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"""Dataset for the Judgment Prediction task."""
import csv
import json
import lzma
import os
import datasets
try:
import lzma as xz
except ImportError:
import pylzma as xz
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
# You can copy an official description
_DESCRIPTION = """\
This dataset contains court decision for judgment prediction task.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"full": "https://huggingface.co/datasets/rcds/judgment_prediction/resolve/main/data/huggingface"
}
class JudgmentPrediction(datasets.GeneratorBasedBuilder):
"""This dataset contains court decision for judgment prediction task."""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="full", version=VERSION, description="This part of my dataset covers the whole dataset"),
]
DEFAULT_CONFIG_NAME = "full" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
if self.config.name == "full": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"decision_id": datasets.Value("string"),
"facts": datasets.Value("string"),
"considerations": datasets.Value("string"),
"label": datasets.Value("string"),
"law_area": datasets.Value("string"),
"language": datasets.Value("string"),
"year": datasets.Value("int32"),
"court": datasets.Value("string"),
"chamber": datasets.Value("string"),
"canton": datasets.Value("string"),
"region": datasets.Value("string")
# These are the features of your dataset like images, labels ...
}
)
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, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# 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):
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
urls = _URLS[self.config.name]
filepath_train = dl_manager.download(os.path.join(urls, "train.jsonl.xz"))
filepath_validation = dl_manager.download(os.path.join(urls, "validation.jsonl.xz"))
filepath_test = dl_manager.download(os.path.join(urls, "test.jsonl.xz"))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath_train,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath_validation,
"split": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": filepath_test,
"split": "test"
},
)
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
line_counter = 0
try:
with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for id, line in enumerate(f):
line_counter += 1
if line:
data = json.loads(line)
if self.config.name == "full":
yield id, {
"decision_id": data["decision_id"],
"facts": data["facts"],
"considerations": data["considerations"],
"label": data["label"],
"law_area": data["law_area"],
"language": data["language"],
"year": data["year"],
"court": data["court"],
"chamber": data["chamber"],
"canton": data["canton"],
"region": data["region"]
}
except lzma.LZMAError as e:
print(split, e)
if line_counter == 0:
raise e
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