| from __future__ import annotations |
|
|
| import logging |
|
|
| import numpy as np |
|
|
| from mteb.MTEBResults import ScoresDict |
|
|
| from ..evaluation.evaluators import SummarizationEvaluator |
| from .AbsTask import AbsTask |
|
|
| logger = logging.getLogger(__name__) |
|
|
|
|
| class AbsTaskSummarization(AbsTask): |
| """Abstract class for summarization 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: |
| text: str |
| human_summaries: list[str] |
| machine_summaries: list[str] |
| relevance: list[float] (the score of the machine generated summaries) |
| """ |
|
|
| def __init__(self, **kwargs): |
| super().__init__(**kwargs) |
|
|
| @property |
| def min_score(self): |
| return self.metadata_dict["min_score"] |
|
|
| @property |
| def max_score(self): |
| return self.metadata_dict["max_score"] |
|
|
| def _evaluate_subset(self, model, data_split, **kwargs) -> ScoresDict: |
| normalized_scores = list( |
| map( |
| lambda x: (np.array(x) - self.min_score) |
| / (self.max_score - self.min_score), |
| data_split["relevance"], |
| ) |
| ) |
| evaluator = SummarizationEvaluator( |
| machine_summaries=data_split["machine_summaries"], |
| human_summaries=data_split["human_summaries"], |
| texts=data_split["text"], |
| gold_scores=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] |
|
|
