File size: 3,974 Bytes
8d2e0ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 |
"""EurlexResources"""
import json
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 = """
"""
_CITATION = """
"""
_URL = "https://huggingface.co/datasets/joelito/eurlex_resources"
_DATA_URL = f"{_URL}/resolve/main/data"
_LANGUAGES = [
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv",
]
_RESOURCE_TYPES = ["caselaw", "decision", "directive", "intagr", "proposal", "recommendation", "regulation"]
class EurlexResourcesConfig(datasets.BuilderConfig):
"""BuilderConfig for EurlexResources."""
def __init__(self, name: str, **kwargs):
"""BuilderConfig for EurlexResources.
Args:
name: combination of language and resource_type with _
language: One of bg,cs,da,de,el,en,es,et,fi,fr,ga,hr,hu,it,lt,lv,mt,nl,pl,pt,ro,sk,sl,sv or all
resource_type: One of caselaw, decision, directive, intagr, proposal, recommendation, regulation
**kwargs: keyword arguments forwarded to super.
"""
super(EurlexResourcesConfig, self).__init__(**kwargs)
self.name = name
self.language = name.split("_")[0]
self.resource_type = name.split("_")[1]
class EurlexResources(datasets.GeneratorBasedBuilder):
"""EurlexResources: A Corpus Covering the Largest EURLEX Resources"""
BUILDER_CONFIGS = [EurlexResourcesConfig(f"{language}_{resource_type}")
for resource_type in _RESOURCE_TYPES + ["all"]
for language in _LANGUAGES + ["all"]]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"celex": datasets.Value("string"),
"date": datasets.Value("string"),
"title": datasets.Value("string"),
"text": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_urls = []
languages = _LANGUAGES if self.config.language == "all" else [self.config.language]
resource_types = _RESOURCE_TYPES if self.config.resource_type == "all" else [self.config.resource_type]
for language in languages:
for resource_type in resource_types:
data_urls.append(f"{_DATA_URL}/{language}/{resource_type}.jsonl.xz")
downloaded_files = dl_manager.download(data_urls)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": 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 xz.open(open(filepath, "rb"), "rt", 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_, {
"celex": example.get("celex", ""),
"date": example.get("date", ""),
"title": example.get("title", ""),
"text": example.get("text", ""),
}
id_ += 1
except:
print("Error reading file:", filepath)
|