Datasets:

trec-product-search
/

Product-Search-Triples

	# 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
	"""Product Search Triples dataset."""

	import json

	import datasets

	_CITATION = """

	"""

	_DESCRIPTION = "dataset load script training triples for TREC Product Search Track"

	_DATASET_URLS = {
	'train': "https://huggingface.co/datasets/trec-product-search/Product-Search-Triples-v0.1/resolve/main/train.jsonl.gz",
	'dev': "https://huggingface.co/datasets/trec-product-search/Product-Search-Triples-v0.1/resolve/main/dev.jsonl.gz",
	'test' : "https://huggingface.co/datasets/trec-product-search/Product-Search-Triples-v0.1/resolve/main/test.jsonl.gz",
	}


	class ProductSearch(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("0.0.1")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(version=VERSION,
	description="TREC Product Search train/dev/test datasets"),
	]

	def _info(self):
	features = datasets.Features({
	'query_id': datasets.Value('string'),
	'query': datasets.Value('string'),
	'positive_passages': [
	{'docid': datasets.Value('string'), 'title': datasets.Value('string'), 'text': datasets.Value('string')}
	],
	'negative_passages': [
	{'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):
	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') is None:
	data['negative_passages'] = []
	if data.get('positive_passages') is None:
	data['positive_passages'] = []
	yield data['query_id'], data