ruff pytest_ruff.RuffError: flair/models/entity_mention_linking.py:1:8: F401 [*] `copy` imported but unused | 1 | import copy | ^^^^ F401 2 | import inspect 3 | import logging | = help: Remove unused import: `copy` flair/models/entity_mention_linking.py:761:9: D417 Missing argument description in the docstring for `__init__`: `batch_size` | 759 | """Entity linking model for the biomedical domain.""" 760 | 761 | def __init__( | ^^^^^^^^ D417 762 | self, 763 | candidate_generator: CandidateSearchIndex, | flair/models/entity_mention_linking.py:795:9: D417 Missing argument description in the docstring for `predict`: `batch_size` | 793 | return self._dictionary 794 | 795 | def predict( | ^^^^^^^ D417 796 | self, 797 | sentences: Union[List[Sentence], Sentence], | flair/models/entity_mention_linking.py:1099:24: C401 Unnecessary generator (rewrite as a `set` comprehension) | 1097 | spans = sentence.get_spans(gold_label_type) 1098 | for span in spans: 1099 | exps = set(exp.value for exp in span.get_labels(gold_label_type) if exp.value not in exclude_labels) | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ C401 1100 | 1101 | predictions = set(pred.value for pred in span.get_labels("predicted")) | = help: Rewrite as a `set` comprehension flair/models/entity_mention_linking.py:1101:31: C401 Unnecessary generator (rewrite as a `set` comprehension) | 1099 | exps = set(exp.value for exp in span.get_labels(gold_label_type) if exp.value not in exclude_labels) 1100 | 1101 | predictions = set(pred.value for pred in span.get_labels("predicted")) | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ C401 1102 | total += 1 1103 | if exps & predictions: | = help: Rewrite as a `set` comprehension

Black format check --- /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-21 21:38:23.594197+00:00 +++ /home/runner/work/flair/flair/flair/models/entity_mention_linking.py 2024-01-21 21:40:46.453971+00:00 @@ -1087,11 +1087,17 @@ raise NotImplementedError("evaluating an EntityMentionLinker with a gold_label_dictionary is not supported") if isinstance(data_points, Dataset): data_points = list(_iter_dataset(data_points)) - self.predict(data_points, top_k=k, pred_label_type="predicted", entity_label_types=gold_label_type, batch_size=mini_batch_size) + self.predict( + data_points, + top_k=k, + pred_label_type="predicted", + entity_label_types=gold_label_type, + batch_size=mini_batch_size, + ) hits = 0 total = 0 for sentence in data_points: spans = sentence.get_spans(gold_label_type) @@ -1106,10 +1112,6 @@ accuracy = hits / total detailed_results = f"Accuracy@{k}: {accuracy:0.2%}" scores = {"accuracy": accuracy, f"accuracy@{k}": accuracy, "loss": 0.0} - return Result( - main_score=accuracy, - detailed_results=detailed_results, - scores=scores - ) + return Result(main_score=accuracy, detailed_results=detailed_results, scores=scores)