Comments (2)
You're right that we could use some more explanations or links to explain the metrics supported.
For examples on just using one metric, you can see that the README links to the test cases which show more detailed examples than just what is appropriate for the README:
https://github.com/Maluuba/nlg-eval#usage links to https://github.com/Maluuba/nlg-eval/blob/master/nlgeval/tests/test_nlgeval.py which has a test case called test_compute_metrics_omit
: https://github.com/Maluuba/nlg-eval/blob/master/nlgeval/tests/test_nlgeval.py#L88
so to just test one metric:
metric_to_use = 'ROUGE_L'
n = NLGEval(metrics_to_omit=NLGEval.valid_metrics-{metric_to_use})
n.compute_...
should work.
from nlg-eval.
Our paper http://arxiv.org/pdf/1706.09799 describes all the metrics very briefly and cites the papers that first proposed these metrics so you could read those in more details. In the research community, there is not much of a consensus on which of these metrics work better (people measure correlation with human evaluation to figure out which metrics are more suited for their task and results vary a lot) so people usually report several metrics. From what I have observed, BLEU-4 and METEOR are the most widely used ones but CIDEr usage has been increasing.
from nlg-eval.
Related Issues (20)
- Add metrics to compute fluency of references HOT 6
- Getting error "ValueError: could not convert string to float: '' HOT 2
- Why do I only output Bleu when I use it on Mac ?
- download for glove 6B fails HOT 3
- ModuleNotFoundError: No module named 'nlgeval' HOT 7
- Problem with "the object oriented API for repeated calls in a script - multiple examples" HOT 4
- _pickle.UnpicklingError: pickle data was truncated HOT 1
- zipfile.BadZipFile: File is not a zip file
- TypeError: compute_individual_metrics() missing 1 required positional argument: 'hyp' HOT 2
- Assertion Error HOT 1
- about the files downloaded HOT 1
- nlg-eval --setup
- BrokenPipeError HOT 1
- CIDEr score evaluates to 0.0 no matter what references and metrics I use
- thanks for the codes! I have a question: should I tokenize the predictions and reference texts before using this api? HOT 1
- nlg-eval --setup error can't download glove.6B.zip HOT 3
- Compatibility with gensim 4 HOT 1
- New releases? HOT 1
- v2.4.0 tag does not have the right version info HOT 1
- "Not found for url" while downloading weights HOT 6
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from nlg-eval.