| from __future__ import annotations |
|
|
| import logging |
|
|
| from datasets import Dataset |
|
|
| from ..evaluation.evaluators import BitextMiningEvaluator |
| from ..MTEBResults import HFSubset, ScoresDict |
| from .AbsTask import AbsTask |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class AbsTaskBitextMining(AbsTask): |
| """Abstract class for BitextMining tasks |
| The similarity is computed between pairs and the results are ranked. |
| |
| 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: |
| id: str |
| sentence1: str |
| sentence2: str |
| """ |
|
|
| parallel_subsets = False |
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
|
|
| def evaluate(self, model, split, **kwargs) -> dict[HFSubset, ScoresDict]: |
| if not self.data_loaded: |
| self.load_data() |
|
|
| hf_subsets = ( |
| [l for l in self.dataset] |
| if self.is_multilingual or self.is_crosslingual |
| else ["default"] |
| ) |
|
|
| scores = {} |
| if self.parallel_subsets: |
| scores["default"] = self._evaluate_subset( |
| model, self.dataset[split], parallel=True, **kwargs |
| ) |
| else: |
| for hf_subet in hf_subsets: |
| logger.info( |
| f"\nTask: {self.metadata_dict['name']}, split: {split}, subset: {hf_subet}. Running..." |
| ) |
|
|
| if hf_subet not in self.dataset and hf_subet == "default": |
| data_split = self.dataset[split] |
| else: |
| data_split = self.dataset[hf_subet][split] |
| scores[hf_subet] = self._evaluate_subset( |
| model, data_split, subsets=["sentence1", "sentence2"], **kwargs |
| ) |
|
|
| return scores |
|
|
| def _evaluate_subset( |
| self, model, data_split: Dataset, parallel=False, **kwargs |
| ) -> ScoresDict: |
| evaluator = BitextMiningEvaluator(data_split, **kwargs) |
| metrics = evaluator(model) |
| if parallel: |
| for v in metrics.values(): |
| self._add_main_score(v) |
| else: |
| self._add_main_score(metrics) |
| return metrics |
|
|
| def _add_main_score(self, scores) -> None: |
| scores["main_score"] = scores[self.metadata.main_score] |
|
|
