README.md

---
# 元数据标签，Hugging Face 会解析这些字段
language:
  - en
tags:
  - sentiment-analysis
  - distilbert
  - text-classification
  - nlp
license: apache-2.0
datasets:
  - amazon_polarity
metrics:
  - accuracy
  - f1
model-index:
  - name: fine-tuned-distilbert
    results:
      - task:
          type: text-classification
          name: Sentiment Analysis
        dataset:
          name: Amazon Polarity
          type: amazon_polarity
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.90  # 示例值，替换为你的实际结果
          - name: F1
            type: f1
            value: 0.89  # 示例值，替换为你的实际结果
---

# Fine-Tuned DistilBERT for Sentiment Analysis

## Model Description

This model is a fine-tuned version of [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased) on the [`amazon_polarity`](https://huggingface.co/datasets/amazon_polarity) dataset. It is designed for binary sentiment classification, predicting whether a given text expresses a **positive** (1) or **negative** (0) sentiment. The model leverages the lightweight architecture of DistilBERT, making it efficient for deployment while maintaining strong performance.

- **Developed by**: [Jack.RX Tech]
- **Model Type**: Transformer-based text classification
- **Base Model**: `distilbert-base-uncased`
- **Language**: English
- **License**: Apache 2.0

## Intended Uses

This model is intended for sentiment analysis tasks, particularly in analyzing product reviews or user feedback. It can be used in:
- E-commerce platforms to monitor customer opinions.
- Social media analysis for brand reputation management.
- Market research to gauge consumer sentiment.

### Direct Use
The model can classify text directly without additional fine-tuning for similar binary sentiment tasks.

### Downstream Use
It can be further fine-tuned for domain-specific sentiment analysis (e.g., medical reviews, movie critiques).

## How to Use

### Python Code Example
Below is an example of how to load and use the model with the `transformers` library:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# 加载模型和tokenizer
model_name = "huevan/distilbert-base-uncased-rx"  # 替换为你的仓库名
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# 输入文本
text = "I love this product, it's amazing!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)

# 预测
model.eval()
with torch.no_grad():
    outputs = model(**inputs)
    prediction = outputs.logits.argmax(-1).item()
    sentiment = "positive" if prediction == 1 else "negative"
print(f"Sentiment: {sentiment}")  # 输出: positive