WritingBench as an open source project provides complete code and data, but in the environment configuration needs to be completed by the user. The project does not provide a standard requirements.txt file , the user needs to manually install Python3.8 or later , and according to the functional requirements to install the relevant dependency libraries .
Core dependencies include libraries such as torch (GPU acceleration support), transformers (large model manipulation), and requests (data processing). Specialized judging models also require additional installation of PyTorch and CUDA support. The whole configuration process is relatively flexible, but also requires the user to have some Python environment management skills.
Although this design increases the threshold of use, it also avoids the version conflict problem caused by fixed dependencies and gives developers more freedom in configuration.
This answer comes from the articleWritingBench: a benchmarking assessment tool to test the writing skills of large modelsThe
