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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import datasets |
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import evaluate |
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_CITATION = """\ |
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tba |
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""" |
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_DESCRIPTION = """\ |
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Negation-aware version of BLEURT metric. |
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BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018) and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations and the CANNOT negation awareness dataset. |
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""" |
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_KWARGS_DESCRIPTION = """ |
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Calculates the NegBLEURT scores between references and predictions |
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Args: |
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predictions: list of predictions to score. Each prediction should be a string. |
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references: list of references, one for each prediction. Each reference should be a string |
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batch_size: batch_size for model inference. Default is 16 |
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Returns: |
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negBLEURT: List of NegBLEURT scores for all predictions |
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Examples: |
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>>> negBLEURT = evaluate.load('tum-nlp/negbleurt') |
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>>> predictions = ["Ray Charles is a legend.", "Ray Charles isn’t legendary."] |
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>>> references = ["Ray Charles is legendary.", "Ray Charles is legendary."] |
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>>> results = negBLEURT.compute(predictions=predictions, references=references) |
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>>> print(results) |
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{'negBLERUT': [0.8409, 0.2601]} |
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""" |
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@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION) |
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class NegBLEURT(evaluate.Metric): |
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def _info(self): |
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return evaluate.MetricInfo( |
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description=_DESCRIPTION, |
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citation=_CITATION, |
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inputs_description=_KWARGS_DESCRIPTION, |
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features=[ |
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datasets.Features( |
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{ |
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"predictions": datasets.Value("string", id="sequence"), |
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"references": datasets.Value("string", id="sequence"), |
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} |
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), |
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], |
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codebase_urls=["https://github.com/MiriUll/negation_aware_evaluation"] |
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) |
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def _download_and_prepare(self, dl_manager): |
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model_name = "tum-nlp/NegBLEURT" |
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self.tokenizer = AutoTokenizer.from_pretrained(model_name) |
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self.model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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def _compute( |
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self, predictions, references, batch_size=16 |
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): |
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scores_negbleurt = [] |
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for i in range(0, len(references), batch_size): |
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tokenized = self.tokenizer(references[i:i+batch_size], predictions[i:i+batch_size], return_tensors='pt', padding=True, max_length=512, truncation=True) |
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scores_negbleurt += self.model(**tokenized).logits.flatten().tolist() |
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return {'negBLEURT': scores_negbleurt} |
