File size: 4,824 Bytes
e91656b |
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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
import json
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """\
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
"""
_DATASETNAME = "xquad"
_DESCRIPTION = """\
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance.
The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 together (Rajpurkar et al., 2016)
with their professional translations into ten languages in their original implementation: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi and two in this dataloader: Vietnamese & Thai
"""
_HOMEPAGE = "https://github.com/google-deepmind/xquad"
_LICENSE = Licenses.CC_BY_SA_4_0.value
_LOCAL = False
_LANGUAGES = ["tha", "vie"]
_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class XQuADDataset(datasets.GeneratorBasedBuilder):
"""
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering performance.
The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set of SQuAD v1.1 together
with their professional translations into ten languages: Spanish, German, Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi.
"""
subsets = ["xquad.vi", "xquad.th"]
BUILDER_CONFIGS = [
SEACrowdConfig(
name="{sub}_source".format(sub=subset),
version=datasets.Version(_SOURCE_VERSION),
description="{sub} source schema".format(sub=subset),
schema="source",
subset_id="{sub}".format(sub=subset),
)
for subset in subsets
] + [
SEACrowdConfig(
name="{sub}_seacrowd_qa".format(sub=subset),
version=datasets.Version(_SEACROWD_VERSION),
description="{sub} SEACrowd schema".format(sub=subset),
schema="seacrowd_qa",
subset_id="{sub}".format(sub=subset),
)
for subset in subsets
]
DEFAULT_CONFIG_NAME = "xquad.vi_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{"context": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Features({"answer_start": [datasets.Value("int64")], "text": [datasets.Value("string")]}), "id": datasets.Value("string")}
)
elif self.config.schema == "seacrowd_qa":
features = schemas.qa_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN
)
]
def _generate_examples(self) -> Tuple[int, Dict]:
name_split = self.config.name.split("_")
subset_name = name_split[0]
dataset = datasets.load_dataset(_DATASETNAME, subset_name)
# Validation is the only subset name available for this dataset
for data in dataset['validation']:
if self.config.schema == "source":
yield data['id'], {
"context": data['context'],
"question": data['question'],
"answers": {"answer_start": str(data['answers']['answer_start'][0]), "text": data['answers']['text'][0]},
"id": data['id'],
}
elif self.config.schema == "seacrowd_qa":
yield data['id'], {
"question_id": data['id'],
"context": data['context'],
"question": data['question'],
"answer": {"answer_start": data['answers']['answer_start'][0], "text": data['answers']['text'][0]},
"id": data['id'],
"choices": [],
"type": "",
"document_id": data['id'],
"meta": {},
}
|