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--- |
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license: mit |
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language: |
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- zh |
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base_model: |
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- joeddav/xlm-roberta-large-xnli |
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pipeline_tag: text-classification |
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tags: |
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- emotion |
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library_name: transformers |
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datasets: |
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- Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset |
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--- |
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# chinese-text-emotion-classifier |
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Here's a model is fine-tuned based on another base model and features a smaller parameter size. For users who require faster inference speed, this model is a suitable choice.The actual performance test results are also not much different. |
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Model：[Chinese-Emotion-Small](https://huggingface.co/Johnson8187/Chinese-Emotion-Small) |
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此模型是基於另一個基座模型所調整的結果，擁有較小的參數規模。對於有推理速度需求的使用者，可以選擇此模型以達到更快速的性能表現，實際測試性能也相差不大。 |
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模型：[Chinese-Emotion-Small](https://huggingface.co/Johnson8187/Chinese-Emotion-Small) |
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## 📚 Model Introduction |
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This model is fine-tuned based on the [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) model, specializing in **Chinese text emotion analysis**. |
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Through fine-tuning, the model can identify the following 8 emotion labels: |
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- **Neutral tone** |
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- **Concerned tone** |
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- **Happy tone** |
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- **Angry tone** |
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- **Sad tone** |
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- **Questioning tone** |
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- **Surprised tone** |
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- **Disgusted tone** |
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The model is applicable to various scenarios, such as customer service emotion monitoring, social media analysis, and user feedback classification. |
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--- |
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## 📚 模型簡介 |
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本模型基於[joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) 模型進行微調，專注於 **中文語句情感分析**。 |
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通過微調，模型可以識別以下 8 種情緒標籤： |
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- **平淡語氣** |
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- **關切語調** |
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- **開心語調** |
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- **憤怒語調** |
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- **悲傷語調** |
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- **疑問語調** |
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- **驚奇語調** |
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- **厭惡語調** |
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該模型適用於多種場景，例如客服情緒監控、社交媒體分析以及用戶反饋分類。 |
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--- |
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## 🚀 Quick Start |
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### Install Dependencies |
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Ensure that you have installed Hugging Face's Transformers library and PyTorch: |
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```bash |
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pip install transformers torch |
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``` |
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###Load the Model |
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Use the following code to load the model and tokenizer, and perform emotion classification: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# 添加設備設定 |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# 標籤映射字典 |
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label_mapping = { |
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0: "平淡語氣", |
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1: "關切語調", |
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2: "開心語調", |
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3: "憤怒語調", |
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4: "悲傷語調", |
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5: "疑問語調", |
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6: "驚奇語調", |
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7: "厭惡語調" |
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} |
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def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion"): |
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# 載入模型和分詞器 |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device) # 移動模型到設備 |
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# 將文本轉換為模型輸入格式 |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device) # 移動輸入到設備 |
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# 進行預測 |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# 取得預測結果 |
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predicted_class = torch.argmax(outputs.logits).item() |
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predicted_emotion = label_mapping[predicted_class] |
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return predicted_emotion |
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if __name__ == "__main__": |
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# 使用範例 |
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test_texts = [ |
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"雖然我努力了很久，但似乎總是做不到，我感到自己一無是處。", |
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"你說的那些話真的讓我很困惑，完全不知道該怎麼反應。", |
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"這世界真的是無情，為什麼每次都要給我這樣的考驗？", |
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"有時候，我只希望能有一點安靜，不要再聽到這些無聊的話題。", |
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"每次想起那段過去，我的心還是會痛，真的無法釋懷。", |
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"我從來沒有想過會有這麼大的改變，現在我覺得自己完全失控了。", |
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"我完全沒想到你會這麼做，這讓我驚訝到無法言喻。", |
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"我知道我應該更堅強，但有些時候，這種情緒真的讓我快要崩潰了。" |
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] |
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for text in test_texts: |
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emotion = predict_emotion(text) |
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print(f"文本: {text}") |
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print(f"預測情緒: {emotion}\n") |
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``` |
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--- |
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## 🚀 快速開始 |
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### 安裝依賴 |
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請確保安裝了 Hugging Face 的 Transformers 庫和 PyTorch： |
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```bash |
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pip install transformers torch |
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``` |
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### 加載模型 |
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使用以下代碼加載模型和分詞器，並進行情感分類： |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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# 添加設備設定 |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# 標籤映射字典 |
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label_mapping = { |
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0: "平淡語氣", |
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1: "關切語調", |
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2: "開心語調", |
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3: "憤怒語調", |
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4: "悲傷語調", |
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5: "疑問語調", |
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6: "驚奇語調", |
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7: "厭惡語調" |
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} |
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def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion"): |
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# 載入模型和分詞器 |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device) # 移動模型到設備 |
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# 將文本轉換為模型輸入格式 |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device) # 移動輸入到設備 |
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# 進行預測 |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# 取得預測結果 |
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predicted_class = torch.argmax(outputs.logits).item() |
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predicted_emotion = label_mapping[predicted_class] |
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return predicted_emotion |
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if __name__ == "__main__": |
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# 使用範例 |
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test_texts = [ |
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"雖然我努力了很久，但似乎總是做不到，我感到自己一無是處。", |
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"你說的那些話真的讓我很困惑，完全不知道該怎麼反應。", |
|
"這世界真的是無情，為什麼每次都要給我這樣的考驗？", |
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"有時候，我只希望能有一點安靜，不要再聽到這些無聊的話題。", |
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"每次想起那段過去，我的心還是會痛，真的無法釋懷。", |
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"我從來沒有想過會有這麼大的改變，現在我覺得自己完全失控了。", |
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"我完全沒想到你會這麼做，這讓我驚訝到無法言喻。", |
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"我知道我應該更堅強，但有些時候，這種情緒真的讓我快要崩潰了。" |
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] |
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for text in test_texts: |
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emotion = predict_emotion(text) |
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print(f"文本: {text}") |
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print(f"預測情緒: {emotion}\n") |
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``` |
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--- |
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### Dataset |
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- The fine-tuning dataset consists of 4,000 annotated Traditional Chinese emotion samples, covering various emotion categories to ensure the model's generalization capability in emotion classification. |
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- [Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset](https://huggingface.co/datasets/Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset) |
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### 數據集 |
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- 微調數據來自4000個自行標註的高質量繁體中文情感語句數據，覆蓋了多種情緒類別，確保模型在情感分類上的泛化能力。 |
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- [Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset](https://huggingface.co/datasets/Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset) |
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--- |
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🌟 Contact and Feedback |
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If you encounter any issues while using this model, please contact: |
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Email: fable8043@gmail.com |
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Hugging Face Project Page: chinese-text-emotion-classifier |
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## 🌟 聯繫與反饋 |
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如果您在使用該模型時有任何問題，請聯繫： |
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- 郵箱：`fable8043@gmail.com` |
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- Hugging Face 項目頁面：[chinese-text-emotion-classifier](https://huggingface.co/Johnson8187/chinese-text-emotion-classifier) |
