Benchmarked Resiliparse & added flag to evaluate parsers individually #25
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KhoomeiK
changed the title
lopuhin
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Oct 11, 2024
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Thanks a lot for contributing a new extractor @KhoomeiK . I left a few small comments - also if you prefer I could merge your PR as-is and address them in another PR.
Besides that, do you mind also updating the README with the result of the new parser, adding a line at the end of Result of packages added after original evaluation: table?
| try: | ||
| extractor_module = importlib.import_module(f'extractors.run_{name}') | ||
| extractor_module.main() | ||
| except: |
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I'd rather catch Exception here, e.g. see motivation in this (rejected) PEP https://peps.python.org/pep-0760/#motivation
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| except: | |
| except Exception: |
| 'accuracy={accuracy:.3f} ± {accuracy_std:.3f} ' | ||
| .format(name=name, **metrics)) | ||
| metrics_by_name[name] = metrics | ||
| else: |
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I think it would be best to refactor the code in a way which does not leave to having to repeat the reporting. For example, we could pass args.parser to evaluate function.
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Resiliparse is actively used by some AI labs to extract web data for LLM pre-training, but it has not been publicly benchmarked alongside many other similar web parsing tools. I've added an eval script for Resiliparse as well as its data output. I also added a flag to eval individual parsers separately.