a9d1ea402e976ed526792cd16d","94ceb13d56594d3f99d3a03abced58cc","d99fd5f7c40443678da1082688c42c65","1bd42bc9adbc4857ae37e1c0b0b0b6c5","c1e2a78143484aea8f371e11c69ab6e3","f8b527cfca1742578a268ad2b607a872","2e16d295cf4e4cbc9d509f9c26e6ee06","65edf3b8401e47eb96a50a2cdbf9a662","4db9ba3f1aff47a99f307d3d9fa41a6d","74dabcf2946a4b6dbf9af8fe2e264ef8","e2a450d30a654f57a00194b9c59b0419","eef29186386d49b8a6db1a4cd2b6534c","1a3cfbc170a245b19ffe0fa947628a7e","94840fc04ab14cd59fee3dc4c604a2a5","7541382dc9c44afdbfa3f41e3633a0e4","566b0e89a9bc4bb5b238178f8d8ae0a6","9bca7b9835e845a3be6371a04c77ad81","edd2a3b1783d48f2be1acfd71f1c8bb4","90f2604c031b4b279e069ea7d2881c1a","24530917c4f54d44a4341f0c84fbab06","7b103fd3144f4eb8b733cb65ec51c91f","4de7e750ea0f407a9b5b205421147d50","ebd1823852cc4db588f99706e590343a"]},"id":"pNGBR4ap3kb0","executionInfo":{"status":"ok","timestamp":1675823696812,"user_tz":-540,"elapsed":6427,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"663bdfa2-adf8-4aef-9be0-6785dfd392c0"},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ea4ca5a4cae4e34a6110d3e9d88c651"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"94ceb13d56594d3f99d3a03abced58cc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1a3cfbc170a245b19ffe0fa947628a7e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[fr] 데이터셋에서 [de]모델의 F1 점수: 0.696\n"]}]},{"cell_type":"code","source":["f1_scores[\"de\"][\"it\"] = evaluate_lang_performance(\"it\", trainer)\n","print(f\"[fr] 데이터셋에서 [de]모델의 F1 점수: {f1_scores['de']['it']:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":130,"referenced_widgets":["195e10dbbb5b48ab9e398a66758dde13","d35d0b468d914833a56e4fcd52aca4e8","d146b5d96e45412d97f5079b140930e6","4461809827d74d86ad4eedb80d42942d","ba555485343d4f399028b953501f71ed","f96d049dddc142e78dbc2d63de5f8901","69080edf86894d588459022e04b3a423","71116a1c3046472ebd41de2a251c9b4f","c109c485393a4ca0873a72868a39936c","5af63ad216444ffbbbcd529af95cb22b","635fc65327a5438594ce1473362e8200","930adae80e9d4e9999acee0c4fc323e4","56d8530176d140c19704c0bd74613441","90f26840fb2c46448d33fcea56bad086","64a431cd1ace4a6f94a07a6a5cb703a6","b310cb126eb7460cb424ac80ffded79e","9701bf14e9784508bb3f12295aba919e","235ace930bb44693b9fe92a781bcf376","197ebb4e52f24882af7bd0d39095aea6","60908eeb9bad47ad9165e195b2cf92f9","08d772af60f6440a802b0b412b287698","e9de5adfeec2456fbb81305ecd597480","eacf66ae9ce54d368c4418c72c421860","aa3195e2c70d4e709cdcf50c49e8dca4","6ad96368e58c43b688eb5d41df83878f","31dcedf6b7d34474a5bd0f95ddb6837c","fa1d95ea5db140a1b8f79ff4ad1f9773","438c35fceffa4c0e96151cc1e28ac273","0ca091f5848644c189ed0c13be310b0e","0e6981685a534d8a9577361131ed4b5e","b8831dc27dbe4052b94d94c214ca947e","61447819912d4ff399f1e411dbce3229","36933e871b114325836b7a7f5d3408ca"]},"id":"XZq7n6UW4GYQ","executionInfo":{"status":"ok","timestamp":1675823725336,"user_tz":-540,"elapsed":2847,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"e43e74b3-0e36-4861-8a33-ad5a7fc17c8e"},"execution_count":60,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"195e10dbbb5b48ab9e398a66758dde13"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"930adae80e9d4e9999acee0c4fc323e4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eacf66ae9ce54d368c4418c72c421860"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[fr] 데이터셋에서 [de]모델의 F1 점수: 0.656\n"]}]},{"cell_type":"code","source":["f1_scores[\"de\"][\"en\"] = evaluate_lang_performance(\"en\", trainer)\n","print(f\"[en] 데이터셋에서 [de]모델의 F1 점수: {f1_scores['de']['en']:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":106},"id":"aYeah7_J4MVv","executionInfo":{"status":"ok","timestamp":1675823768887,"user_tz":-540,"elapsed":1802,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"483cc49b-a88f-435c-8c9f-ed059e44873f"},"execution_count":62,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-67b25ecca7eb6275.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-47a0aa212a54f269.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-38556abcbf0fe146.arrow\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[en] 데이터셋에서 [de]모델의 F1 점수: 0.594\n"]}]},{"cell_type":"markdown","source":["## 제로샷 전이가 유용할때\n","프랑스어 말뭉치로 훈련 세트의 크기를 증가시키면서 XLM-R을 미세 튜닝해 독일어 XLM-R로 교차 언어 전이한 것보다 나은 결과를 내는 훈련 세트 크기를 구한 후 실제로 레이블링된 데이터를 더 많이 수집해야 하는지 판단"],"metadata":{"id":"XKOxtsBn4YAF"}},{"cell_type":"code","source":["def train_on_subset(dataset, num_samples):\n","  train_ds = dataset[\"train\"].shuffle(seed=42).select(range(num_samples))\n","  valid_ds = dataset[\"validation\"]\n","  test_ds = dataset[\"test\"]\n","  training_args.logging_steps = len(train_ds) // batch_size\n","\n","  trainer = Trainer(model_init=model_init, \n","                    args=training_args, \n","                    data_collator=data_collator,\n","                    compute_metrics=compute_metrics,\n","                    train_dataset=train_ds,\n","                    eval_dataset=valid_ds,\n","                    tokenizer=xlmr_tokenizer)\n","  trainer.train()\n","  \n","  f1_score = get_f1_score(trainer, test_ds)\n","  return pd.DataFrame.from_dict({\"num_samples\": [len(train_ds)], \"f1_score\": [f1_score]})"],"metadata":{"id":"yNJ8zAd346gI","executionInfo":{"status":"ok","timestamp":1675824167014,"user_tz":-540,"elapsed":514,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":63,"outputs":[]},{"cell_type":"markdown","source":["독일어 말뭉치 미세튜닝처럼 프랑스어 말뭉치를 입력 ID, 어텐션 마스크, 레이블 ID로 인코딩"],"metadata":{"id":"QMDf11Wr59rN"}},{"cell_type":"code","source":["panx_fr_encoded = encode_panx_dataset(panx_ch[\"fr\"])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["30c6a6fbd2944377ace47d48bc4a4aea","6af4cd75f04b4a6abed43abd8f3b30a3","dbf5ba978d06434ab1910c2f8e932f48","a18cc967159849caac2877ce9b3dc6a2","66200da357d74e4fb357f1f561d01cad","0f439a2b3fba46f3a60f1ca4762becee","472c551e7eba4817affcb192af57e973","1822ae06b53641028266009060997372","c2bc8eb70e324e83906f1990f9033cad","3b0af0519c10406ea44b7f4bb812f6a1","92da5753833b4007b2194cbe6715c4ff"]},"id":"-mMv0ERZ6EAB","executionInfo":{"status":"ok","timestamp":1675824242235,"user_tz":-540,"elapsed":1284,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"22dd73df-4199-4203-f4a9-b7f83b136485"},"execution_count":64,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"30c6a6fbd2944377ace47d48bc4a4aea"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-6c6f6ff4a59e7dd1.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-bd9403b1b214a85b.arrow\n"]}]},{"cell_type":"code","source":["metrics_df = train_on_subset(panx_fr_encoded, 250)\n","metrics_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":288},"id":"fAIY-gam6KWH","executionInfo":{"status":"ok","timestamp":1675824313205,"user_tz":-540,"elapsed":35927,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"bcf8e919-39fe-4664-cf9b-787f0a37e2f9"},"execution_count":65,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='33' max='33' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [33/33 00:23, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.797900</td>\n","      <td>1.382164</td>\n","      <td>0.110526</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>1.306500</td>\n","      <td>1.131393</td>\n","      <td>0.170438</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>1.084300</td>\n","      <td>1.035612</td>\n","      <td>0.185041</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["   num_samples  f1_score\n","0          250  0.167937"],"text/html":["\n","  <div id=\"df-347f26f3-1d25-41fc-a308-d29fee38594c\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>num_samples</th>\n","      <th>f1_score</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>250</td>\n","      <td>0.167937</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-347f26f3-1d25-41fc-a308-d29fee38594c')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-347f26f3-1d25-41fc-a308-d29fee38594c button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [63/63 00:30, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.492500</td>\n","      <td>1.051098</td>\n","      <td>0.185071</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.899900</td>\n","      <td>0.705176</td>\n","      <td>0.569553</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.587300</td>\n","      <td>0.584122</td>\n","      <td>0.618641</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-6b17d58cd0d6e4ba.arrow\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='126' max='126' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [126/126 00:46, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.205300</td>\n","      <td>0.606873</td>\n","      <td>0.550045</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.481400</td>\n","      <td>0.425944</td>\n","      <td>0.702297</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.330100</td>\n","      <td>0.390302</td>\n","      <td>0.704059</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-6b17d58cd0d6e4ba.arrow\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='252' max='252' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [252/252 01:19, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.805700</td>\n","      <td>0.408441</td>\n","      <td>0.737765</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.331000</td>\n","      <td>0.350425</td>\n","      <td>0.780576</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.217100</td>\n","      <td>0.333629</td>\n","      <td>0.810279</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-6b17d58cd0d6e4ba.arrow\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='501' max='501' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [501/501 02:24, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.605400</td>\n","      <td>0.337464</td>\n","      <td>0.781681</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.271200</td>\n","      <td>0.285458</td>\n","      <td>0.813073</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.182100</td>\n","      <td>0.287294</td>\n","      <td>0.833361</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}}]},{"cell_type":"code","source":["fig, ax = plt.subplots()\n","ax.axhline(f1_scores[\"de\"][\"fr\"], ls=\"--\", color=\"r\")\n","metrics_df.set_index(\"num_samples\").plot(ax=ax)\n","plt.legend([\"Zero-shot from de\", \"Fine-tuned on fr\"], loc=\"lower right\")\n","plt.ylim((0,1))\n","plt.xlabel(\"Number of Training Samples\")\n","plt.ylabel(\"F1 Score\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"vgzTqZib7Dij","executionInfo":{"status":"ok","timestamp":1675824867616,"user_tz":-540,"elapsed":729,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"3a949aca-49c3-4812-96ea-8f03f2e063c4"},"execution_count":67,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 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l7rOAWdXm3Rsz7cBt4atZWFFQCKA7AhFpMRpyR/Cimf0O6Ghm3wHeAJ6Mb1jJa2nYUTxEHcUi0kLUe0dgwdNSM4ABwC6gP3Cvu/9vE8SWlJbnF9KjUyYdM9MTHYqISKOoNxGETUKz3D0XiOzJP9ay/EKG91D/gIi0HA1pGlpkZqPiHkkzsK1oPwU796lZSERalIZ0Fp8KXG1m/yT45pAR3CwMiWtkSWh5ftBRPESlJUSkBWlIIjg/7lE0E8vyCzGDwfrGkIi0IIdsGnL3fwIdgYvCV8dwXuQsy99J387taNdawziISMvRkCeLvw88Dxwbvp4zs5vjHViycXeWFRQyRHcDItLCNOTS9nrgVHffA2BmDwDzgcfiGViy+XRXMVt371dHsYi0OA351pABZTHvy8J5kbIs7CjWGAQi0tI05I7g98B7Zvbn8P3FwH/GL6TktCx/J6kpxqCu7RMdiohIozpkInD3X5jZHOD0cNZ17r44rlEloWX5hZx0XBYZrVITHYqISKM6ZCIwszHASndfFL5vb2anuvt7cY8uSbg7ywsKmTDo+ESHIiLS6BrSR/A4UBTzviicFxmbP9/Hzr0lGpFMRFqkBnUWh+WiAXD3chrWt9BiVFQcHaqOYhFpgRqSCDaY2S1m1ip8fZ9mMIpYY1peUEh6WgonHXfI8XhERJqdhiSCG4DTCMYbLiCoPTQ5nkElm6Wbd3Jyl/akpzVoiGcRkWalId8a2kIw3nAklZc7KwoK+cqIbokORUQkLuq8xDWz75jZieG0mdnTZlZoZsvM7JSmCzGxNmwrYs+BMg1NKSItVn1tHd8HNoXTVwJDgT4E4ws/Gt+wksfSzcETxRqsXkRaqvoSQam7l4TTXwaecfft7v4G0OAB7Ju75QWFZKan0rdzu0SHIiISF/UlgnIz62JmGcAXCAatr9AmvmElj6X5OxnctQOpKZErryQiEVFfIrgXyCNoHprp7isBzOxMIvL10ZKyclZ9vEsPkolIi1bnt4bc/VUz6wlkufuOmEV5wBVxjywJrPlsN/tLy1V6WkRatHq/PurupcCOavP2xDWiJKIxikUkCvSEVD2W5heSlZFGr+zMRIciIhI3SgT1WF6wkyHdOmCmjmIRabmOKBGY2YDGDiTZFJeU8cEnu9UsJCIt3pHeEbzeqFEkoQ8+3U1puWuwehFp8ersLDazX9W1CGjxl8nLwtLTQ/REsYi0cPV9a+g64HZgfy3LroxPOMljWX4h2W3T6dohI9GhiIjEVX2JYAGwwt3nVV9gZvfFLaIksSxfHcUiEg319RFcBiypbYG7945POMlhz/5S1m0pIlcdxSISAfUlgnbuvrfJIkkiKz/eRbnDUD1RLCIRUF8i+EvFhJm90gSxJI2KjmLVGBKRKKgvEcQ2jvc5ko2b2QQz+9DM1pnZXfWs9xUzczMbeST7aWzL8gvp0iGDY7PUUSwiLV99icDrmG4QM0sFpgEXAAOBK81sYC3rZREMgvPe4e4jXpYXFGpEMhGJjPoSwVAz22Vmu4Eh4fQuM9ttZrsasO3RwDp33+DuB4AXgEm1rPcT4AGg+LCjj4PCfSVs3LZHI5KJSGTUmQjcPdXd27t7lrunhdMV79s3YNsnAJtj3ueH8yqFYx93d/e/1bchM5tsZnlmlrd169YG7PrIrSgIKo7qjkBEoiJhRefMLAX4BcFDa/Vy9yfcfaS7j+zcuXNc41pa8USxOopFJCLimQgKgO4x77uF8ypkAYOBOWa2CRgDzEx0h/Hy/EJ6dMqkY2Z6IsMQEWky8UwEC4ATzay3maUDXwNmVix090J3z3H3Xu7eC3gXmOjueXGM6ZCW5RfqbkBEIiVuiSAc3ewm4DVgNfCiu680s/vNbGK89ns0thXtp2DnPiUCEYmUeoeqPFruPguYVW3evXWse1Y8Y2kIDU0pIlGkEcpiLM3fiRkM1jeGRCRClAhiLM8vpG/ndrRrHdcbJRGRpKJEEHJ3lqqjWEQiSIkg9OmuYrYV7dfQlCISOUoEoaWbw45ilZYQkYhRIggtL9hJWooxsEtDqmeIiLQcSgShZfmFnHRcFhmtUhMdiohIk1IiIOgo1hPFIhJVSgTAR5/vpXBfiR4kE5FIUiIgaBYCVRwVkWhSIiAYozg9LYWTjstKdCgiIk1OiYDgjuDkLu1JT9PhEJHoifyZr6zcWVFQyFA1C4lIREU+EWzcVsSeA2UamlJEIivyiaDiiWINVi8iURX5RLC8oJDM9FT6dm6X6FBERBIi8olgaf5OBnftQGqKJToUEZGEiHQiKCkrZ9XHu8hVR7GIRFikE8Gaz3azv7RcD5KJSKRFOhFojGIRkYgngqX5hWRlpNErOzPRoYiIJEykE8Hygp0M6dYBM3UUi0h0RTYRFJeU8cEnu9UsJCKRF9lEsPqTXZSWu8YoFpHIi2wiWF6gMYpFRCDCiWDp5kKy26bTtUNGokMREUmoyCYCdRSLiAQimQj27C9l3ZYictVRLCISzUSw8uNdlDsag0BEhIgmgmX5OwFUY0hEhMgmgkK6dMjg2Cx1FIuIRDQR7NSIZCIiocglgsK9JWzavlcjkomIhCKXCCoeJNMdgYhIIK6JwMwmmNmHZrbOzO6qZfltZrbKzJaZ2Ztm1jOe8QAsKwg6ijUGgYhIIG6JwMxSgWnABcBA4EozG1httcXASHcfArwM/Dxe8VRYtrmQntmZdMxMj/euRESahXjeEYwG1rn7Bnc/ALwATIpdwd1nu/ve8O27QLc4xgMETUNqFhIROSieieAEYHPM+/xwXl2uB/5e2wIzm2xmeWaWt3Xr1iMOaFvRfgp27mOonigWEamUFJ3FZvZ1YCTwYG3L3f0Jdx/p7iM7d+58xPupGJpSD5KJiByUFsdtFwDdY953C+dVYWbnAvcAZ7r7/jjGw9L8nZjBYDUNiYhUiucdwQLgRDPrbWbpwNeAmbErmNlw4HfARHffEsdYgOCOoG/ndrRrHc/8JyLSvMQtEbh7KXAT8BqwGnjR3Vea2f1mNjFc7UGgHfCSmS0xs5l1bK4x4mFpfqG+NioiUk1cL43dfRYwq9q8e2Omz43n/mN9uquYbUX7NTSliEg1kWkjWbpZQ1NK9JSUlJCfn09xcXGiQ5EmkpGRQbdu3WjVqlWDPxOZRLB+axFpKcbALu0THYpIk8nPzycrK4tevXppNL4IcHe2b99Ofn4+vXv3bvDnkuLro03he2f3Y8E955LRKjXRoYg0meLiYrKzs5UEIsLMyM7OPuw7wMgkAoBj2qqshESPkkC0HMnvO1KJQEREalIiEJG4+fOf/8ywYcOqvFJSUvj732utJtPo5syZw5e//OXD+swjjzzC3r17a1321ltvMWjQIIYNG8a+ffsaI8QGadeuXVy3r0QgInFzySWXsGTJksrXjTfeyPjx4zn//PMb9Hl3p7y8PM5RVlVfInj++ee5++67WbJkCW3atKmcX1pa2lThxYUSgUiUnHVWzddvfhMs27u39uXTpwfLt22ruewwrFmzhvvvv59nn32WlJTg1PPggw8yatQohgwZwo9+9CMANm3aRP/+/fnmN7/J4MGD2bx5M3fccQeDBw8mNzeXGTNm1Lr9f/zjH5V3HcOHD2f37t0AFBUVcdlllzFgwACuvvpq3B2AN998k+HDh5Obm8u3vvUt9u/fz69+9Ss+/vhjzj77bM4+++wq23/qqad48cUXmTp1KldffTVz5sxh/PjxTJw4kYEDB1JcXMx1111Hbm4uw4cPZ/bs2QBMnz6diy++mPPOO49evXrx61//ml/84hcMHz6cMWPG8Pnnn9f4WTZu3MjYsWPJzc3lhz/8YZVltR2zo6VEICJxV1JSwlVXXcXDDz9Mjx49AHj99ddZu3Yt77//PkuWLGHhwoXMnTsXgLVr13LjjTeycuVK8vLyWLJkCUuXLuWNN97gjjvu4JNPPqmxj4ceeohp06axZMkS3nrrrcor9sWLF/PII4+watUqNmzYwDvvvENxcTHXXnstM2bMYPny5ZSWlvL4449zyy230LVrV2bPnl15Iq/w7W9/m4kTJ/Lggw/y/PPPA7Bo0SIeffRR1qxZw7Rp0zAzli9fzh//+Eeuueaaym/vrFixgj/96U8sWLCAe+65h8zMTBYvXszYsWN55plnavws3//+95kyZQrLly+nS5culfPrO2ZHIzLPEYgIMGdO3csyM+tfnpNT//J6TJ06lUGDBnHFFVdUznv99dd5/fXXGT58OBBcua9du5YePXrQs2dPxowZA8Dbb7/NlVdeSWpqKscddxxnnnkmCxYsYOLEiVX2MW7cOG677TauvvpqLr30Urp1C4Y3GT16dOX0sGHD2LRpE1lZWfTu3ZuTTjoJgGuuuYZp06Zx6623HtbPNXr06Mrv67/99tvcfPPNAAwYMICePXuyZs0aAM4++2yysrLIysqiQ4cOXHTRRQDk5uaybNmyGtt95513eOWVVwD4xje+wZ133lnvMTvjjDMOK+7qlAhEJK7mzJnDK6+8wqJFi6rMd3fuvvtuvvvd71aZv2nTJtq2bXvI7U6bNo0nn3wSgFmzZnHXXXdx4YUXMmvWLMaNG8drr70GQOvWrSs/k5qa2qjt+Q2Js3oMKSkple9TUlLqjKe2r4HWdcyOlpqGRCRuduzYwXXXXcczzzxDVlZWlWXnn38+Tz/9NEVFRQAUFBSwZUvNIsTjx49nxowZlJWVsXXrVubOncvo0aP53ve+V9kJ3bVrV9avX09ubi533nkno0aN4oMPPqgzrv79+7Np0ybWrVsHwLPPPsuZZ54JQFZWVmX/wuEYP358ZZPRmjVr+Oijj+jfv/9hbweCu5sXXngBoHKb0PBjdrh0RyAicfPb3/6WLVu2MGXKlCrz7777bq644gpWr17N2LFjgeArks899xypqVWf/r/kkkuYP38+Q4cOxcz4+c9/zvHHH19jX4888gizZ88mJSWFQYMGccEFFzB//vxa48rIyOD3v/89X/3qVyktLWXUqFHccMMNAEyePJkJEyZU9hU01I033siUKVPIzc0lLS2N6dOnV7kTOByPPvooV111FQ888ACTJh0c4feLX/xircfs2GOPPaL9VLCKHvTmYuTIkZ6Xl5foMESahdWrV3PyyScnOgxpYrX93s1sobuPrG19NQ2JiEScEoGISMQpEYiIRJwSgYhIxCkRiIhEnBKBiEjEKRGISFylpqZWKUO9adMmTjvttEbZ9s6dO/lNRdG8ODvrrLOIx1fXr7zySoYMGcIvf/nLRt92Q+mBMhGJqzZt2rBkyZIq8+bNm9co265IBDfeeGOjbK+pffrppyxYsKDyCedYpaWlpKU1zSlaiUAkIn7815Ws+nhXo25zYNf2/OiiQYf9uXbt2lFUVMScOXO47777yMnJYcWKFYwYMYLnnnsOM2PhwoXcdtttFBUVkZOTw/Tp06tU4gS46667WL9+PcOGDeO8887jwgsv5KGHHuLVV18F4KabbmLkyJFce+219OrVi2uuuYa//vWvlJSU8NJLLzFgwAD27NnDzTffzIoVKygpKeG+++5j0qRJ7Nu3j+uuu46lS5cyYMCAOgeiefPNN/nBD35Q+YTy448/TuvWrevcX6wvfvGLFBQUMGzYMB577DGmTp3KsGHDKgvt3X777Yd9bI+EmoZEJK727dtX2Sx0ySWX1FheW5nokpISbr75Zl5++WUWLlzIt771Le65554an/3Zz35G3759WbJkCQ8++OAhY8nJyWHRokVMmTKFhx56CICf/vSnnHPOObz//vvMnj2bO+64gz179vD444+TmZnJ6tWr+fGPf8zChQtrbK+uctb17S/WzJkzK+MfP348AAcOHCAvL6/JkgDojkAkMo7kyr0x1NY0FKu2MtEdO3ZkxYoVnHfeeQCUlZXVuBs4EpdeeikAI0aM4E9/+hMQlHaeOXNm5Ym6uLiYjz76iLlz53LLLbcAMGTIEIYMGVJjex9++GG95axr29+hxJbqbipKBCKSULWViXZ3Bg0aVKNo3ObNmytr+d9www1MmDChyvK0tLQqQ1tWDAxTfV+x5ajdnVdeeeWIK4XWp7b9HUpDS1s3JjUNiUjS6d+/P1u3bq1MBCUlJaxcuZLu3btXlp6+4cZEmfgAAAxFSURBVIYbapSM7tmzJ6tWrWL//v3s3LmTN99885D7Ov/883nssccqh7BcvHgxAGeccQZ/+MMfgGCEsdoGkKmvnHVzokQgIkknPT2dl19+mTvvvJOhQ4cybNiwWr9plJ2dzbhx4xg8eDB33HEH3bt35/LLL2fw4MFcfvnllSN51Wfq1KmUlJQwZMgQBg0axNSpUwGYMmUKRUVFnHzyydx7772MGDGixmdjy1nn5uaSkpJSWc66OVEZapEWTGWoo0llqEVE5LAoEYiIRJwSgUgL19yaf+XoHMnvW4lApAXLyMhg+/btSgYR4e5s376djIyMw/qcniMQacG6detGfn4+W7duTXQo0kQyMjIqH9BrKCUCkRasVatW9O7dO9FhSJKLa9OQmU0wsw/NbJ2Z3VXL8tZmNiNc/p6Z9YpnPCIiUlPcEoGZpQLTgAuAgcCVZjaw2mrXAzvcvR/wS+CBeMUjIiK1i+cdwWhgnbtvcPcDwAvApGrrTAL+K5x+GfiCmVkcYxIRkWri2UdwArA55n0+cGpd67h7qZkVAtnAttiVzGwyMDl8W2RmH8Yl4iOTQ7V4k0yyxwfJH2OyxwfJH2OyxwctP8aedS1oFp3F7v4E8ESi46iNmeXV9dh2Mkj2+CD5Y0z2+CD5Y0z2+CDaMcazaagA6B7zvls4r9Z1zCwN6ABsj2NMIiJSTTwTwQLgRDPrbWbpwNeAmdXWmQlcE05fBvyf68kXEZEmFbemobDN/ybgNSAVeNrdV5rZ/UCeu88E/hN41szWAZ8TJIvmJimbrGIke3yQ/DEme3yQ/DEme3wQ4RibXRlqERFpXKo1JCIScUoEIiIRp0RwCGa2ycyWm9kSM8sL53Uys/81s7Xhv8eE883MfhWWzFhmZqfEKaanzWyLma2ImXfYMZnZNeH6a83smtr21Yjx3WdmBeFxXGJmX4pZdncY34dmdn7M/HpLlBxljN3NbLaZrTKzlWb2/XB+UhzHeuJLmuNoZhlm9r6ZLQ1j/HE4v3dYMmZdWEImPZxfZ0mZumKPU3zTzWxjzDEcFs5v8r+VmO2nmtliM3s1fN+0x9Dd9arnBWwCcqrN+zlwVzh9F/BAOP0l4O+AAWOA9+IU0xnAKcCKI40J6ARsCP89Jpw+Jo7x3Qf8oJZ1BwJLgdZAb2A9wZcLUsPpPkB6uM7ARjyGXYBTwuksYE0YS1Icx3riS5rjGB6LduF0K+C98Ni8CHwtnP9bYEo4fSPw23D6a8CM+mKPY3zTgctqWb/J/1Zi9n0b8Afg1fB9kx5D3REcmdjSGP8FXBwz/xkPvAt0NLMujb1zd59L8C2ro4npfOB/3f1zd98B/C8wIY7x1WUS8IK773f3jcA6gvIkDSlRcjQxfuLui8Lp3cBqgifdk+I41hNfXZr8OIbHoih82yp8OXAOQckYqHkMayspU1fs8YqvLk3+twJgZt2AC4GnwvdGEx9DJYJDc+B1M1toQakLgOPc/ZNw+lPguHC6trIa9f3xNqbDjSkRsd4U3nI/XdHkkgzxhbfXwwmuGJPuOFaLD5LoOIZNGkuALQQnyPXATncvrWV/VUrKABUlZeIWY/X43L3iGP40PIa/NLPW1eOrFke8f8ePAP8PKA/fZ9PEx1CJ4NBOd/dTCKqofs/Mzohd6MF9WVJ9BzcZYwIeB/oCw4BPgIcTG07AzNoBrwC3uvuu2GXJcBxriS+pjqO7l7n7MILKAaOBAYmMp7rq8ZnZYOBugjhHETT33Jmo+Mzsy8AWd1+YqBhAieCQ3L0g/HcL8GeC/+yfVTT5hP9uCVdvSFmNeDncmJo0Vnf/LPyjLAee5OBta8LiM7NWBCfZ5939T+HspDmOtcWXjMcxjGsnMBsYS9CkUvGwauz+6iopE/cYY+KbEDa7ubvvB35PYo/hOGCimW0iaLY7B3iUpj6GjdHR0VJfQFsgK2Z6HkHb4INU7VD8eTh9IVU7m96PY2y9qNoZe1gxEVwJbSTo/DomnO4Ux/i6xEz/C0F7JsAgqnZybSDo4EwLp3tzsJNzUCPGZ8AzwCPV5ifFcawnvqQ5jkBnoGM43QZ4C/gy8BJVOzpvDKe/R9WOzhfriz2O8XWJOcaPAD9L5N9KTLxncbCzuEmPYaP+IC3tRfBNi6XhayVwTzg/G3gTWAu8UfGfIvwPNI2gnXQ5MDJOcf2RoFmghKAt8PojiQn4FkGn0jrgujjH92y4/2UENaZiT2j3hPF9CFwQM/9LBN+WWV9x7BsxxtMJmn2WAUvC15eS5TjWE1/SHEdgCLA4jGUFcG/M38374fF4CWgdzs8I368Ll/c5VOxxiu//wmO4AniOg98savK/lWrxnsXBRNCkx1AlJkREIk59BCIiEadEICIScUoEIiIRp0QgIhJxSgQiIhGnRCBHxMzczB6Oef8DM7uvkbY93cwua4xtHWI/XzWz1WY2O2ZebkxVys9jqlS+0cBtTjxUhU8z62pmL9e3TkOZ2XFm9mpYYXOVmc1qjO3Ws79eFlNVVlqGuA1VKS3efuBSM/sPd9+W6GAqmFmaH6zRcijXA99x97crZrj7coLyDZjZdILvdVc5ade3Dw+GYK0+Nnf1dT4mGKO7MdxPUEPn0TC2IY20XYkQ3RHIkSolGD/1X6ovqH5Fb2ZF4b9nmdk/zOy/zWyDmf3MzK4Oa8YvN7O+MZs518zyzGxNWI+looDYg2a2ICwY9t2Y7b5lZjOBVbXEc2W4/RVm9kA4716Ch7b+08wePNQPa2ZzzOwRC8ak+L6ZXRTWg19sZm+Y2XHhetea2a9jjsOvzGxe+PNeFs6vvKoO1/+Tmf2PBbXufx6zz+vDn/99M3uyYrvVdCF4aA8Ad18Wfradmb1pZovCn31SzL4/CGNbY2bPm9m5ZvZOuP/R4Xr3mdmzZjY/nP+dWo5JXb+PLmY2N7yTWmFm4w91fCWxdEcgR2MasCz25NUAQ4GTCcpUbwCecvfRFgy8cjNwa7heL4IaMH2B2WbWD/gmUOjuoyyoGPmOmb0ern8KMNiDEryVzKwr8AAwAthBUEn2Yne/38zOIajtn9fA2NPdfWS43WOAMe7uZvZtguqRt9fymS4ECWcAwZ1CbU1Cwwiqi+4HPjSzx4AyYGr4c+0meBp2aS2fnQbMMLObCJ6E/n14x1EMXOLuu8wsB3g3TJQA/YCvEjwtuwC4KoxxIvCvHCx5PISg1EJbYLGZ/a3avq+n9t/HpcBr7v5TM0sFMmuJW5KIEoEcsfAk8wxwC7CvgR9b4GGZZzNbD1ScyJcDZ8es96IHhdXWmtkGghPpF4EhMXcbHYATgQMEdWGqJIHQKGCOu28N9/k8wcA5f2lgvLFmxEx3IzgBdyGo4VPbvgH+Ev4cqyruGmrxprsXhvGtAnoCOcA/3P3zcP5LwEnVP+jur5lZH4IaWBcQnLAHAzuBf7egWm45QUniiv1vDJvAMLOV4f7dzJYTJOAK/+3u+4B9FvSjjCYodVGhrt/HAuBpC4rm/cXdYz8jSUiJQI7WI8AigiqOFUoJmx3NLIXgRFlhf8x0ecz7cqr+f6xe+8QJasHc7O6vxS4ws7OAPUcW/mGJ3cdjwC/cfWa4//vq+Ezsz2sNWKeMw/y7DJPFH4A/WDDU4RkEo5p1Bka4e4kF1S0zatnf4f4OYtX6+wAIE9CFwHQz+4W7P3M4P5M0LfURyFEJT0IvEjQTVNhE0BQDQXNDqyPY9FfNLCXsN+hDUEjrNWBKeKWJmZ1kZm0PsZ33gTPNLCdsprgS+McRxFNdBw6W+b2mEbZX3QKCuI+xoNzwV2pbyczOMbPMcDqLoCntozC+LWESOJvgLuNwTbJg3N9sgoJoC6otr/X3YWY9gc/c/UmCUbfiMna3NB7dEUhjeBi4Keb9k8B/m9lS4H84sqv1jwhO4u2BG9y92MyeImi6WGRmBmzlYHt2rdz9Ewu+zjmb4Ar2b+7+30cQT3X3AS+Z2Q6C9vvejbDNSu5eYGb/TnAMPgc+IBiNqroRwK/NrOIu7Cl3X2BmG4G/hs09eeHnD9cyguOWA/zE3T+2mMHSCU7yvaj5+zgLuMPMSoAigr4dSWKqPiqSpMysnbsXhXcEfwaedvc/N9G+7wOK3P2hptifJJaahkSS130WjLe7gqAz+kg6uEUOSXcEIiIRpzsCEZGIUyIQEYk4JQIRkYhTIhARiTglAhGRiPv/BwKsiZ4q9l4AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["훈련 샘플이 약 750개일때까지는 제로샷이 앞섬 <br>\n","문서를 일일이 레이블링 하는 비용을 생각했을때 제로샷이 경제적으로 더 우위를 점하고 괜찮음"],"metadata":{"id":"JIalkzgV7gFI"}},{"cell_type":"markdown","source":["# 다국어에서 동시에 미세튜닝하기\n","성능 감소 폭을 줄이는 방법 하나는 다국어에서 동시에 미세튜닝을 하는것"],"metadata":{"id":"sOkBGZr-7_Pv"}},{"cell_type":"code","source":["from datasets import concatenate_datasets\n","\n","def concatenate_splits(corpora):\n","  multi_corpus = DatasetDict()\n","  for split in corpora[0].keys():\n","    multi_corpus[split] = concatenate_datasets([corpus[split] for corpus in corpora]).shuffle(seed=42)\n","  return multi_corpus\n","\n","panx_de_fr_encoded = concatenate_splits([panx_de_encoded, panx_fr_encoded])"],"metadata":{"id":"Vvfd7NRp8ElJ","executionInfo":{"status":"ok","timestamp":1675824873243,"user_tz":-540,"elapsed":2,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}}},"execution_count":68,"outputs":[]},{"cell_type":"code","source":["training_args.logging_steps = len(panx_de_fr_encoded[\"train\"]) // batch_size\n","training_args.push_to_hub = False\n","training_args.output_dir = \"xlm-roberta-base-finetuned-panx-de-fr\"\n","\n","trainer = Trainer(model_init=model_init,\n","                  args=training_args,\n","                  data_collator=data_collator,\n","                  compute_metrics=compute_metrics,\n","                  tokenizer=xlmr_tokenizer,\n","                  train_dataset=panx_de_fr_encoded[\"train\"],\n","                  eval_dataset=panx_de_fr_encoded[\"validation\"])\n","\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":259},"id":"prfwfMh68nhG","executionInfo":{"status":"ok","timestamp":1675825757936,"user_tz":-540,"elapsed":700206,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"db544003-a225-4230-81f4-0f0c4c931d26"},"execution_count":69,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='2145' max='2145' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [2145/2145 11:33, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.290500</td>\n","      <td>0.178259</td>\n","      <td>0.830973</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.146100</td>\n","      <td>0.159958</td>\n","      <td>0.845506</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.094800</td>\n","      <td>0.165646</td>\n","      <td>0.858905</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=2145, training_loss=0.17714588014118043, metrics={'train_runtime': 694.0678, 'train_samples_per_second': 74.171, 'train_steps_per_second': 3.09, 'total_flos': 1140291491923584.0, 'train_loss': 0.17714588014118043, 'epoch': 3.0})"]},"metadata":{},"execution_count":69}]},{"cell_type":"markdown","source":["각 언어의 테스트 세트에서 모델의 성능 확인"],"metadata":{"id":"37XkUPr59WEv"}},{"cell_type":"code","source":["for lang in langs:\n","  f1 = evaluate_lang_performance(lang, trainer)\n","  print(f\"[{lang}] 데이터셋에서 [de-fr] 모델의 f1 점수: {f1:.3f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":359,"referenced_widgets":["40ee5833271c494cb982535613847865","9cd43cf140984ac594dc4449ea856e1a","d2656be4605a408b8960d620155d6872","40c9c94449694fbbaf08b0a4da3a2470","5fa3437f023c44d3887654afac5164f6","5ee625774f254d7bbfdb58fc2b88c032","6e949eae282e4fcfb12a5fa8f678b3f8","aca5de1935654297aa171b16d4ae2117","8ada8e3b825043589a33076d479fa97a","33cc02879cb148b998f9da0fff984862","b1a1ce3dadb3403bb8c20f2ca16e3513","323822f38a9e48a0b69b9e72713ad9fd","5b58cd19a040458785de40b350d0a0a2","afb03e48c9a64e459182c7969105d693","0f3f7f44d66a40ccb0c4c0d409aceff1","78d9f1251e3f43368ef4400057df0e9c","f475fece0cc94fc3875d60e697b712f9","be5245964f2f496792d8513dbd681e42","2d428917487c4f18abc84819b6ddbcde","6a24b1cebe05436da45963e95550b673","d14809e23834488fb4f2a385f0bb03c1","c992e5f51eb94d079c00d433f819606f","d6c483f1f2a74e0b88a864e9524a52f6","4894b9cf2ee5495bafa58b8e78af5497","90c9e7c500534b758f21b5a9567252c6","1dfee2d2e19c40e0b5606e01a4f1b1ac","5207663a59f642c9935f742990337395","521599d1ca3047fbb6736a55a84386a7","345d1df57cd347979e56b8434bbe9579","700d93168336461a9b5c221f06b778a4","a0e796962708476a8d43898e8f9e7eb4","b54b5dd342c147a98f0c859f3e80d807","ea15d48dae7d4f3f810df52a2c445966"]},"id":"epQuCOif9Yk1","executionInfo":{"status":"ok","timestamp":1675825989266,"user_tz":-540,"elapsed":29774,"user":{"displayName":"HanGyo Jung","userId":"11224950994115057744"}},"outputId":"bc69b4b0-bf53-4cb0-c82a-6c49455d40c2"},"execution_count":70,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/13 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"40ee5833271c494cb982535613847865"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"323822f38a9e48a0b69b9e72713ad9fd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/7 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d6c483f1f2a74e0b88a864e9524a52f6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-1dce373a1b796a7e.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-6c6f6ff4a59e7dd1.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.fr/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-bd9403b1b214a85b.arrow\n"]},{"output_type":"stream","name":"stdout","text":["[de] 데이터셋에서 [de-fr] 모델의 f1 점수: 0.869\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-3b79d468ccf0c113.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-cabbeaac000ca811.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.it/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-32c6f859b24f0a36.arrow\n"]},{"output_type":"stream","name":"stdout","text":["[fr] 데이터셋에서 [de-fr] 모델의 f1 점수: 0.866\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-67b25ecca7eb6275.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-47a0aa212a54f269.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/xtreme/PAN-X.en/1.0.0/29f5d57a48779f37ccb75cb8708d1095448aad0713b425bdc1ff9a4a128a56e4/cache-38556abcbf0fe146.arrow\n"]},{"output_type":"stream","name":"stdout","text":["[it] 데이터셋에서 [de-fr] 모델의 f1 점수: 0.795\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["[en] 데이터셋에서 [de-fr] 모델의 f1 점수: 0.702\n"]}]},{"cell_type":"markdown","source":["- 다중 언어 학습은 성능상 큰 이득을 제공, 특히 유사한 언어군에서 데이터가 부족한 언어로 교차 언어 전이를 수행할 때 큰 이득을 얻게 됨\n","- 일반적 전략으로 한국어처럼 다른 종류의 텍스트를 다룰 때는 한 어족(language family) 내에서 교차 언어 전이에 초점을 맞추는 것이 좋음"],"metadata":{"id":"lhF39TZI-UHS"}}]}
\ No newline at end of file
diff --git a/Transformer/03_MultiLanguageDetection/images/NER_Transformer_Structure.png b/Transformer/03_MultiLanguageDetection/images/NER_Transformer_Structure.png
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index 0000000..7b35ad9
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diff --git a/Transformer/03_MultiLanguageDetection/images/Transformer_BodyHead.png b/Transformer/03_MultiLanguageDetection/images/Transformer_BodyHead.png
new file mode 100644
index 0000000..863d81d
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diff --git a/Transformer/jupyternotebook/MotionClassifier.ipynb b/Transformer/jupyternotebook/MotionClassifier.ipynb
new file mode 100644
index 0000000..c62292c
--- /dev/null
+++ b/Transformer/jupyternotebook/MotionClassifier.ipynb
@@ -0,0 +1,1396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2f3c6918",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "현재 허브에는 18210개의 데이터셋이 존재\n",
+      "처음 10개 데이터셋: ['acronym_identification', 'ade_corpus_v2', 'adversarial_qa', 'aeslc', 'afrikaans_ner_corpus', 'ag_news', 'ai2_arc', 'air_dialogue', 'ajgt_twitter_ar', 'allegro_reviews']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import list_datasets\n",
+    "\n",
+    "all_datasets = list_datasets()\n",
+    "print(f\"현재 허브에는 {len(all_datasets)}개의 데이터셋이 존재\")\n",
+    "print(f\"처음 10개 데이터셋: {all_datasets[:10]}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "feed8efa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "83e024c2b83843538cbc56569f44625a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading builder script:   0%|          | 0.00/3.97k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6b4dfeb478949b08ed2a927b108bfbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading metadata:   0%|          | 0.00/3.28k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7bc67236fbb34c579f56e1bb1dd9c1db",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading readme:   0%|          | 0.00/8.78k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No config specified, defaulting to: emotion/split\n",
+      "/Users/hangyojeong/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.2\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset emotion/split to /Users/hangyojeong/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "afe2d3661e3d4d349f96c2c09c9b1421",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2788eed81ab348b3978b3344e043956c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/592k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "71c017cd60da4cd898b4169ab7831d11",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/74.0k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51cc468b21d9492397f3e235a17b9c40",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/74.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1732ed2382cd4f0ca1d2a50461a74457",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split:   0%|          | 0/16000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating validation split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating test split:   0%|          | 0/2000 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset emotion downloaded and prepared to /Users/hangyojeong/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47d3bc05c94244409cdaecfe087fb84c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "emotions = load_dataset(\"emotion\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cbd5df51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 16000\n",
+       "    })\n",
+       "    validation: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['text', 'label'],\n",
+       "        num_rows: 2000\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "emotions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "63910423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['text', 'label'],\n",
+       "    num_rows: 16000\n",
+       "})"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_ds = emotions[\"train\"]\n",
+    "train_ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "29ff03af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'text': 'i didnt feel humiliated', 'label': 0}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_ds[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "100d35c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['text', 'label']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_ds.column_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "dd5f381c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'text': Value(dtype='string', id=None), 'label': ClassLabel(names=['sadness', 'joy', 'love', 'anger', 'fear', 'surprise'], id=None)}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dataset의 속성\n",
+    "print(train_ds.features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "37d99831",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'text': ['i didnt feel humiliated',\n",
+       "  'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
+       "  'im grabbing a minute to post i feel greedy wrong',\n",
+       "  'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n",
+       "  'i am feeling grouchy'],\n",
+       " 'label': [0, 0, 3, 2, 3]}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_ds[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8c00c688",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i didnt feel humiliated',\n",
+       " 'i can go from feeling so hopeless to so damned hopeful just from being around someone who cares and is awake',\n",
+       " 'im grabbing a minute to post i feel greedy wrong',\n",
+       " 'i am ever feeling nostalgic about the fireplace i will know that it is still on the property',\n",
+       " 'i am feeling grouchy']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_ds['text'][:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f6dd1a51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>i didnt feel humiliated</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>i can go from feeling so hopeless to so damned...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>i am feeling grouchy</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label\n",
+       "0                            i didnt feel humiliated      0\n",
+       "1  i can go from feeling so hopeless to so damned...      0\n",
+       "2   im grabbing a minute to post i feel greedy wrong      3\n",
+       "3  i am ever feeling nostalgic about the fireplac...      2\n",
+       "4                               i am feeling grouchy      3"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "emotions.set_format(type=\"pandas\")\n",
+    "df = emotions[\"train\"][:]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "44b1fd35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "      <th>label_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>i didnt feel humiliated</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sadness</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>i can go from feeling so hopeless to so damned...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>sadness</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>im grabbing a minute to post i feel greedy wrong</td>\n",
+       "      <td>3</td>\n",
+       "      <td>anger</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>i am ever feeling nostalgic about the fireplac...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>love</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>i am feeling grouchy</td>\n",
+       "      <td>3</td>\n",
+       "      <td>anger</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label label_name\n",
+       "0                            i didnt feel humiliated      0    sadness\n",
+       "1  i can go from feeling so hopeless to so damned...      0    sadness\n",
+       "2   im grabbing a minute to post i feel greedy wrong      3      anger\n",
+       "3  i am ever feeling nostalgic about the fireplac...      2       love\n",
+       "4                               i am feeling grouchy      3      anger"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def label_int2str(row):\n",
+    "    return emotions[\"train\"].features[\"label\"].int2str(row)\n",
+    "\n",
+    "df[\"label_name\"] = df[\"label\"].apply(label_int2str)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b96c7176",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# plot.barh() : Make a horizontal bar plot\n",
+    "df[\"label_name\"].value_counts(ascending=True).plot.barh()\n",
+    "plt.title(\"Frequency of Classes\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "50520dc3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[\"Words Per Tweet\"] = df[\"text\"].str.split().apply(len)\n",
+    "df.boxplot(\"Words Per Tweet\", by=\"label_name\", grid=False, showfliers=False, color=\"black\")\n",
+    "plt.suptitle(\"\")\n",
+    "plt.xlabel(\"\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "bae0a4c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 데이터셋의 출력 포맷 초기화\n",
+    "emotions.reset_format()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530d7e4a",
+   "metadata": {},
+   "source": [
+    "# 토큰화"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b397f12b",
+   "metadata": {},
+   "source": [
+    "## 문자 토큰화\n",
+    "각 문자를 개별로 모델에 주입 <br>\n",
+    "파이썬의 str 객체 내부는 사실 배열이므로 문자 수준의 토큰화가 손쉽게 가능"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9b5c18a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['T', 'o', 'k', 'e', 'n', 'i', 'z', 'i', 'n', 'g', ' ', 't', 'e', 'x', 't', ' ', 'i', 's', ' ', 'a', ' ', 'c', 'o', 'r', 'e', ' ', 't', 'a', 's', 'k', ' ', 'o', 'f', ' ', 'N', 'L', 'P', '.']\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"Tokenizing text is a core task of NLP.\"\n",
+    "tokenized_text = list(text)\n",
+    "print(tokenized_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "acf0acdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{' ': 0, '.': 1, 'L': 2, 'N': 3, 'P': 4, 'T': 5, 'a': 6, 'c': 7, 'e': 8, 'f': 9, 'g': 10, 'i': 11, 'k': 12, 'n': 13, 'o': 14, 'r': 15, 's': 16, 't': 17, 'x': 18, 'z': 19}\n"
+     ]
+    }
+   ],
+   "source": [
+    "token2idx = {ch: idx for idx, ch in enumerate(sorted(set(tokenized_text)))}\n",
+    "print(token2idx)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ff118c22",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 14, 12, 8, 13, 11, 19, 11, 13, 10, 0, 17, 8, 18, 17, 0, 11, 16, 0, 6, 0, 7, 14, 15, 8, 0, 17, 6, 16, 12, 0, 14, 9, 0, 3, 2, 4, 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_ids = [token2idx[token] for token in tokenized_text]\n",
+    "print(input_ids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8508c09d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn.functional as F"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "4ce2e4e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/rz/96n_vjq17kg53f0rgc21grc40000gn/T/ipykernel_1858/3674191911.py:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  input_ids = torch.tensor(input_ids)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([38, 20])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "input_ids = torch.tensor(input_ids)\n",
+    "one_hot_encodings = F.one_hot(input_ids, num_classes=len(token2idx))\n",
+    "one_hot_encodings.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b8449c2",
+   "metadata": {},
+   "source": [
+    "38개의 입력 토큰 각각에 20차원의 원-핫 벡터가 만들어짐\n",
+    "\n",
+    "5 .....len(20) <br>\n",
+    "14.....len(20) <br>\n",
+    "12.....len(20) <br>\n",
+    ".\n",
+    ".\n",
+    ". .....len(20) <br>\n",
+    "2 .....len(20) <br>\n",
+    "4 .....len(20) <br>\n",
+    "1 .....len(20) <br>\n",
+    "\n",
+    "각 토큰별 20개의 원소를 가진 벡터가 만들어짐"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "58874e4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "토큰: Tokenizing\n",
+      "텐서 인덱스: 5\n",
+      "원-핫 인코딩: tensor([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"토큰: {tokenized_text[0]}\")\n",
+    "print(f\"텐서 인덱스: {input_ids[0]}\")\n",
+    "print(f\"원-핫 인코딩: {one_hot_encodings[0]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5729671a",
+   "metadata": {},
+   "source": [
+    "## 단어 토큰화\n",
+    "텍스트를 문자가 아닌 단어로 분할하고 각 단어를 정수로 매핑"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "6fbe209f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['this', 'is', 'a', 'test']\n"
+     ]
+    }
+   ],
+   "source": [
+    "tokenized_text = text.split()\n",
+    "print(tokenized_text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8988f71",
+   "metadata": {},
+   "source": [
+    "단어 토큰화는 단어에 곡용, 활용형, 철자 오류가 포함되어 어휘 사전이 금세 수백만 개까지 늘어나게 됨"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bfb042f0",
+   "metadata": {},
+   "source": [
+    "단어를 토큰화 하게 되면 어휘사전이 커지게 되고 이러면 신경망의 파라미터 역시 많이 필요하게 됨\n",
+    "<br>\n",
+    "- 어휘 사전의 크기를 제한하는 일반적인 방법은 드물게 등장하는 단어는 무시하는 것. 이러한 단어는 'UNK' 토큰으로 매핑\n",
+    "- 하지만 이렇게 되면 토큰화 과정에서 중요 정보 일부를 잃게 됨\n",
+    "- 모든 입력 정보와 일부 입력 구조를 유지하는 문자 토큰화와 단어 토큰화를 절충하는 방법으로 **부분단어 토큰화(subword tokenization)** 라는 방법"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "95a96577",
+   "metadata": {},
+   "source": [
+    "## 부분단어 토큰화\n",
+    "- 부분단어 토큰화는 문자 단어 토큰화 + 단어 토큰화 의 장점을 결합\n",
+    "- 드물게 등장하는 단어를 더 작은 단위로 나누면 모델이 복잡한 단어나 철자 오류를 처리하기 용이\n",
+    "- 다른 방법으론 입력 길이를 적절한 크기로 유지하기 위해 자주 등장하는 단어를 고유한 항목으로 유지\n",
+    "<br><br>\n",
+    "- NLP 분야에서 널리 사용되는 부분단어 토큰화 중 먼저 BERT와 DistilBERT의 토크나이저 **WordPiece**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "2379be10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import AutoTokenizer\n",
+    "\n",
+    "model_ckpt = \"distilbert-base-uncased\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_ckpt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "6b1e2b90",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "this is a test\n",
+      "{'input_ids': [101, 2023, 2003, 1037, 3231, 102], 'attention_mask': [1, 1, 1, 1, 1, 1]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "encoded_text = tokenizer(text)\n",
+    "print(text)\n",
+    "print(encoded_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "0b85a1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['[CLS]', 'this', 'is', 'a', 'test', '[SEP]']\n"
+     ]
+    }
+   ],
+   "source": [
+    "tokens = tokenizer.convert_ids_to_tokens(encoded_text.input_ids)\n",
+    "print(tokens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b6f7c5",
+   "metadata": {},
+   "source": [
+    "1. 특수 토큰 [CLS], [SEP]가 시퀀스 처음과 끝에 추가\n",
+    "2. 토큰이 모두 소문자로 변환\n",
+    "3. 'tokenizing'과 'NLP'가 각각 2개의 토큰으로 나뉘어짐. 자주 등장하는 단어가 아니기 때문 ##izing, ##p에 있는 #은 공백이 아님을 의미\n",
+    "\n",
+    "이런 접두사가 붙은 토큰은 문자열로 다시 바꿀 때 앞의 토큰과 합침"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "259920c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CLS] this is a test [SEP]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenizer.convert_tokens_to_string(tokens))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "46c19084",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30522"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 어휘 사전 크기 확인\n",
+    "tokenizer.vocab_size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "15e57040",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "512"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 모델 최대 문맥 크기\n",
+    "tokenizer.model_max_length"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "dbbba4bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['input_ids', 'attention_mask']"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 모델이 정방향 패스(forward pass)\n",
+    "tokenizer.model_input_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8fa19c9",
+   "metadata": {},
+   "source": [
+    "# 전체 데이터셋 토큰화\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "5e5a58d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tokenize(batch):\n",
+    "    return tokenizer(batch[\"text\"], padding=True, truncation=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db6da0cc",
+   "metadata": {},
+   "source": [
+    "Tokenizer를 샘플 배치에 적용\n",
+    "<br>\n",
+    "- padding=True: 배치에 있는 가장 긴 샘플 크기에 맞춰 샘플을 0으로 패딩\n",
+    "- truncation=True: 모델의 최대 문맥 크기에 맞춰 샘플을 잘라냄"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "9658e034",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'input_ids': [[101, 1045, 2134, 2102, 2514, 26608, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 1045, 2064, 2175, 2013, 3110, 2061, 20625, 2000, 2061, 9636, 17772, 2074, 2013, 2108, 2105, 2619, 2040, 14977, 1998, 2003, 8300, 102]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tokenize(emotions[\"train\"][:2]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "03458041",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /Users/hangyojeong/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-6316da5de5436960.arrow\n",
+      "Loading cached processed dataset at /Users/hangyojeong/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-2cb11f4abdb48d91.arrow\n",
+      "Loading cached processed dataset at /Users/hangyojeong/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd/cache-1983a40220248de7.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "emotions_encoded = emotions.map(tokenize, batched=True, batch_size=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ceffe2fe",
+   "metadata": {},
+   "source": [
+    "map() 메서드는 말뭉치에 있는 모든 샘플에 개별적으로 작용 <br>\n",
+    "\n",
+    "- batched=True: 트윗을 배치로 인코딩\n",
+    "- batch_size=None: 전체 데이터셋이 하나의 배치로 tokenize() 함수에 적용, 입력 텐서와 어텐션 마스크는 전역적으로 동일한 크기로 생성"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3de1ab5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['text', 'label', 'input_ids', 'attention_mask']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(emotions_encoded[\"train\"].column_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3d9224c",
+   "metadata": {},
+   "source": [
+    "# 텍스트 분류 모델 훈련\n",
+    "\n",
+    "### 특성 추출\n",
+    "사전 훈련된 모델을 수정하지 않고 은닉 상태를 특성(feature)으로 사용해 분류 모델을 훈련\n",
+    "\n",
+    "### 미세 튜닝\n",
+    "사전 훈련된 모델의 파라미터도 업데이트하기 위해 전체 모델을 엔드-투-엔드로 훈련"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b923107",
+   "metadata": {},
+   "source": [
+    "## 특성 추출 방법\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6560f7f",
+   "metadata": {},
+   "source": [
+    "사전 훈련된 모델 사용하기"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "e6f48da5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_layer_norm.weight']\n",
+      "- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import AutoModel\n",
+    "\n",
+    "model_ckpt = \"distilbert-base-uncased\"\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model = AutoModel.from_pretrained(model_ckpt).to(device)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "5e3da298",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "입력 텐서 크기: torch.Size([1, 6])\n"
+     ]
+    }
+   ],
+   "source": [
+    "text = \"this is a test\"\n",
+    "inputs = tokenizer(text, return_tensors=\"pt\")\n",
+    "print(f\"입력 텐서 크기: {inputs['input_ids'].size()}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "4f99ad6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('input_ids', tensor([[ 101, 2023, 2003, 1037, 3231,  102]])), ('attention_mask', tensor([[1, 1, 1, 1, 1, 1]]))])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.items()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "a81ee80b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "k: input_ids\n",
+      "v: tensor([[ 101, 2023, 2003, 1037, 3231,  102]])\n",
+      "k: attention_mask\n",
+      "v: tensor([[1, 1, 1, 1, 1, 1]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "for k, v in inputs.items():\n",
+    "    print(f\"k: {k}\")\n",
+    "    print(f\"v: {v}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "c3d5a3ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BaseModelOutput(last_hidden_state=tensor([[[-0.1565, -0.1862,  0.0528,  ..., -0.1188,  0.0662,  0.5470],\n",
+      "         [-0.3575, -0.6484, -0.0618,  ..., -0.3040,  0.3508,  0.5221],\n",
+      "         [-0.2772, -0.4459,  0.1818,  ..., -0.0948, -0.0076,  0.9958],\n",
+      "         [-0.2841, -0.3917,  0.3753,  ..., -0.2151, -0.1173,  1.0526],\n",
+      "         [ 0.2661, -0.5094, -0.3180,  ..., -0.4203,  0.0144, -0.2149],\n",
+      "         [ 0.9441,  0.0112, -0.4714,  ...,  0.1439, -0.7288, -0.1619]]]), hidden_states=None, attentions=None)\n"
+     ]
+    }
+   ],
+   "source": [
+    "inputs = {k:v.to(device) for k,v in inputs.items()}\n",
+    "with torch.no_grad():\n",
+    "    outputs = model(**inputs)\n",
+    "    \n",
+    "print(outputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "a7b6ba5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 6, 768])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs.last_hidden_state.size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f551af1",
+   "metadata": {},
+   "source": [
+    "은닉 상태 텐서의 크기는 [batch_size, n_tokens, hidden_dim] <br>\n",
+    "-> 6개의 입력 토큰마다 768차원의 벡터가 반환"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "9a5b950a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 768])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs.last_hidden_state[:,0].size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "c6eb5006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_hidden_states(batch):\n",
+    "    inputs = {k:v.to(device) for k,v in batch.items() if k in tokenizer.model_input_names}\n",
+    "    \n",
+    "    # 마지막 은닉 상태를 추출\n",
+    "    with torch.no_grad():\n",
+    "        last_hidden_state = model(**inputs).last_hidden_state\n",
+    "        \n",
+    "    # [CLS] 토큰에 대한 벡터를 반환\n",
+    "    return {\"hidden_state\": last_hidden_state[:,0].cpu().numpy()}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "1faae4d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "PyTorch needs to be installed to be able to return PyTorch tensors.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[77], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43memotions_encoded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtorch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/dataset_dict.py:548\u001b[0m, in \u001b[0;36mDatasetDict.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_values_type()\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[0;32m--> 548\u001b[0m     \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/fingerprint.py:480\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             validate_fingerprint(kwargs[fingerprint_name])\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 480\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/arrow_dataset.py:2326\u001b[0m, in \u001b[0;36mDataset.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m   2324\u001b[0m \u001b[38;5;66;03m# Check that the format_type and format_kwargs are valid and make it possible to have a Formatter\u001b[39;00m\n\u001b[1;32m   2325\u001b[0m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m=\u001b[39m get_format_type_from_alias(\u001b[38;5;28mtype\u001b[39m)\n\u001b[0;32m-> 2326\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mget_formatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;66;03m# Check filter column\u001b[39;00m\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(columns, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/formatting/__init__.py:127\u001b[0m, in \u001b[0;36mget_formatter\u001b[0;34m(format_type, **format_kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _FORMAT_TYPES[format_type](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_type \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE:\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn type should be None or selected in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mtype\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but got \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformat_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m     )\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43memotions_encoded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtorch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/dataset_dict.py:548\u001b[0m, in \u001b[0;36mDatasetDict.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_values_type()\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[0;32m--> 548\u001b[0m     \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/fingerprint.py:480\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             validate_fingerprint(kwargs[fingerprint_name])\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 480\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/arrow_dataset.py:2326\u001b[0m, in \u001b[0;36mDataset.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m   2324\u001b[0m \u001b[38;5;66;03m# Check that the format_type and format_kwargs are valid and make it possible to have a Formatter\u001b[39;00m\n\u001b[1;32m   2325\u001b[0m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m=\u001b[39m get_format_type_from_alias(\u001b[38;5;28mtype\u001b[39m)\n\u001b[0;32m-> 2326\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mget_formatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;66;03m# Check filter column\u001b[39;00m\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(columns, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/formatting/__init__.py:127\u001b[0m, in \u001b[0;36mget_formatter\u001b[0;34m(format_type, **format_kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _FORMAT_TYPES[format_type](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_type \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE:\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn type should be None or selected in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mtype\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but got \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformat_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m     )\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43memotions_encoded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtorch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/dataset_dict.py:548\u001b[0m, in \u001b[0;36mDatasetDict.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_values_type()\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[0;32m--> 548\u001b[0m     \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/fingerprint.py:480\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             validate_fingerprint(kwargs[fingerprint_name])\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 480\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/arrow_dataset.py:2326\u001b[0m, in \u001b[0;36mDataset.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m   2324\u001b[0m \u001b[38;5;66;03m# Check that the format_type and format_kwargs are valid and make it possible to have a Formatter\u001b[39;00m\n\u001b[1;32m   2325\u001b[0m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m=\u001b[39m get_format_type_from_alias(\u001b[38;5;28mtype\u001b[39m)\n\u001b[0;32m-> 2326\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mget_formatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;66;03m# Check filter column\u001b[39;00m\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(columns, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/formatting/__init__.py:127\u001b[0m, in \u001b[0;36mget_formatter\u001b[0;34m(format_type, **format_kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _FORMAT_TYPES[format_type](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_type \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE:\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn type should be None or selected in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mtype\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but got \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformat_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m     )\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43memotions_encoded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtorch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/dataset_dict.py:548\u001b[0m, in \u001b[0;36mDatasetDict.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_values_type()\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[0;32m--> 548\u001b[0m     \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/fingerprint.py:480\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             validate_fingerprint(kwargs[fingerprint_name])\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 480\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/arrow_dataset.py:2326\u001b[0m, in \u001b[0;36mDataset.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m   2324\u001b[0m \u001b[38;5;66;03m# Check that the format_type and format_kwargs are valid and make it possible to have a Formatter\u001b[39;00m\n\u001b[1;32m   2325\u001b[0m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m=\u001b[39m get_format_type_from_alias(\u001b[38;5;28mtype\u001b[39m)\n\u001b[0;32m-> 2326\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mget_formatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;66;03m# Check filter column\u001b[39;00m\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(columns, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/formatting/__init__.py:127\u001b[0m, in \u001b[0;36mget_formatter\u001b[0;34m(format_type, **format_kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _FORMAT_TYPES[format_type](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_type \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE:\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn type should be None or selected in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mtype\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but got \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformat_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m     )\n",
+      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[76], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43memotions_encoded\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtorch\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minput_ids\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mattention_mask\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlabel\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/dataset_dict.py:548\u001b[0m, in \u001b[0;36mDatasetDict.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_values_type()\n\u001b[1;32m    547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[0;32m--> 548\u001b[0m     \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_format\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_all_columns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_all_columns\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/fingerprint.py:480\u001b[0m, in \u001b[0;36mfingerprint_transform.<locals>._fingerprint.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    476\u001b[0m             validate_fingerprint(kwargs[fingerprint_name])\n\u001b[1;32m    478\u001b[0m \u001b[38;5;66;03m# Call actual function\u001b[39;00m\n\u001b[0;32m--> 480\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    482\u001b[0m \u001b[38;5;66;03m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[39;00m\n\u001b[1;32m    484\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:  \u001b[38;5;66;03m# update after calling func so that the fingerprint doesn't change if the function fails\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/arrow_dataset.py:2326\u001b[0m, in \u001b[0;36mDataset.set_format\u001b[0;34m(self, type, columns, output_all_columns, **format_kwargs)\u001b[0m\n\u001b[1;32m   2324\u001b[0m \u001b[38;5;66;03m# Check that the format_type and format_kwargs are valid and make it possible to have a Formatter\u001b[39;00m\n\u001b[1;32m   2325\u001b[0m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m=\u001b[39m get_format_type_from_alias(\u001b[38;5;28mtype\u001b[39m)\n\u001b[0;32m-> 2326\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mget_formatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mtype\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfeatures\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeatures\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mformat_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2328\u001b[0m \u001b[38;5;66;03m# Check filter column\u001b[39;00m\n\u001b[1;32m   2329\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(columns, \u001b[38;5;28mstr\u001b[39m):\n",
+      "File \u001b[0;32m~/miniforge3/envs/m1_tf_keras/lib/python3.9/site-packages/datasets/formatting/__init__.py:127\u001b[0m, in \u001b[0;36mget_formatter\u001b[0;34m(format_type, **format_kwargs)\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _FORMAT_TYPES[format_type](\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mformat_kwargs)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m format_type \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE:\n\u001b[0;32m--> 127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    130\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReturn type should be None or selected in \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mtype\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;129;01min\u001b[39;00m _FORMAT_TYPES\u001b[38;5;241m.\u001b[39mkeys() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but got \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mformat_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    131\u001b[0m     )\n",
+      "\u001b[0;31mValueError\u001b[0m: PyTorch needs to be installed to be able to return PyTorch tensors."
+     ]
+    }
+   ],
+   "source": [
+    "emotions_encoded.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"label\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49cf0fca",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}