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K1mG0ng/AI-taste-sociology-4B

sourceHugging Facemitupdated 5mo agoView on Hugging Face
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AI-Taste-Sociology-4B

This repository provides a fine-tuned Qwen3 4B model for AI Taste experiments on social science research articles, with a current focus on Sociology 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-Sociology-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Validation

  • —Top-1 accuracy: 51.00% (200 samples)
  • —Top-1+2 accuracy: 77.00%
  • —Balanced accuracy: 51.00%
  • —Macro F1: 49.79%

Notes

  • —Subject: SOCIOLOGY
  • —Source checkpoint: /workspace/gongziqin/228/RQ/finetune/social_science_rqcontext_SOCIOLOGY_4B_/final_model
  • —Output quality depends on prompt format and label schema used at inference time.