{"library":"rouge-metric","title":"ROUGE Metric","description":"A fast Python implementation of full ROUGE metrics for automatic summarization evaluation, also providing a Python wrapper for the official ROUGE-1.5.5.pl Perl script. It supports various ROUGE variants (N, L, W, S, SU) and multi-reference evaluation. The library is actively maintained with periodic updates.","language":"python","status":"active","last_verified":"Sun May 17","install":{"commands":["pip install rouge-metric","pip install git+https://github.com/li-plus/rouge-metric.git@master"],"cli":null},"imports":["from rouge_metric import Rouge","from rouge_metric import PerlRouge"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"from rouge_metric import Rouge\n\nhypothesis = 'The cat sat on the mat.'\nreference = 'The cat was on the mat.'\n\nrouge = Rouge()\nscores = rouge.evaluate(hypothesis, reference)\n\nprint(scores)\n\n# Example with multiple references (list of lists of tokens)\nhyp_tokens = ['the', 'cat', 'sat', 'on', 'the', 'mat']\nref1_tokens = ['the', 'cat', 'was', 'on', 'the', 'mat']\nref2_tokens = ['a', 'feline', 'was', 'resting', 'on', 'the', 'rug']\n\nscores_multi_ref = rouge.evaluate_from_tokens(hyp_tokens, [ref1_tokens, ref2_tokens])\nprint(scores_multi_ref)","lang":"python","description":"This quickstart demonstrates how to use the pure Python ROUGE implementation to evaluate a single hypothesis against a single reference string, and also how to evaluate tokenized input against multiple references. 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