File size: 3,191 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 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | from __future__ import annotations
import datasets
from mteb.abstasks.TaskMetadata import TaskMetadata
from ....abstasks.AbsTaskRetrieval import AbsTaskRetrieval
class FQuADRetrieval(AbsTaskRetrieval):
_EVAL_SPLITS = ["test", "validation"]
metadata = TaskMetadata(
name="FQuADRetrieval",
description="This dataset has been built from the French SQuad dataset.",
reference="https://huggingface.co/datasets/manu/fquad2_test",
dataset={
"path": "manu/fquad2_test",
"revision": "5384ce827bbc2156d46e6fcba83d75f8e6e1b4a6",
},
type="Retrieval",
category="s2p",
eval_splits=_EVAL_SPLITS,
eval_langs=["fra-Latn"],
main_score="ndcg_at_10",
date=("2019-11-01", "2020-05-01"),
form=["written"],
domains=["Encyclopaedic"],
task_subtypes=["Article retrieval"],
license="apache-2.0",
socioeconomic_status="mixed",
annotations_creators="human-annotated",
dialect=[],
text_creation="created",
bibtex_citation="""@inproceedings{dhoffschmidt-etal-2020-fquad,
title = "{FQ}u{AD}: {F}rench Question Answering Dataset",
author = "d{'}Hoffschmidt, Martin and
Belblidia, Wacim and
Heinrich, Quentin and
Brendl{\'e}, Tom and
Vidal, Maxime",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.107",
doi = "10.18653/v1/2020.findings-emnlp.107",
pages = "1193--1208",
}""",
n_samples={"test": 400, "validation": 100},
avg_character_length={"test": 937, "validation": 930},
)
def load_data(self, **kwargs):
if self.data_loaded:
return
dataset_raw = datasets.load_dataset(
**self.metadata_dict["dataset"],
)
# set valid_hasAns and test_hasAns as the validation and test splits (only queries with answers)
dataset_raw["validation"] = dataset_raw["valid_hasAns"]
del dataset_raw["valid_hasAns"]
dataset_raw["test"] = dataset_raw["test_hasAns"]
del dataset_raw["test_hasAns"]
# rename context column to text
dataset_raw = dataset_raw.rename_column("context", "text")
self.queries = {
eval_split: {
str(i): q["question"] for i, q in enumerate(dataset_raw[eval_split])
}
for eval_split in self.metadata_dict["eval_splits"]
}
self.corpus = {
eval_split: {str(row["title"]): row for row in dataset_raw[eval_split]}
for eval_split in self.metadata_dict["eval_splits"]
}
self.relevant_docs = {
eval_split: {
str(i): {str(q["title"]): 1}
for i, q in enumerate(dataset_raw[eval_split])
}
for eval_split in self.metadata_dict["eval_splits"]
}
self.data_loaded = True
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