Chinese-Emotion / README.md
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metadata
license: mit
language:
  - zh
base_model:
  - joeddav/xlm-roberta-large-xnli
pipeline_tag: text-classification
tags:
  - emotion
library_name: transformers
datasets:
  - Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset

chinese-text-emotion-classifier

📚 Model Introduction

This model is fine-tuned based on the joeddav/xlm-roberta-large-xnli model, specializing in Chinese text emotion analysis.
Through fine-tuning, the model can identify the following 8 emotion labels:

  • Neutral tone
  • Concerned tone
  • Happy tone
  • Angry tone
  • Sad tone
  • Questioning tone
  • Surprised tone
  • Disgusted tone

The model is applicable to various scenarios, such as customer service emotion monitoring, social media analysis, and user feedback classification.


chinese-text-emotion-classifier

📚 模型簡介

本模型基於joeddav/xlm-roberta-large-xnli 模型進行微調,專注於 中文語句情感分析
通過微調,模型可以識別以下 8 種情緒標籤:

  • 平淡語氣
  • 關切語調
  • 開心語調
  • 憤怒語調
  • 悲傷語調
  • 疑問語調
  • 驚奇語調
  • 厭惡語調

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。


🚀 Quick Start

Install Dependencies

Ensure that you have installed Hugging Face's Transformers library and PyTorch:

pip install transformers torch

###Load the Model Use the following code to load the model and tokenizer, and perform emotion classification:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 添加設備設定
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 標籤映射字典
label_mapping = {
    0: "平淡語氣",
    1: "關切語調",
    2: "開心語調",
    3: "憤怒語調",
    4: "悲傷語調",
    5: "疑問語調",
    6: "驚奇語調",
    7: "厭惡語調"
}

def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion"):
    # 載入模型和分詞器
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)  # 移動模型到設備
    
    # 將文本轉換為模型輸入格式
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)  # 移動輸入到設備
    
    # 進行預測
    with torch.no_grad():
        outputs = model(**inputs)
    
    # 取得預測結果
    predicted_class = torch.argmax(outputs.logits).item()
    predicted_emotion = label_mapping[predicted_class]
    
    return predicted_emotion

if __name__ == "__main__":
    # 使用範例
    test_texts = [
        "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。",
        "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?",
        "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。",
        "每次想起那段過去,我的心還是會痛,真的無法釋懷。",
        "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。",
        "我完全沒想到你會這麼做,這讓我驚訝到無法言喻。",
        "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。"
    ]

    for text in test_texts:
        emotion = predict_emotion(text)
        print(f"文本: {text}")
        print(f"預測情緒: {emotion}\n")

🚀 快速開始

安裝依賴

請確保安裝了 Hugging Face 的 Transformers 庫和 PyTorch:

pip install transformers torch

加載模型

使用以下代碼加載模型和分詞器,並進行情感分類:

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 添加設備設定
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 標籤映射字典
label_mapping = {
    0: "平淡語氣",
    1: "關切語調",
    2: "開心語調",
    3: "憤怒語調",
    4: "悲傷語調",
    5: "疑問語調",
    6: "驚奇語調",
    7: "厭惡語調"
}

def predict_emotion(text, model_path="Johnson8187/Chinese-Emotion"):
    # 載入模型和分詞器
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)  # 移動模型到設備
    
    # 將文本轉換為模型輸入格式
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)  # 移動輸入到設備
    
    # 進行預測
    with torch.no_grad():
        outputs = model(**inputs)
    
    # 取得預測結果
    predicted_class = torch.argmax(outputs.logits).item()
    predicted_emotion = label_mapping[predicted_class]
    
    return predicted_emotion

if __name__ == "__main__":
    # 使用範例
    test_texts = [
        "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。",
        "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?",
        "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。",
        "每次想起那段過去,我的心還是會痛,真的無法釋懷。",
        "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。",
        "我完全沒想到你會這麼做,這讓我驚訝到無法言喻。",
        "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。"
    ]

    for text in test_texts:
        emotion = predict_emotion(text)
        print(f"文本: {text}")
        print(f"預測情緒: {emotion}\n")

Dataset

  • 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.
  • Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset

數據集


🌟 Contact and Feedback If you encounter any issues while using this model, please contact:

Email: fable8043@gmail.com Hugging Face Project Page: chinese-text-emotion-classifier

🌟 聯繫與反饋

如果您在使用該模型時有任何問題,請聯繫: