The dedicated judging model accompanying WritingBench is a special assessment tool optimized for Qwen-7B based on the following features:
- Multi-dimensional assessment: Can be scored on 5 dimensions simultaneously: logical coherence, domain expertise, style fit, etc.
- quantitative output: each dimension is given a specific score from 0-10
- Explanatory note: not only a score is given, but also a textual description of the reasons for the score
- Localized operation: Can be used in an offline environment after download to protect data privacy
Accessibility:
- Access the HuggingFace model library: https://huggingface.co/AQuarterMile/WritingBench-Critic-Model-Qwen-7B
- Download full model file (approx. 15GB)
- Configure the local model path in criter.py
- Requires PyTorch and a CUDA version.
It should be noted that the judging model requires strong computing resources, and it is recommended to use a GPU device with at least 24GB of video memory. Compared to scoring with a large model API, the evaluation results of a dedicated judging model are more stable and reproducible, which is especially suitable for R&D scenarios that require high-volume testing.
This answer comes from the articleWritingBench: a benchmarking assessment tool to test the writing skills of large modelsThe
May not be reproduced without permission:AI productivity tools " What are the features of WritingBench's judging model? How is it accessed?
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