Datasets:
Tasks:
Question Answering
Modalities:
Text
Sub-tasks:
extractive-qa
Languages:
English
Size:
10K<n<100K
Tags:
License:
File size: 3,778 Bytes
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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
"""SQUAD: The Stanford Question Answering Dataset."""
import json
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
#_URL = "https://huggingface.co/datasets/Lexi/NQ_squad_format/blob/main/"
_URLS = {
"train": "train.json",
"dev": "dev_incomplete.json",
"test": "openbook_beam_5.json"
}
class SquadConfig(datasets.BuilderConfig):
"""BuilderConfig for SQUAD."""
def __init__(self, **kwargs):
"""BuilderConfig for SQUAD.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SquadConfig, self).__init__(**kwargs)
class Squad(datasets.GeneratorBasedBuilder):
"""SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
BUILDER_CONFIGS = [
SquadConfig(
name="plain_text",
version=datasets.Version("1.0.0", ""),
description="Plain text",
),
]
def _info(self):
return datasets.DatasetInfo(
#description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string"),
"answer_start": datasets.Value("int32"),
}
),
}
),
# No default supervised_keys (as we have to pass both question
# and context as input).
supervised_keys=None,
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["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
key = 0
print(filepath)
with open(filepath, 'rb') as f:
data = json.load(f)
print("example data: ", data[0])
print("number of data: ", len(data))
print("data keys: ", data[0].keys())
for line in data:
yield key, {
"context": line['context'],
"question": line["question"],
"id": line["id"],
"answers": line['answers']
}
key += 1
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