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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
"""Wikipedia NQ dataset."""
import json
import datasets
_CITATION = """
@inproceedings{xorqa,
title = {{XOR} {QA}: Cross-lingual Open-Retrieval Question Answering},
author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
booktitle={NAACL-HLT},
year = {2021}
}
"""
_DESCRIPTION = "dataset load script for Wikipedia NQ"
base = "/home/czhang/src/task-sparse/tevatron/hgf_datasets/xor-tydi"
_DATASET_URLS = {
'eng_span': {
'train': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
'dev': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/dev/xor_dev_retrieve_eng_span_v1_1.jsonl',
'test': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/test/xor_test_retrieve_eng_span_q_only_v1_1.jsonl',
},
'full': {
'train': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/train/xor-t2e-100w.jsonl.gz',
'dev': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/dev/xor_dev_full_v1_1.jsonl',
'test': f'https://huggingface.co/datasets/Tevatron/xor-tydi/resolve/main/test/xor_test_full_q_only_v1_1.jsonl',
}
# 'test': f"{base}",
}
class XORTyDi(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.1")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=VERSION,
name="eng_span",
description="XOR-TyDI train/dev/test datasets of English Span Task"),
datasets.BuilderConfig(
version=VERSION,
name="full",
description="XOR-TyDI train/dev/test datasets of Full Task"),
]
def _info(self):
features = datasets.Features({
'query_id': datasets.Value('string'),
'query': datasets.Value('string'),
'answers': [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="",
# License for the dataset if available
license="",
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
group = self.config.name
if self.config.data_files:
downloaded_files = self.config.data_files
else:
downloaded_files = dl_manager.download_and_extract(_DATASET_URLS[group])
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."""
def process_train_entry(data):
if data.get('negative_passages') is None:
data['negative_passages'] = []
if data.get('positive_passages') is None:
data['positive_passages'] = []
if data.get('answers') is None:
data['answers'] = []
return data['query_id'], data
def process_dev_test_entry(data):
return data["id"], {
"query_id": data["id"],
"query": data["question"],
"answers": data.get("answers", []),
"positive_passages": [],
"negative_passages": [],
}
for filepath in files:
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
if "id" in data and "query_id" not in data:
yield process_dev_test_entry(data)
else:
yield process_train_entry(data)
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