File size: 2,155 Bytes
73cc8d2 | 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 | from __future__ import annotations
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
class SyntecRetrieval(AbsTaskRetrieval):
_EVAL_SPLITS = ["test"]
metadata = TaskMetadata(
name="SyntecRetrieval",
description="This dataset has been built from the Syntec Collective bargaining agreement.",
reference="https://huggingface.co/datasets/lyon-nlp/mteb-fr-retrieval-syntec-s2p",
dataset={
"path": "lyon-nlp/mteb-fr-retrieval-syntec-s2p",
"revision": "19661ccdca4dfc2d15122d776b61685f48c68ca9",
},
type="Retrieval",
category="s2p",
eval_splits=_EVAL_SPLITS,
eval_langs=["fra-Latn"],
main_score="ndcg_at_10",
date=None,
form=None,
domains=None,
task_subtypes=None,
license=None,
socioeconomic_status=None,
annotations_creators=None,
dialect=[],
text_creation=None,
bibtex_citation=None,
n_samples={"test": 90},
avg_character_length={"test": 62},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
# fetch both subsets of the dataset
corpus_raw = datasets.load_dataset(
name="documents",
**self.metadata_dict["dataset"],
)
queries_raw = datasets.load_dataset(
name="queries",
**self.metadata_dict["dataset"],
)
eval_split = self.metadata_dict["eval_splits"][0]
self.queries = {
eval_split: {
str(i): q["Question"] for i, q in enumerate(queries_raw[eval_split])
}
}
corpus_raw = corpus_raw[eval_split]
corpus_raw = corpus_raw.rename_column("content", "text")
self.corpus = {eval_split: {str(row["id"]): row for row in corpus_raw}}
self.relevant_docs = {
eval_split: {
str(i): {str(q["Article"]): 1}
for i, q in enumerate(queries_raw[eval_split])
}
}
self.data_loaded = True
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