Datasets:
Sub-tasks:
masked-language-modeling
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
extended
ArXiv:
License:
File size: 5,213 Bytes
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# 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 |