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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
"""PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search."""
import json
import os.path
import datasets
from datasets.tasks import QuestionAnsweringExtractive
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
"""
_DESCRIPTION = """\
"""
_HOMEPAGE = ""
_LICENSE = "CC-BY-4.0"
_URL = "https://auburn.edu/~tmp0038/PiC/"
_SPLITS = {
"train": "train-v1.0.json",
"dev": "dev-v1.0.json",
"test": "test-v1.0.json",
}
_PR_PASS = "PR-pass"
_PR_PAGE = "PR-page"
class PRConfig(datasets.BuilderConfig):
"""BuilderConfig for Phrase Retrieval in PiC."""
def __init__(self, **kwargs):
"""BuilderConfig for Phrase Retrieval in PiC.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PRConfig, self).__init__(**kwargs)
class PhraseRetrieval(datasets.GeneratorBasedBuilder):
"""Phrase Retrieval in PiC dataset. Version 1.0."""
BUILDER_CONFIGS = [
PRConfig(
name=_PR_PASS,
version=datasets.Version("1.0.0"),
description="The PiC Dataset for Phrase Retrieval at short passage level (~11 sentences)"
),
PRConfig(
name=_PR_PAGE,
version=datasets.Version("1.0.0"),
description="The PiC Dataset for Phrase Retrieval at Wiki page level"
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"title": datasets.Value("string"),
"context": datasets.Value("string"),
"question": datasets.Value("string"),
"answers": datasets.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,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
task_templates=[
QuestionAnsweringExtractive(
question_column="question", context_column="context", answers_column="answers"
)
],
)
def _split_generators(self, dl_manager):
urls_to_download = {
"train": os.path.join(_URL, self.config.name, _SPLITS["train"]),
"dev": os.path.join(_URL, self.config.name, _SPLITS["dev"]),
"test": os.path.join(_URL, self.config.name, _SPLITS["test"])
}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
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
with open(filepath, encoding="utf-8") as f:
pic_pr = json.load(f)
for example in pic_pr["data"]:
title = example.get("title", "")
answer_starts = [answer["answer_start"] for answer in example["answers"]]
answers = [answer["text"] for answer in example["answers"]]
# Features currently used are "context", "question", and "answers".
# Others are extracted here for the ease of future expansions.
yield key, {
"title": title,
"context": example["context"],
"question": example["question"],
"id": example["id"],
"answers": {
"answer_start": answer_starts,
"text": answers,
},
}
key += 1
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