yahoo_answers_qa / yahoo_answers_qa.py
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Update files from the datasets library (from 1.6.0)
2040e07
# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors.
#
# 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.
"""Yahoo Non-Factoid Question Dataset"""
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
Yahoo Non-Factoid Question Dataset is derived from Yahoo's Webscope L6 collection using machine learning techiques such \
that the questions would contain non-factoid answers.The dataset contains 87,361 questions and their corresponding answers. \
Each question contains its best answer along with additional other answers submitted by users. \
Only the best answer was reviewed in determining the quality of the question-answer pair.
"""
_URL = "https://ciir.cs.umass.edu/downloads/nfL6/nfL6.json.gz"
class YahooAnswersQa(datasets.GeneratorBasedBuilder):
"""Yahoo Non-Factoid Question Dataset"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [datasets.BuilderConfig(name="yahoo_answers_qa", version=datasets.Version("1.0.0"))]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"answer": datasets.Value("string"),
"nbestanswers": datasets.features.Sequence(datasets.Value("string")),
"main_category": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://ciir.cs.umass.edu/downloads/nfL6/index.html",
)
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file}),
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
if os.path.isdir(filepath):
filepath = os.path.join(filepath, "nfL6.json")
with open(filepath, encoding="utf-8") as f:
data = json.load(f)
for example in data:
yield example["id"], example