bench/README.md

bench/

• score.py: rescores an lvwb predictions file with the same rules as the Go scorer. No model is needed: python bench/score.py <result>.predictions.jsonl (add --data DIR to use local parquet or JSONL files instead of downloading the dataset).

Submitting a result

1. Run the model with lvwb run <model> on the standard profile.
2. Open a discussion on this dataset, titled Result: <model>. Attach the result .json and its .predictions.jsonl, and say whether the model is general or a specialist.
3. To show the score on the model's own page, open a PR on the model repo that adds the output of lvwb export <result.json> --eval-results as .eval_results/lvw-bench.yaml.