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README.md

@@ -1,3 +1,100 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: 
+- Qwen/Qwen2.5-1.5B
+- Qwen/Qwen2.5-3B
+task_categories:
+- text-classification
+language:
+- en
+- zh
+tags:
+- quality-assessment
+- text-quality
+- regression
+pipeline_tag: text-classification
+library_name: transformers
+---
+
+# Qwen2.5 Text Quality Classifier
+
+Fine-tuned Qwen2.5-1.5B and Qwen2.5-3B models for automated text quality assessment. Predicts quality scores on a 0-1 scale focusing on educational value and mathematical intelligence.
+
+## Model Details
+
+- **Base Models**: Qwen2.5-1.5B / Qwen2.5-3B  
+- **Task**: Text Quality Regression
+- **Languages**: English, Chinese
+- **Training Data**: [OpenSQZ/Classifiers-Data](https://huggingface.co/datasets/OpenSQZ/Classifiers-Data)
+- **Loss Function**: MSE Loss
+
+## Performance
+
+| Model | Test MSE Loss |
+|-------|---------------|
+| Qwen2.5-1.5B | 0.00226 |
+| Qwen2.5-3B | 0.00209 |
+
+## Quick Start
+
+### Installation
+```bash
+pip install transformers torch
+```
+
+### Usage
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "OpenSQZ/Qwen2.5-1.5B-Classifier"  # or Qwen2.5-3B-Quality-Classifier
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Predict quality score
+text = "Linear algebra is fundamental to understanding vector spaces and matrix operations in mathematics."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=8192)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    score = torch.sigmoid(outputs.logits).item()
+
+print(f"Quality Score: {score:.3f}")  # Output: Quality Score: 0.847
+```
+
+## Quality Score Interpretation
+
+| Score Range | Quality Level | Use Case |
+|-------------|---------------|----------|
+| 0.8 - 1.0 | Excellent | Premium training data |
+| 0.6 - 0.8 | Good | Standard training data |
+| 0.4 - 0.6 | Average | Conditional use |
+| 0.0 - 0.4 | Poor | Filter out |
+
+## Model Selection
+
+- **1.5B Model**: Faster inference, good for real-time applications
+- **3B Model**: Higher accuracy, better for batch processing
+
+## Limitations
+
+- Optimized for educational and mathematical content
+- May not generalize well to creative or subjective content
+- Scores should be used as guidance, not absolute judgments
+
+## Citation
+
+```bibtex
+@model{qwen25_quality_classifier_2025,
+  title={Qwen2.5 Text Quality Classifier},
+  author={Chao Li, Yifan Zhang},
+  year={2025},
+  publisher={OpenSQZ}
+}
+```
+
+## License
+
+Apache 2.0