Datasets:
File size: 5,054 Bytes
7fc1305 9bad33a 7fc1305 af7254c 7fc1305 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
# 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__)
# _CITATION = """\
# @article{2016arXiv160605250R,
# author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
# Konstantin and {Liang}, Percy},
# title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
# journal = {arXiv e-prints},
# year = 2016,
# eid = {arXiv:1606.05250},
# pages = {arXiv:1606.05250},
# archivePrefix = {arXiv},
# eprint = {1606.05250},
# }
# """
_DESCRIPTION = """\
TopiOCQA is an information-seeking conversational dataset with challenging topic switching phenomena.
"""
# _URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
_URLS = {
"train": "data/topiocqa_train.jsonl",
"valid": "data/topiocqa_valid.jsonl",
}
class TopiOCQAConfig(datasets.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for TopiOCQA.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TopiOCQAConfig, self).__init__(**kwargs)
class Squad(datasets.GeneratorBasedBuilder):
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
BUILDER_CONFIGS = [
TopiOCQAConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
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"),
# }
# ),
}
),
supervised_keys=None,
homepage="https://mcgill-nlp.github.io/topiocqa/",
# citation=_CITATION,
# task_templates=[
# QuestionAnsweringExtractive(
# question_column="Question", context_column="context", answers_column="answers"
# )
# ],
)
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"],
}
key += 1
|