def _info(self): if self.config_name not in [ "sst2", "mnli", "mnli_mismatched", "mnli_matched", "cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans" ]: raise KeyError( 'You should supply a configuration name selected in ' '["sst2", "mnli", "mnli_mismatched", "mnli_matched", ' '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]' ) return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value( 'int64' if self.config_name != 'stsb' else 'float32'), 'references': nlp.Value( 'int64' if self.config_name != 'stsb' else 'float32'), }), codebase_urls=[], reference_urls=[], format='numpy')
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Sequence(nlp.Value('string', id='token'), id='sequence'), 'references': nlp.Sequence(nlp.Sequence(nlp.Value('string', id='token'), id='sequence'), id='references'), }), codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"], reference_urls=["https://en.wikipedia.org/wiki/BLEU", "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213"] )
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, homepage="https://github.com/chakki-works/seqeval", inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Sequence(nlp.Value('string', id='label'), id='sequence'), 'references': nlp.Sequence(nlp.Value('string', id='label'), id='sequence'), }), codebase_urls=["https://github.com/chakki-works/seqeval"], reference_urls=["https://github.com/chakki-works/seqeval"])
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, homepage="https://github.com/Tiiiger/bert_score", inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('string', id='sequence'), 'references': nlp.Sequence(nlp.Value('string', id='sequence'), id='references'), }), codebase_urls=["https://github.com/Tiiiger/bert_score"], reference_urls=["https://github.com/Tiiiger/bert_score", "https://arxiv.org/abs/1904.09675"] )
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('string', id='sequence'), 'references': nlp.Value('string', id='sequence'), }), codebase_urls=["https://github.com/ns-moosavi/coval"], reference_urls=["https://github.com/ns-moosavi/coval", "https://www.aclweb.org/anthology/P16-1060", "http://www.conll.cemantix.org/2012/data.html"] )
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Sequence(nlp.Value('string', id='token'), id='sequence'), 'references': nlp.Sequence(nlp.Sequence(nlp.Value('string', id='token'), id='sequence'), id='references'), }), codebase_urls=["https://github.com/cnap/gec-ranking"], reference_urls=["https://github.com/cnap/gec-ranking"])
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value( 'int64' if self.config_name != 'sts-b' else 'float32'), 'references': nlp.Value( 'int64' if self.config_name != 'sts-b' else 'float32'), }), codebase_urls=[], reference_urls=[], format='numpy')
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, homepage="https://github.com/google-research/bleurt", inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('string', id='sequence'), 'references': nlp.Value('string', id='sequence'), }), codebase_urls=["https://github.com/google-research/bleurt"], reference_urls=[ "https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696" ])
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('string', id='sequence'), 'references': nlp.Value('string', id='sequence'), }), codebase_urls=[ "https://github.com/google-research/google-research/tree/master/rouge" ], reference_urls=[ "https://en.wikipedia.org/wiki/ROUGE_(metric)", "https://github.com/google-research/google-research/tree/master/rouge" ])
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('string', id='sequence'), 'references': nlp.Value('string', id='sequence') }), codebase_urls=[ "https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py" ], reference_urls=[ "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score", "https://en.wikipedia.org/wiki/METEOR" ])
def _info(self): return nlp.MetricInfo( description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': { "id": nlp.Value("string"), "prediction_text": nlp.Value("string") }, 'references': { "id": nlp.Value("string"), "answers": nlp.features.Sequence( {"text": nlp.Value("string"), "answer_start": nlp.Value("int32"),} ), }, }), codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"], reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"] )
def _info(self): return nlp.MetricInfo(description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=nlp.Features({ 'predictions': nlp.Value('float', id='sequence'), 'prob_y_hat': nlp.Value('float', id='sequence'), 'prob_y_hat_alpha': nlp.Value('float', id='sequence'), 'null_difference': nlp.Value('float', id='sequence'), 'model': nlp.Value('float', id='sequence'), 'tokenizer': nlp.Value('float', id='sequence'), 'mode': nlp.Value('string', id='sequence'), 'normalization': nlp.Value('bool', id='sequence'), }))