初始化项目，由ModelHub XC社区提供模型

Model: FutureMa/Qwen3-4B-Evasion
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+tags:
+- finance
+- earnings-call
+- evasion-detection
+- qwen3
+- text-classification
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- earnings-call-qa
+metrics:
+- accuracy
+- f1
+model-index:
+- name: Qwen3-4B-Evasion
+  results:
+  - task:
+      type: text-classification
+      name: Evasion Classification
+    metrics:
+    - type: accuracy
+      value: 0.7508
+      name: Accuracy
+    - type: f1
+      value: 0.7475
+      name: Weighted F1
+library_name: transformers
+pipeline_tag: text-classification
+---
+
+# Qwen3-4B-Evasion
+
+A fine-tuned model for detecting evasion levels in earnings call Q&A responses.
+
+## Model Description
+
+**Qwen3-4B-Evasion** is a specialized model fine-tuned from [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) for analyzing executive responses during earnings call Q&A sessions. The model classifies responses into three evasion categories based on the Rasiah taxonomy.
+
+## Intended Use
+
+### Primary Use Case
+- Analyze transparency and directness of executive responses in earnings calls
+- Financial discourse analysis
+- Corporate communication research
+
+### Classification Categories
+
+- **direct**: Clear, on-topic resolution to the question
+- **intermediate**: Partially responsive, incomplete, or softened answer
+- **fully_evasive**: Does not provide requested information
+
+## Training Details
+
+### Training Data
+- **Dataset**: 27,097 earnings call Q&A pairs
+- **Source**: Annotated by DeepSeek-V3.2 and Qwen3-Max models
+- **Label Distribution**:
+  - intermediate: 45.4%
+  - direct: 29.8%
+  - fully_evasive: 24.9%
+
+### Training Configuration
+- **Base Model**: Qwen/Qwen3-4B-Instruct-2507
+- **Training Type**: Full parameter fine-tuning
+- **Hardware**: 2x NVIDIA B200 GPUs
+- **Epochs**: 2
+- **Batch Size**: 32 (effective)
+- **Learning Rate**: 2e-5
+- **Framework**: MS-SWIFT
+
+## Performance
+
+Evaluated on 297 human-annotated benchmark samples:
+
+| Metric | Score |
+|--------|-------|
+| **Overall Accuracy** | 75.08% |
+| **Weighted F1** | 74.75% |
+| **Weighted Precision** | 77.56% |
+| **Weighted Recall** | 75.08% |
+
+### Per-Class Performance
+
+| Class | Precision | Recall | F1-Score | Support |
+|-------|-----------|--------|----------|---------|
+| direct | 86.67% | 54.74% | 67.10% | 95 |
+| intermediate | 63.12% | 80.91% | 70.92% | 110 |
+| fully_evasive | 85.42% | 89.13% | 87.23% | 92 |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "FutureMa/Qwen3-4B-Evasion"
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Prepare input
+question = "What are your revenue projections for next quarter?"
+answer = "We don't provide specific guidance on that."
+
+prompt = f"""You are a financial discourse analyst. Classify the evasion level of this executive response.
+
+Question: {question}
+Answer: {answer}
+
+Return JSON: {{"rasiah":"direct|intermediate|fully_evasive","confidence":0.00}}"""
+
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=128, temperature=1)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Limitations
+
+- **Direct Class Recall**: Lower recall (54.74%) for direct responses - model tends to be conservative
+- **Domain Specific**: Optimized for earnings call context, may not generalize to other domains
+- **English Only**: Trained exclusively on English text
+- **Confidence Calibration**: Model confidence scores may require further calibration
+
+## Bias and Ethical Considerations
+
+- Training data derived from corporate earnings calls may reflect existing biases in financial communication
+- Model should not be used as sole determinant for investment decisions
+- Human oversight recommended for critical applications
+
+## Citation
+
+```bibtex
+@misc{qwen3-4b-evasion,
+  author = {Shijian Ma},
+  title = {Qwen3-4B-Evasion: Earnings Call Evasion Detection Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/FutureMa/Qwen3-4B-Evasion}}
+}
+```
+
+## License
+
+Apache 2.0
+
+## Acknowledgments
+
+- Base model: [Qwen Team](https://huggingface.co/Qwen)
+- Training framework: [MS-SWIFT](https://github.com/modelscope/ms-swift)
+- Evasion taxonomy: Rasiah et al.
+
+## Contact
+
+For questions or issues, please open an issue on the model repository.