K1mG0ng/AI-taste-business-finance-4B
K1mG0ng/AI-taste-business-finance-4B is a 4 billion parameter Qwen3-based model fine-tuned for evaluating social science research articles, specifically in Business and Finance. With a 32768 token context length, it excels at tasks like research question quality assessment, article-level prompt scoring, and social science journal tier benchmarking. This model is optimized for classifying research-article prompts into discrete quality levels, achieving a Top-1 accuracy of 53.50% and a Top-1+2 accuracy of 82.50% on 200 samples.
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AI-Taste-Business-Finance-4B Overview
This model, developed by K1mG0ng, is a fine-tuned Qwen3 4B architecture specifically designed for evaluating social science research articles, with a primary focus on Business and Finance content. It leverages a 32768 token context length to process detailed article prompts.
Key Capabilities
- Research Article Evaluation: Optimized for ranking or classifying research-article prompts into discrete quality levels.
- Specialized Assessment: Capable of assessing research question quality, scoring article-level prompts, and benchmarking social science journal tiers.
- High Accuracy: Achieves a Top-1 accuracy of 53.50% and a Top-1+2 accuracy of 82.50% on 200 samples, with a balanced accuracy and Macro F1 of 53.50% and 53.19% respectively.
Good for
- Academic Research: Ideal for researchers and institutions conducting internal experiments on structured article-evaluation prompts in Business and Finance.
- Content Quality Assessment: Useful for automated assessment of research article quality and relevance within specific social science domains.
- Journal Benchmarking: Can be applied to benchmark the quality of articles across different social science journal tiers.
This model is intended for research and internal experimentation and is not a general-purpose factual assistant or a substitute for expert peer review.