Datasets:

lexlms
/

legal_lama

	# 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.
	"""LegalLAMA: Legal LAnguage Model Analysis (LAMA) (LAMA) dataset."""

	import json
	import os
	import datasets


	MAIN_CITATION = """
	@inproceedings{chalkidis-garneau-etal-2023-lexlms,
	title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
	author = "Chalkidis*, Ilias and
	Garneau*, Nicolas and
	Goanta, Catalina and
	Katz, Daniel Martin and
	Søgaard, Anders",
	booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
	month = july,
	year = "2023",
	address = "Toronto, Canada",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/xxx",
	}
	"""

	_DESCRIPTION = """LegalLAMA: Legal LAnguage Model Analysis (LAMA) (LAMA) dataset."""
	MAIN_PATH = 'https://huggingface.co/datasets/lexlms/legal_lama/resolve/main'


	class LegalLAMAConfig(datasets.BuilderConfig):
	"""BuilderConfig for XAI - Fairness."""

	def __init__(
	self,
	data_url,
	**kwargs,
	):
	"""BuilderConfig for LegalLAMA.

	Args:
	data_url: `string`, url to download the zip file from
	**kwargs: keyword arguments forwarded to super.
	"""
	super(LegalLAMAConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
	self.data_url = data_url


	class LegalLAMA(datasets.GeneratorBasedBuilder):
	"""LegalLAMA: A multilingual benchmark for evaluating fairness in legal text processing. Version 1.0"""

	BUILDER_CONFIGS = [
	LegalLAMAConfig(
	name="canadian_crimes",
	data_url=os.path.join(MAIN_PATH, "canadian_crimes.jsonl"),
	),
	LegalLAMAConfig(
	name="canadian_sections",
	data_url=os.path.join(MAIN_PATH, "canadian_sections.jsonl"),
	),
	LegalLAMAConfig(
	name="cjeu_terms",
	data_url=os.path.join(MAIN_PATH, "cjeu_terms.jsonl"),
	),
	LegalLAMAConfig(
	name="ecthr_terms",
	data_url=os.path.join(MAIN_PATH, "ecthr_terms.jsonl"),
	),
	LegalLAMAConfig(
	name="ecthr_articles",
	data_url=os.path.join(MAIN_PATH, "ecthr_articles.jsonl"),
	),
	LegalLAMAConfig(
	name="us_crimes",
	data_url=os.path.join(MAIN_PATH, "us_crimes.jsonl"),
	),
	LegalLAMAConfig(
	name="us_terms",
	data_url=os.path.join(MAIN_PATH, "us_terms.jsonl"),
	),
	LegalLAMAConfig(
	name="contract_types",
	data_url=os.path.join(MAIN_PATH, "contract_types.jsonl"),
	),
	LegalLAMAConfig(
	name="contract_sections",
	data_url=os.path.join(MAIN_PATH, "contract_sections.jsonl"),
	),
	]

	def _info(self):
	features = {"text": datasets.Value("string"), "label": datasets.Value("string"), "category": datasets.Value("string")}
	return datasets.DatasetInfo(
	description=self.config.description,
	features=datasets.Features(features),
	homepage="https://huggingface.co/datasets/lexlms/legal_lama",
	citation=MAIN_CITATION,
	)

	def _split_generators(self, dl_manager):
	data_dir = dl_manager.download(self.config.data_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir,
	"split": "test",
	},
	),
	]

	def _get_category(self, sample):
	if 'canadian_article' in sample:
	category = sample['canadian_article']
	elif 'legal_topic' in sample:
	category = sample['legal_topic']
	elif 'echr_article' in sample:
	category = sample['echr_article']
	else:
	category = sample['obj_label']
	return category

	def _generate_examples(self, filepath, split):
	"""This function returns the examples in the raw (text) form."""
	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	example = {
	"text": data["masked_sentences"][0],
	"label": data["obj_label"],
	"category": self._get_category(data)
	}
	yield id_, example