Qwen3-4B-Evasion is a specialized model fine-tuned from 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
Training framework: MS-SWIFT
Evasion taxonomy: Rasiah et al.

Contact

For questions or issues, please open an issue on the model repository.

goodlucky55555/Affine-final2

Qwen3-4B-Evasion

Model Description