Datasets:
File size: 3,992 Bytes
1da9e10 |
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 |
"""
Loading script only for local Mr. Tydi. Used to generate the ir-format topic, qrels, folds locally.
"""
import os
import json
import datasets
from dataclasses import dataclass
_CITATION = '''
@article{mrtydi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
'''
languages = [
'arabic',
'bengali',
'english',
'indonesian',
'finnish',
'korean',
'russian',
'swahili',
'telugu',
'thai',
'japanese',
'combined',
]
dirname, abspath, os_join = os.path.dirname, os.path.abspath, os.path.join
current_working_dir = os.getcwd()
dataset_dir = current_working_dir
_DESCRIPTION = 'local dataset load script for Mr. TyDi'
_DATASET_LOCATIONS = {
lang: {
'train': os_join(dataset_dir, f'./mrtydi-v1.1-{lang}/train.jsonl.gz'),
'dev': os_join(dataset_dir, f'./mrtydi-v1.1-{lang}/dev.jsonl.gz'),
'test': os_join(dataset_dir, f'./mrtydi-v1.1-{lang}/test.jsonl.gz'),
} for lang in languages
}
print(_DATASET_LOCATIONS)
class LocalMrTyDi(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(
version=datasets.Version('1.1.0'),
name=lang, description=f'Mr TyDi dataset in language {lang}.'
) for lang in languages
]
def _info(self):
features = datasets.Features({
'query_id': datasets.Value('string'),
'query': datasets.Value('string'),
'positive_passages': [{
'docid': datasets.Value('string'),
'text': datasets.Value('string'), 'title': datasets.Value('string')
}],
'negative_passages': [{
'docid': datasets.Value('string'),
'text': datasets.Value('string'), 'title': datasets.Value('string'),
}],
})
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://github.com/castorini/mr.tydi',
# License for the dataset if available
license='',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
lang = self.config.name
downloaded_files = dl_manager.download_and_extract(_DATASET_LOCATIONS[lang])
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepath': downloaded_files['train'],
},
),
datasets.SplitGenerator(
name='dev',
gen_kwargs={
'filepath': downloaded_files['dev'],
},
),
datasets.SplitGenerator(
name='test',
gen_kwargs={
'filepath': downloaded_files['test'],
},
),
]
# splits = [
# datasets.SplitGenerator(
# name='train',
# gen_kwargs={
# 'filepath': downloaded_files['train'],
# },
# ),
# ]
return splits
def _generate_examples(self, filepath):
with open(filepath) as f:
for i, line in enumerate(f):
data = json.loads(line)
for feature in ['negative_passages', 'positive_passages']:
if data.get(feature) is None:
data[feature] = []
yield data['query_id'], data
|