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"""minipileoflaw"""
import json
import logging
import gzip
import csv
import pandas as pd
import ast
import datasets
try:
import lzma as xz
except ImportError:
import pylzma as xz
datasets.logging.set_verbosity_info()
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """
This is minipileoflaw
"""
_CITATION = """
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson, Peter and Krass, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
"""
_URL = "https://huggingface.co/datasets/tomrb/minipileoflaw"
BASE_URL = "https://huggingface.co/datasets/tomrb/minipileoflaw/resolve/main/data/minipileoflaw_"
subsets_names = ['r_legaladvice', 'courtlistener_docket_entry_documents', 'atticus_contracts', 'courtlistener_opinions', 'federal_register', 'bva_opinions', 'us_bills', 'cc_casebooks', 'tos', 'euro_parl', 'nlrb_decisions', 'scotus_oral_arguments', 'cfr', 'state_codes', 'scotus_filings', 'exam_outlines', 'edgar', 'cfpb_creditcard_contracts', 'constitutions', 'congressional_hearings', 'oig', 'olc_memos', 'uscode', 'founding_docs', 'ftc_advisory_opinions', 'echr', 'eurlex', 'tax_rulings', 'un_debates', 'fre', 'frcp', 'canadian_decisions', 'eoir', 'dol_ecab', 'icj-pcij', 'uspto_office_actions', 'ed_policy_guidance', 'acus_reports', 'hhs_alj_opinions', 'sec_administrative_proceedings', 'fmshrc_bluebooks', 'resource_contracts', 'medicaid_policy_guidance', 'irs_legal_advice_memos', 'doj_guidance_documents']
_DATA_URL = {
key: {
"train": [f"{BASE_URL}{key}_train.jsonl"],
"validation": [f"{BASE_URL}{key}_validation.jsonl"]
}
for key in subsets_names
}
_VARIANTS = ["all"] + list(_DATA_URL.keys())
class MiniPileOfLaw(datasets.GeneratorBasedBuilder):
"""TODO"""
BUILDER_CONFIGS = [datasets.BuilderConfig(name) for name in _VARIANTS]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"created_timestamp": datasets.Value("string"),
"downloaded_timestamp": datasets.Value("string"),
"url": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_urls = {}
if self.config.name == "all":
data_sources = list(_DATA_URL.keys())
else:
data_sources = [self.config.name]
for split in ["train", "validation"]:
data_urls[split] = []
for source in data_sources:
for chunk in _DATA_URL[source][split]:
data_urls[split].append(chunk)
train_downloaded_files = dl_manager.download(data_urls["train"])
validation_downloaded_files = dl_manager.download(data_urls["validation"])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
),
]
def _generate_examples(self, filepaths):
"""This function returns the examples in the raw (text) form by iterating on all the files."""
id_ = 0
for filepath in filepaths:
logger.info("Generating examples from = %s", filepath)
try:
with open(filepath, "r", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
if example is not None and isinstance(example, dict):
yield id_, {
"text": example.get("text", ""),
"created_timestamp": example.get("created_timestamp", ""),
"downloaded_timestamp": example.get("downloaded_timestamp", ""),
"url": example.get("url", "")
}
id_ += 1
except Exception as e:
print("Error reading file:", filepath)
print("Exception:", str(e))
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