| from __future__ import annotations |
|
|
| import logging |
| from collections import defaultdict |
|
|
| from datasets import Dataset |
|
|
| from ..encoder_interface import Encoder, EncoderWithQueryCorpusEncode |
| from ..evaluation.evaluators import PairClassificationEvaluator |
| from ..MTEBResults import ScoresDict |
| from .AbsTask import AbsTask |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class AbsTaskPairClassification(AbsTask): |
| """Abstract class for PairClassificationTasks |
| The similarity is computed between pairs and the results are ranked. Average precision |
| is computed to measure how well the methods can be used for pairwise pair classification. |
| |
| self.load_data() must generate a huggingface dataset with a split matching self.metadata_dict["eval_splits"], and assign it to self.dataset. It must contain the following columns: |
| sent1: list[str] |
| sent2: list[str] |
| labels: list[int] |
| """ |
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
|
|
| def _add_main_score(self, scores) -> None: |
| scores["main_score"] = scores["max"][self.metadata.main_score] |
|
|
| def _evaluate_subset( |
| self, |
| model: Encoder | EncoderWithQueryCorpusEncode, |
| dataset: Dataset, |
| **kwargs, |
| ) -> ScoresDict: |
| data_split = dataset[0] |
| logging.getLogger( |
| "sentence_transformers.evaluation.PairClassificationEvaluator" |
| ).setLevel(logging.WARN) |
| evaluator = PairClassificationEvaluator( |
| data_split["sent1"], data_split["sent2"], data_split["labels"], **kwargs |
| ) |
| scores = evaluator.compute_metrics(model) |
|
|
| |
| max_scores = defaultdict(list) |
| for sim_fct in scores: |
| for metric in ["accuracy", "f1", "ap"]: |
| max_scores[metric].append(scores[sim_fct][metric]) |
|
|
| for metric in max_scores: |
| max_scores[metric] = max(max_scores[metric]) |
|
|
| scores["max"] = dict(max_scores) |
|
|
| self._add_main_score(scores) |
| return scores |
|
|
