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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and 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.
# Lint as: python3
"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""
import json
import datasets
# from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """\
TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
"""
_URLS = {
"train": "data/topiocqa_train.jsonl",
"valid": "data/topiocqa_valid.jsonl",
}
class TopiOCQAConfig(datasets.BuilderConfig):
"""BuilderConfig for TopiOCQA."""
def __init__(self, **kwargs):
"""BuilderConfig for TopiOCQA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TopiOCQAConfig, self).__init__(**kwargs)
class TopiOCQA(datasets.GeneratorBasedBuilder):
"""TopiOCQA: Open-domain Conversational Question Answering with Topic Switching"""
BUILDER_CONFIGS = [
TopiOCQAConfig(
name="plain_text",
version=datasets.Version("1.0.1", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"Conversation_no": datasets.Value("int32"),
"Turn_no": datasets.Value("int32"),
"Question": datasets.Value("string"),
"Answer": datasets.Value("string"),
"Topic": datasets.Value("string"),
"Topic_section": datasets.Value("string"),
"Rationale": datasets.Value("string"),
"is_nq": datasets.Value("bool"),
"Context": datasets.features.Sequence(datasets.Value("string")),
"Additional_answers": datasets.features.Sequence(
{
"Answer": datasets.Value("string"),
"Topic": datasets.Value("string"),
"Topic_section": datasets.Value("string"),
"Rationale": datasets.Value("string"),
}
),
"Gold_passage": {
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"text": datasets.Value("string"),
}
}
),
supervised_keys=None,
homepage="https://mcgill-nlp.github.io/topiocqa/",
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["valid"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
with open(filepath, encoding="utf-8") as f:
for line in f:
data = json.loads(line)
yield key, {
"Conversation_no": data["Conversation_no"],
"Turn_no": data["Turn_no"],
"Question": data["Question"],
"Answer": data["Answer"],
"Topic": data["Topic"],
"Topic_section": data["Topic_section"],
"Rationale": data["Rationale"],
"is_nq": data["is_nq"],
"Context": data["Context"],
"Additional_answers": data["Additional_answers"],
"Gold_passage": data["Gold_passage"],
}
key += 1
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