File size: 1,876 Bytes
83d24b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | 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]
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