Update README.md

README.md

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-license: unknown
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+DistilBERT Question Detector Model
+# DistilBERT 占卜问题检测模型
+
+本项目提供了一个基于 `DistilBERT` 占卜问题检测模型，可用于判断输入文本是否为符合塔罗占卜的问题。  
+## 📂 目录结构
+model.safetensors: The trained model weights.
+config.json: The configuration file for the model architecture.
+tokenizer.json: The tokenizer configuration.
+special_tokens_map.json: The special tokens configuration.
+vocab.txt: The vocabulary file for the tokenizer.
+---
+## 🚀 快速开始  
+### **1️⃣ 安装依赖**
+请确保你的环境已安装 Python 3.8+，然后运行以下命令安装所需的依赖库：
+
+pip install torch transformers fastapi uvicorn safetensors
+
+### **2️⃣ 直接运行推理**
+如果你想直接在本地测试模型，可以运行 inference.py：
+python inference.py
+示例代码（inference.py）：
+```python
+    import torch
+    from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+    # 1. 加载模型
+    model_path = "./distilbert-question-detector/checkpoint-5150"
+    tokenizer = DistilBertTokenizer.from_pretrained(model_path)
+    model = DistilBertForSequenceClassification.from_pretrained(model_path)
+    model.eval()
+
+    # 2. 进行推理
+    text = "Is this a question?"
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+    predicted_class = torch.argmax(probabilities, dim=-1).item()
+
+    print(f"Probabilities: {probabilities}")
+    print(f"Predicted class: {predicted_class}")  # 1 代表是疑问句，0 代表不是
+```
+### **3️⃣ 运行 API**
+你也可以使用 FastAPI 部署一个 HTTP 接口，允许其他应用通过 HTTP 请求访问模型。
+uvicorn app:app --host 0.0.0.0 --port 8000
+示例 API 代码（app.py）：
+```python
+    from fastapi import FastAPI
+    import torch
+    from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+
+    app = FastAPI()
+
+    # 加载模型
+    model_path = "./distilbert-question-detector/checkpoint-5150"
+    tokenizer = DistilBertTokenizer.from_pretrained(model_path)
+    model = DistilBertForSequenceClassification.from_pretrained(model_path)
+    model.eval()
+
+    @app.post("/predict/")
+    async def predict(text: str):
+        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            probabilities = torch.nn.functional.softmax(logits, dim=-1)
+
+        predicted_class = torch.argmax(probabilities, dim=-1).item()
+        return {"text": text, "probabilities": probabilities.tolist(), "predicted_class": predicted_class}
+```
+API 运行后，可通过以下方式测试：
+```sh
+curl -X 'POST' \
+  'http://127.0.0.1:8000/predict/' \
+  -H 'Content-Type: application/json' \
+  -d '{"text": "Is this a valid question?"}'
+```
+## 📌 结果说明
+predicted_class: 0 代表输入文本是符合条件
+predicted_class: 1 代表输入文本不符合条件
+示例结果
+```json
+{
+    "text": "Is this a valid question?",
+    "probabilities": [[0.9266, 0.0734]],
+    "predicted_class": 0
+}
+```
+## 🔧 其他部署方案（可选）
+如果你希望将模型部署到云端，可以选择：
+
+Hugging Face Hub: 上传 model.safetensors 到 🤗 Hugging Face
+AWS SageMaker: 使用 Amazon SageMaker 进行云端推理
+Docker 部署: 将 FastAPI 端点封装到 Docker 容器中    
+