Datasets:

PiC
/

phrase_similarity

	# 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


	logger = datasets.logging.get_logger(__name__)


	_CITATION = """\
	@article{pham2022PiC,
	title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
	author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
	journal={arXiv preprint arXiv:2207.09068},
	year={2022}
	}
	"""

	_DESCRIPTION = """\
	Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0.
	"""

	_HOMEPAGE = "https://phrase-in-context.github.io/"

	_LICENSE = "CC-BY-NC-4.0"

	_URL = "https://auburn.edu/~tmp0038/PiC/"
	_SPLITS = {
	"train": "train-hard-v2.0.1.json",
	"dev": "dev-hard-v2.0.1.json",
	"test": "test-hard-v2.0.1.json",
	}

	_PS = "PS-hard"


	class PSConfig(datasets.BuilderConfig):
	"""BuilderConfig for Phrase Similarity in PiC."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Phrase Similarity in PiC.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(PSConfig, self).__init__(**kwargs)


	class PhraseSimilarity(datasets.GeneratorBasedBuilder):
	"""Phrase Similarity in PiC dataset. Version 2.0.1. Verified PS labels"""

	BUILDER_CONFIGS = [
	PSConfig(
	name=_PS,
	version=datasets.Version("2.0.1"),
	description="The PiC Dataset for Phrase Similarity"
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"phrase1": datasets.Value("string"),
	"phrase2": datasets.Value("string"),
	"sentence1": datasets.Value("string"),
	"sentence2": datasets.Value("string"),
	"label": datasets.ClassLabel(num_classes=2, names=["negative", "positive"]),
	"idx": 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,
	)

	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_ps = json.load(f)
	for example in pic_ps["data"]:
	yield key, {
	"phrase1": example["phrase1"],
	"phrase2": example["phrase2"],
	"sentence1": example["sentence1"],
	"sentence2": example["sentence2"],
	"label": example["label"],
	"idx": example["idx"]
	}
	key += 1