Datasets:
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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.
# Lint as: python3
import json
import datasets
from collections import defaultdict
from dataclasses import dataclass
_CITATION = '''
'''
languages = ['ar', 'bn', 'en', 'es', 'fa', 'fi', 'fr', 'hi', 'id', 'ja', 'ko', 'ru', 'sw', 'te', 'th', 'zh']
non_surprise_languages = languages
_DESCRIPTION = 'dataset load script for MIRACL'
_DATASET_URLS = {
lang: {
'train': [
f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-train.tsv',
f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/qrels/qrels.miracl-v1.0-{lang}-train.tsv',
],
'dev': [
f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/topics/topics.miracl-v1.0-{lang}-dev.tsv',
f'https://huggingface.co/datasets/miracl/miracl/resolve/main/miracl-v1.0-{lang}/qrels/qrels.miracl-v1.0-{lang}-dev.tsv',
]
} for lang in languages
}
def load_topic(fn):
qid2topic = {}
with open(fn, encoding="utf-8") as f:
for line in f:
qid, topic = line.strip().split('\t')
qid2topic[qid] = topic
return qid2topic
def load_qrels(fn):
qrels = defaultdict(dict)
with open(fn, encoding="utf-8") as f:
for line in f:
qid, _, docid, rel = line.strip().split('\t')
qrels[qid][docid] = int(rel)
return qrels
class MIRACL(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=lang, description=f'MIRACL 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://project-miracl.github.io',
# 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])
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepaths': downloaded_files['train'],
},
),
datasets.SplitGenerator(
name='dev',
gen_kwargs={
'filepaths': downloaded_files['dev'],
},
),
]
return splits
def _generate_examples(self, filepaths):
lang = self.config.name
miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
docid2doc = {doc['docid']: (doc['title'], doc['text']) for doc in miracl_corpus}
topic_fn, qrel_fn = filepaths
qid2topic = load_topic(topic_fn)
qrels = load_qrels(qrel_fn)
for qid in qid2topic:
data = {}
data['query_id'] = qid
data['query'] = qid2topic[qid]
pos_docids = [docid for docid, rel in qrels[qid].items() if rel == 1]
neg_docids = [docid for docid, rel in qrels[qid].items() if rel == 0]
data['positive_passages'] = [{
'docid': docid,
**dict(zip(['title', 'text'], docid2doc[docid]))
} for docid in pos_docids]
data['negative_passages'] = [{
'docid': docid,
**dict(zip(['title', 'text'], docid2doc[docid]))
} for docid in neg_docids]
yield qid, data
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