Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
"""CUAD: A dataset for legal contract review curated by the Atticus Project.""" | |
from __future__ import absolute_import, division, print_function | |
import json | |
import os | |
import datasets | |
_CITATION = """\ | |
@article{hendrycks2021cuad, | |
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, | |
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball}, | |
journal={arXiv preprint arXiv:2103.06268}, | |
year={2021} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510 | |
commercial legal contracts that have been manually labeled to identify 41 categories of important | |
clauses that lawyers look for when reviewing contracts in connection with corporate transactions. | |
""" | |
_HOMEPAGE = "https://www.atticusprojectai.org/cuad" | |
_LICENSE = "CUAD is licensed under the Creative Commons Attribution 4.0 (CC BY 4.0) license." | |
_URL = "https://github.com/TheAtticusProject/cuad/raw/main/data.zip" | |
class CUAD(datasets.GeneratorBasedBuilder): | |
"""CUAD: A dataset for legal contract review curated by the Atticus Project.""" | |
VERSION = "1.0.0" | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"id": datasets.Value("string"), | |
"title": datasets.Value("string"), | |
"context": datasets.Value("string"), | |
"question": datasets.Value("string"), | |
"answers": datasets.features.Sequence( | |
{ | |
"text": datasets.Value("string"), | |
"answer_start": datasets.Value("int32"), | |
} | |
), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = dl_manager.download_and_extract(_URL) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "train_separate_questions.json"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(data_dir, "test.json"), "split": "test"}, | |
), | |
] | |
def _generate_examples( | |
self, filepath, split # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
): | |
"""Yields examples as (key, example) tuples.""" | |
with open(filepath, encoding="utf-8") as f: | |
cuad = json.load(f) | |
for example in cuad["data"]: | |
title = example.get("title", "").strip() | |
for paragraph in example["paragraphs"]: | |
context = paragraph["context"].strip() | |
for qa in paragraph["qas"]: | |
question = qa["question"].strip() | |
id_ = qa["id"] | |
answer_starts = [answer["answer_start"] for answer in qa["answers"]] | |
answers = [answer["text"].strip() for answer in qa["answers"]] | |
yield id_, { | |
"title": title, | |
"context": context, | |
"question": question, | |
"id": id_, | |
"answers": { | |
"answer_start": answer_starts, | |
"text": answers, | |
}, | |
} | |