Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwen2.5
+- sentiment-analysis
+- chinese
+- freeze-tuning
+- llama-factory
+metrics:
+- accuracy
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B-Instruct
+---
+
+# Qwen2.5-Coder-Sentiment-Freeze
+This is a fine-tuned version of `Qwen/Qwen2.5-Coder-1.5B-Instruct` specialized for **Chinese sentiment analysis**.
+
+The model was trained using the efficient **freeze training** method on the **ChnSentiCorp** dataset. By training only the last 6 layers, this approach achieved a significant performance boost, increasing accuracy from **91.6% to 97.8%** on the evaluation set.
+
+This model is designed to classify Chinese text into **positive (1)** or **negative (0)** sentiment and output the result in a clean JSON format.
+
+## 🚀 Full Tutorial & Repository
+
+This model was trained as part of a comprehensive, beginner-friendly tutorial that walks through every step of the process, from data preparation to evaluation and deployment.
+
+### 👉 [**GitHub Repository: IIIIQIIII/MSJ-Factory**](https://github.com/IIIIQIIII/MSJ-Factory)
+
+The repository includes:
+- 💻 A step-by-step Google Colab notebook.
+- ⚙️ All training and evaluation scripts.
+- 📊 Detailed explanations of the training method and results.
+
+**If you find this model or the tutorial helpful, please give the repository a ⭐️ star! It helps support the author's work.**
+
+[![GitHub Stars](https://img.shields.io/github/stars/IIIIQIIII/MSJ-Factory?style=social)](https://github.com/IIIIQIIII/MSJ-Factory)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IIIIQIIII/MSJ-Factory/blob/main/Qwen2_5_Sentiment_Fine_tuning_Tutorial.ipynb)
+
+## 💡 How to Use
+This model follows a specific instruction format to ensure reliable JSON output. Use the prompt template below for the best results.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Model repository on Hugging Face Hub
+model_name = "FutureMa/Qwen2.5-Coder-Sentiment-Freeze"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# --- Define the text for sentiment analysis ---
+text = "这个酒店的服务态度非常好，房间也很干净！" # Example: "The service at this hotel was excellent, and the room was very clean!"
+
+# --- Create the prompt using the required template ---
+prompt = f"""请对以下中文文本进行情感分析，判断其情感倾向。
+
+任务说明：
+- 分析文本表达的整体情感态度
+- 判断是正面(1)还是负面(0)
+
+文本内容：
+```sentence
+{text}
+```
+
+输出格式：
+```json
+{{
+  "sentiment": 0 or 1
+}}
+```"""
+
+messages = [{"role": "user", "content": prompt}]
+text_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text_input], return_tensors="pt").to(model.device)
+
+# --- Generate the response ---
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=256,
+    temperature=0.1
+)
+response = tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
+
+print(f"Input Text: {text}")
+print(f"Model Output:\n{response}")
+
+# Expected output for the example text:
+# Input Text: 这个酒店的服务态度非常好，房间也很干净！
+# Model Output:
+# ```json
+# {
+#   "sentiment": 1
+# }
+# ```
+```
+
+## 📊 Evaluation Results
+
+The fine-tuned model shows significant improvements across all key metrics compared to the base model. It demonstrates perfect precision, meaning it makes no false positive predictions on the test set.
+
+| Model                                | Accuracy | Precision | Recall   | F1-Score |
+|--------------------------------------|----------|-----------|----------|----------|
+| Base Model (Qwen2.5-Coder-1.5B)      | 91.62%   | 98.57%    | 83.13%   | 90.20%   |
+| **This Model (Fine-tuned)**          | **97.77%** | **100.00%** | **95.18%** | **97.53%** |
+
+## ⚙️ Training Details
+
+- **Base Model**: [`Qwen/Qwen2.5-Coder-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
+- **Dataset**: A balanced 3,000-sample subset of [ChnSentiCorp](https://huggingface.co/datasets/seamew/ChnSentiCorp).
+- **Training Method**: Freeze Training (only the last 6 layers and embeddings were trained).
+- **Framework**: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- **Hardware**: T4 GPU (via Google Colab)
+- **Training Time**: ~20 minutes
+
+## ⚠️ Limitations and Bias
+
+- **Task-Specific**: This model is highly specialized for Chinese sentiment analysis. It may not perform well on other NLP tasks or languages without further fine-tuning.
+- **Binary Classification**: The model is designed for binary (positive/negative) classification and does not capture neutral or mixed sentiment.
+- **Data Bias**: The model inherits any biases present in the ChnSentiCorp dataset, which primarily consists of hotel and product reviews. Its performance may vary on text from different domains.
+
+## Citation
+
+If you use this model or the associated tutorial in your work, please cite the repository:
+
+```bibtex
+@misc{msj-factory-2025,
+  title={Qwen2.5-Coder Sentiment Analysis Fine-tuning Tutorial},
+  author={MASHIJIAN},
+  year={2025},
+  howpublished={\url{https://github.com/IIIIQIIII/MSJ-Factory}}
+}
+```
+
+## Acknowledgments
+This work would not be possible without the incredible open-source tools and models from the community:
+- [Qwen Team](https://github.com/QwenLM) for the powerful base model.
+- [Hugging Face](https://huggingface.co/) for the `transformers` library and model hosting.
+- [hiyouga](https://github.com/hiyouga) for the excellent `LLaMA-Factory` framework.
+- Google Colab for providing accessible GPU resources.