AI Taste Business Finance 4B

--- language: - en license: mit library_name: transformers base_model: Qwen/Qwen3-4B tags: - qwen3 - causal-lm - social-science - research-evaluation - fine-tuned --- # AI-Taste-Business-Finance-4B This repository provides a fine-tuned Qwen3 4B model for AI Taste experiments on social science research articles, with a current focus on Business, Finance article evaluation. ## Model Summary - Base model: `Qwen/Qwen3-4B` - Architecture: `Qwen3ForCausalLM` - Format: Hugging Face Transformers + Safetensors - Primary use: ranking or classifying research-article prompts into discrete quality levels ## Intended Use The model is intended for research and internal experimentation on structured article-evaluation prompts, including tasks such as: - research question quality assessment - article-level prompt scoring - social science journal tier benchmarking It is not intended as a general-purpose factual assistant or as a substitute for expert peer review. ## Files This repository keeps only the files needed to load and run the model: - model weights in `safetensors` - tokenizer files - model config and generation config Training-state artifacts such as trainer checkpoints and local run metadata are intentionally excluded. ## Example ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "K1mG0ng/AI-Taste-Business-Finance-4B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) ``` ## Validation - Top-1 accuracy: `53.50%` (200 samples) - Top-1+2 accuracy: `82.50%` - Balanced accuracy: `53.50%` - Macro F1: `53.19%` ## Notes - Subject: `BUSINESS, FINANCE` - Source checkpoint: `/workspace/gongziqin/228/RQ/finetune/social_science_rqcontext_BUSINESS,_FINANCE_4B_/final_model` - Output quality depends on prompt format and label schema used at inference time.