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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.Wikipedia
# Lint as: python3
"""mMARCO Passage dataset."""
import json
import datasets
_CITATION = """
"""
_DESCRIPTION = "dataset load script for mMARCO bilingual-training datasets"
languages = [
"spanish"
]
_DATASET_URLS = {
lang: {
'train': f"https://huggingface.co/datasets/crystina-z/mmarco-train-bi/resolve/main/{lang}.json.gz",
} for lang in languages
}
class MMarcoPassage(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(
version=datasets.Version("0.0.1"),
name=lang,
description=f"mMARCO bilingual-training datasets for {lang}"
) for lang in languages
]
def _info(self):
features = datasets.Features({
'query_id': datasets.Value('string'),
'query_source': datasets.Value('string'),
'query_target': datasets.Value('string'),
'positive_passages_source': [
{'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
],
'positive_passages_target': [
{'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
],
'negative_passages_source': [
{'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
],
'negative_passages_target': [
{'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': 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="",
# 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_URLS[lang])
'''
if self.config.data_files:
downloaded_files = self.config.data_files
else:
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
'''
splits = [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"files": [downloaded_files[split]] if isinstance(downloaded_files[split], str) else
downloaded_files[split],
},
) for split in downloaded_files
]
return splits
def _generate_examples(self, files):
"""Yields examples."""
for filepath in files:
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
if data.get('negative_passages_source') is None:
data['negative_passages_source'] = []
data['negative_passages_target'] = []
if data.get('positive_passages_source') is None:
data['positive_passages_source'] = []
data['positive_passages_target'] = []
yield data['query_id'], data |