| import json | |
| import datasets | |
| import pandas as pd | |
| from huggingface_hub.file_download import hf_hub_url | |
| try: | |
| import lzma as xz | |
| except ImportError: | |
| import pylzma as xz | |
| datasets.logging.set_verbosity_info() | |
| logger = datasets.logging.get_logger(__name__) | |
| _DESCRIPTION ="""\ | |
| """ | |
| _HOMEPAGE = "" | |
| _LICENSE = "" | |
| _CITATION = "" | |
| _URL = { | |
| 'data/' | |
| } | |
| _LANGUAGES = [ | |
| "fr","it","es","en","de","pt" | |
| ] | |
| _TYPES = [ | |
| "laws", "judgements" | |
| ] | |
| _SOURCES = [ | |
| "MultiLegalPile", "Wipolex", "Jug", "BVA", "CC", "IP", "SCOTUS", "SwissJudgementPrediction" | |
| "Gesetz", "Constitution", "CivilCode", "CriminalCode", | |
| ] | |
| _HIGHEST_NUMBER_OF_SHARDS = 4 | |
| class MultiLegalSBDConfig(datasets.BuilderConfig): | |
| def __init__(self, name:str, **kwargs): | |
| super( MultiLegalSBDConfig, self).__init__(**kwargs) | |
| self.name = name | |
| self.language = name.split("_")[0] | |
| self.type = name.split("_")[1] | |
| class MultiLegalSBD(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIG_CLASS = MultiLegalSBDConfig | |
| BUILDER_CONFIGS = [ | |
| MultiLegalSBDConfig(f"{language}_{type}") | |
| for language in _LANGUAGES + ['all'] | |
| for type in _TYPES + ["all"] | |
| ] | |
| DEFAULT_CONFIG_NAME = 'all_all' | |
| def _info(self): | |
| features = datasets.Features( | |
| { | |
| "text": datasets.Value("string"), | |
| "spans": [ | |
| { | |
| "start": datasets.Value("int64"), | |
| "end": datasets.Value("int64"), | |
| "label": datasets.Value("string"), | |
| "token_start": datasets.Value("int64"), | |
| "token_end": datasets.Value("int64") | |
| } | |
| ], | |
| "tokens": [ | |
| { | |
| "text": datasets.Value("string"), | |
| "start": datasets.Value("int64"), | |
| "end": datasets.Value("int64"), | |
| "id": datasets.Value("int64"), | |
| "ws": datasets.Value("bool") | |
| } | |
| ], | |
| "source": datasets.Value("string") | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features = features, | |
| homepage = _HOMEPAGE, | |
| citation=_CITATION | |
| ) | |
| def _split_generators(self, dl_manager): | |
| languages = _LANGUAGES if self.config.language == "all" else [self.config.language] | |
| types = _TYPES if self.config.type == 'all' else [self.config.type] | |
| split_generators = [] | |
| for split in [datasets.Split.TRAIN]: | |
| filepaths = [] | |
| for language in languages: | |
| for type in types: | |
| for shard in range(_HIGHEST_NUMBER_OF_SHARDS): | |
| try: | |
| filepaths.append(dl_manager.download((f'data/{language}_{type}_{shard}.jsonl.xz'))) | |
| except: | |
| break | |
| split_generators.append( | |
| datasets.SplitGenerator(name=split, gen_kwargs={'filepaths': filepaths}) | |
| ) | |
| return split_generators | |
| def _generate_examples(self,filepaths): | |
| id_ = 0 | |
| for filepath in filepaths: | |
| if filepath: | |
| logger.info("Generating examples from = %s", filepath) | |
| try: | |
| with xz.open(open(filepath,'rb'), 'rt', encoding='utf-8') as f: | |
| json_list = list(f) | |
| for json_str in json_list: | |
| example = json.loads(json_str) | |
| if example is not None and isinstance(example, dict): | |
| yield id_, example | |
| id_ +=1 | |
| except Exception: | |
| logger.exception("Error while processing file %s", filepath) | |