Datasets:
Tasks:
Text Classification
Sub-tasks:
semantic-similarity-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 4,752 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
"""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
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