| """ |
| Source: https://github.com/ZurichNLP/recognizing-semantic-differences |
| MIT License |
| Copyright (c) 2023 University of Zurich |
| """ |
|
|
| from typing import List |
|
|
| import torch |
|
|
| from recognizers.feature_based import FeatureExtractionRecognizer |
| from recognizers.utils import DifferenceSample, cos_sim |
|
|
|
|
| class DiffAlign(FeatureExtractionRecognizer): |
|
|
| def __str__(self): |
| return f"DiffAlign(model={self.pipeline.model.name_or_path}, layer={self.layer}" |
|
|
| @torch.no_grad() |
| def _predict_all(self, |
| a: List[str], |
| b: List[str], |
| **kwargs, |
| ) -> List[DifferenceSample]: |
| outputs_a = self.encode_batch(a, **kwargs) |
| outputs_b = self.encode_batch(b, **kwargs) |
| subwords_by_words_a = [self._get_subwords_by_word(sentence) for sentence in a] |
| subwords_by_words_b = [self._get_subwords_by_word(sentence) for sentence in b] |
| subword_labels_a = [] |
| subword_labels_b = [] |
| for i in range(len(a)): |
| cosine_similarities = cos_sim(outputs_a[i], outputs_b[i]) |
| max_similarities_a = torch.max(cosine_similarities, dim=1).values |
| max_similarities_b = torch.max(cosine_similarities, dim=0).values |
| subword_labels_a.append((1 - max_similarities_a)) |
| subword_labels_b.append((1 - max_similarities_b)) |
| samples = [] |
| for i in range(len(a)): |
| labels_a = self._subword_labels_to_word_labels(subword_labels_a[i], subwords_by_words_a[i]) |
| labels_b = self._subword_labels_to_word_labels(subword_labels_b[i], subwords_by_words_b[i]) |
| samples.append(DifferenceSample( |
| tokens_a=tuple(a[i].split()), |
| tokens_b=tuple(b[i].split()), |
| labels_a=tuple(labels_a), |
| labels_b=tuple(labels_b), |
| )) |
| return samples |
|
|
