Xlm Roberta Twitter Spam Classification

🚀 推文垃圾郵件檢測模型

本模型用於將來自X（原Twitter）的推文分類為“垃圾郵件”（1）或“優質內容”（0），有效助力用戶識別推文質量，提升信息獲取效率。

🚀 快速開始

本模型可將來自X（原Twitter）的推文分類為“垃圾郵件”（1）或“優質內容”（0）。

✨ 主要特性

• 基於FacebookAI/xlm - roberta - large基礎模型，具有強大的特徵提取能力。
• 針對推文垃圾郵件檢測進行了微調，能有效區分垃圾推文和優質推文。

📦 安裝指南

文檔未提及具體安裝步驟，故跳過此章節。

💻 使用示例

基礎用法

def classify_texts(df, text_col, model_path="cja5553/xlm-roberta-Twitter-spam-classification", batch_size=24):
    '''
    Classifies texts as either "Quality" or "Spam" using a pre-trained sequence classification model.

    Parameters:
    -----------
    df : pandas.DataFrame
        DataFrame containing the texts to classify.
    
    text_col : str
        Name of the column in that contains the text data to be classified.
    
    model_path : str, default="cja5553/xlm-roberta-Twitter-spam-classification"
        Path to the pre-trained model for sequence classification.
    
    batch_size : int, optional, default=24
        Batch size for loading and processing data in batches. Adjust based on available GPU memory.

    Returns:
    --------
    pandas.DataFrame
        The original DataFrame with an additional column `spam_prediction`, containing the predicted labels ("Quality" or "Spam") for each text.

    '''
    # Load the tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSequenceClassification.from_pretrained(model_path).to("cuda")
    model.eval()  # Set model to evaluation mode
    
    # Prepare the text data for classification
    df["text"] = df[text_col].astype(str)  # Ensure text is in string format

    # Convert the data to a Hugging Face Dataset and tokenize
    text_dataset = Dataset.from_pandas(df)
    
    def tokenize_function(example):
        return tokenizer(
            example["text"],
            padding="max_length",
            truncation=True,
            max_length=512
        )
    
    text_dataset = text_dataset.map(tokenize_function, batched=True)
    text_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])
    
    # DataLoader for the text data
    text_loader = DataLoader(text_dataset, batch_size=batch_size)
    
    # Make predictions
    predictions = []
    with torch.no_grad():
        for batch in tqdm_notebook(text_loader):
            input_ids = batch['input_ids'].to("cuda")
            attention_mask = batch['attention_mask'].to("cuda")
            
            # Forward pass
            outputs = model(input_ids=input_ids, attention_mask=attention_mask)
            logits = outputs.logits
            preds = torch.argmax(logits, dim=-1).cpu().numpy()  # Get predicted labels
            predictions.extend(preds)
    
    # Map predictions to labels
    id2label = {0: "Quality", 1: "Spam"}
    predicted_labels = [id2label[pred] for pred in predictions]
    
    # Add predictions to the original DataFrame
    df["spam_prediction"] = predicted_labels
    
    return df

spam_df_classification = classify_texts(df, "text_col")
print(spam_df_classification)

高級用法

文檔未提及高級用法相關代碼，故不展示此部分。

📚 詳細文檔

訓練數據集

該模型在UtkMl的Twitter垃圾郵件檢測數據集上進行了微調，使用FacebookAI/xlm - roberta - large作為基礎模型。

指標

基於80 - 10 - 10的訓練 - 驗證 - 測試集劃分，在測試集上取得了以下結果：

代碼

用於訓練這些模型的代碼可在GitHub上獲取：github.com/cja5553/Twitter_spam_detection

問題諮詢

如有問題，請聯繫：alba@wustl.edu

📄 許可證

本項目採用MIT許可證。