File size: 2,370 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 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | 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]
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