| from __future__ import annotations |
|
|
| import logging |
|
|
| from ..evaluation.evaluators import STSEvaluator |
| from ..MTEBResults import ScoresDict |
| from .AbsTask import AbsTask |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class AbsTaskSTS(AbsTask): |
| """Abstract class for STS experiments. |
| |
| 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:: |
| sentence1: str |
| sentence2: str |
| score: float |
| """ |
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
|
|
| @property |
| def min_score(self) -> int: |
| return self.metadata_dict["min_score"] |
|
|
| @property |
| def max_score(self) -> int: |
| return self.metadata_dict["max_score"] |
|
|
| def _evaluate_subset(self, model, data_split, **kwargs) -> ScoresDict: |
| def normalize(x): |
| return (x - self.min_score) / (self.max_score - self.min_score) |
|
|
| normalized_scores = list(map(normalize, data_split["score"])) |
| evaluator = STSEvaluator( |
| data_split["sentence1"], |
| data_split["sentence2"], |
| normalized_scores, |
| **kwargs, |
| ) |
| scores = evaluator(model) |
|
|
| self._add_main_score(scores) |
| return scores |
|
|
| def _add_main_score(self, scores: ScoresDict) -> None: |
| m_score = self.metadata.main_score |
| dist, metric = m_score.split("_") |
| dist_mapping = {"cosine": "cos_sim"} |
| scores["main_score"] = scores[dist_mapping.get(dist, dist)][metric] |
|
|
