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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 | 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]
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