File size: 5,247 Bytes
5ae366d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
import json
import datasets
_CITATION = '''
@article{lawrie2024overview,
title={Overview of the TREC 2023 NeuCLIR track},
author={Lawrie, Dawn and MacAvaney, Sean and Mayfield, James and McNamee, Paul and Oard, Douglas W and Soldaini, Luca and Yang, Eugene},
year={2024}
}
'''
_LANGUAGES = [
'rus',
'fas',
'zho',
]
_DESCRIPTION = 'dataset load script for NeuCLIR 2023 Fast'
_DATASET_URLS = {
lang: {
'test': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/test-00000-of-00001.parquet',
} for lang in _LANGUAGES
}
_DATASET_CORPUS_URLS = {
f'corpus-{lang}': {
'corpus': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/corpus-00000-of-00001.parquet'
} for lang in _LANGUAGES
}
_DATASET_QUERIES_URLS = {
f'queries-{lang}': {
'queries': f'https://huggingface.co/datasets/MTEB/neuclir-2023-fast/resolve/main/neuclir-{lang}/queries-00000-of-00001.parquet'
} for lang in _LANGUAGES
}
class MLDR(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=lang, description=f'NeuCLIR dataset in language {lang}.'
) for lang in _LANGUAGES
] + [
datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=f'corpus-{lang}', description=f'corpus of NeuCLIR dataset in language {lang}.'
) for lang in _LANGUAGES
] + [
datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=f'queries-{lang}', description=f'queries of NeuCLIR dataset in language {lang}.'
) for lang in _LANGUAGES
]
def _info(self):
name = self.config.name
if name.startswith('corpus-'):
features = datasets.Features({
'_id': datasets.Value('string'),
'text': datasets.Value('string'),
'title': datasets.Value('string'),
})
elif name.startswith("queries-"):
features = datasets.Features({
'_id': datasets.Value('string'),
'text': datasets.Value('string'),
})
else:
features = datasets.Features({
'query-id': datasets.Value('string'),
'corpus-id': datasets.Value('string'),
'score': datasets.Value('int32'),
})
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
supervised_keys=None,
# Homepage of the dataset for documentation
homepage='https://arxiv.org/abs/2304.12367',
# License for the dataset if available
license=None,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
name = self.config.name
if name.startswith('corpus-'):
downloaded_files = dl_manager.download_and_extract(_DATASET_CORPUS_URLS[name])
splits = [
datasets.SplitGenerator(
name='corpus',
gen_kwargs={
'filepath': downloaded_files['corpus'],
},
),
]
elif name.startswith("queries-"):
downloaded_files = dl_manager.download_and_extract(_DATASET_QUERIES_URLS[name])
splits = [
datasets.SplitGenerator(
name='queries',
gen_kwargs={
'filepath': downloaded_files['queries'],
},
),
]
else:
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[name])
splits = [
datasets.SplitGenerator(
name='test',
gen_kwargs={
'filepath': downloaded_files['test'],
},
),
]
return splits
def _generate_examples(self, filepath):
import pandas as pd
name = self.config.name
df = pd.read_parquet(filepath)
if name.startswith('corpus-'):
for index, row in df.iterrows():
yield row['_id'], {
'_id': row['_id'],
'text': row['text'],
'title': row['title']
}
elif name.startswith("queries-"):
for index, row in df.iterrows():
yield row['_id'], {
'_id': row['_id'],
'text': row['text']
}
else:
for index, row in df.iterrows():
yield f"{row['query-id']}-----{row['corpus-id']}", {
'query-id': row['query-id'],
'corpus-id': row['corpus-id'],
'score': row['score']
} |