Datasets:
Tasks:
Fill-Mask
Multilinguality:
multilingual
Size Categories:
10M<n<100M
Language Creators:
found
Annotations Creators:
other
Source Datasets:
original
Tags:
License:
Commit
•
9b90a85
1
Parent(s):
f5a601e
added dataset card, creation script and data loader
Browse files- MultiLegalPileWikipediaFiltered.py +156 -0
- README.md +592 -0
- prepare_legal_data.py +195 -0
MultiLegalPileWikipediaFiltered.py
ADDED
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| 1 |
+
"""MultiLegalPileWikipediaFiltered"""
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| 2 |
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| 3 |
+
import json
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| 4 |
+
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| 5 |
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import datasets
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| 6 |
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from huggingface_hub.file_download import hf_hub_url
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| 7 |
+
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| 8 |
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try:
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| 9 |
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import lzma as xz
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| 10 |
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except ImportError:
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| 11 |
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import pylzma as xz
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| 12 |
+
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| 13 |
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datasets.logging.set_verbosity_info()
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| 14 |
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logger = datasets.logging.get_logger(__name__)
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| 15 |
+
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| 16 |
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_CITATION = """
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| 17 |
+
"""
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| 18 |
+
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| 19 |
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_DESCRIPTION = """
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| 20 |
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A filtered version of the MultiLegalPile dataset, together with wikipedia articles.
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| 21 |
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"""
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| 22 |
+
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| 23 |
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_REPO_ID = "joelito/MultiLegalPileWikipediaFiltered"
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| 24 |
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_URL = f"https://huggingface.co/datasets/{_REPO_ID}"
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| 25 |
+
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| 26 |
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_LANGUAGES = ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr",
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| 27 |
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"hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"]
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| 28 |
+
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| 29 |
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CASELAW = "caselaw"
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| 30 |
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CONTRACTS = "contracts"
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| 31 |
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LEGISLATION = "legislation"
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| 32 |
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OTHER = "other"
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| 33 |
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WIKIPEDIA = "wikipedia"
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| 34 |
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_TYPES = [CASELAW, CONTRACTS, LEGISLATION, OTHER, WIKIPEDIA]
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| 35 |
+
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| 36 |
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_JURISDICTONS = ["Austria", "Belgium", "Bulgaria", "Croatia", "Czechia", "Denmark", "Estonia", "Finland",
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| 37 |
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"France", "Germany", "Greece", "Hungary", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg",
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| 38 |
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"Malta", "Netherlands", "Poland", "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden",
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| 39 |
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"EU", "Switzerland", "UK", "US", "Canada", "N/A"]
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| 40 |
+
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| 41 |
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# 1 is standard for most languages, types
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| 42 |
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NUMBER_OF_SHARDS = {lang: {type: 1 for type in _TYPES} for lang in _LANGUAGES}
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| 43 |
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for lang in _LANGUAGES:
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| 44 |
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NUMBER_OF_SHARDS[lang][OTHER] = 0 # no other data for most languages
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| 45 |
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NUMBER_OF_SHARDS["en"][OTHER] = 15 # 15 other files for English
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| 46 |
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| 47 |
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NUMBER_OF_SHARDS["cs"][CASELAW] = 2
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| 48 |
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NUMBER_OF_SHARDS["da"][LEGISLATION] = 2
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| 49 |
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NUMBER_OF_SHARDS["de"][CASELAW] = 5
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| 50 |
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NUMBER_OF_SHARDS["de"][LEGISLATION] = 2
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| 51 |
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NUMBER_OF_SHARDS["de"][WIKIPEDIA] = 5
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| 52 |
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NUMBER_OF_SHARDS["el"][LEGISLATION] = 2
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| 53 |
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NUMBER_OF_SHARDS["en"][CASELAW] = 66
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| 54 |
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NUMBER_OF_SHARDS["en"][CONTRACTS] = 16
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| 55 |
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NUMBER_OF_SHARDS["en"][LEGISLATION] = 5
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| 56 |
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NUMBER_OF_SHARDS["en"][WIKIPEDIA] = 11
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| 57 |
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NUMBER_OF_SHARDS["es"][WIKIPEDIA] = 3
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| 58 |
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NUMBER_OF_SHARDS["fr"][CASELAW] = 3
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| 59 |
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NUMBER_OF_SHARDS["fr"][LEGISLATION] = 2
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| 60 |
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NUMBER_OF_SHARDS["fr"][WIKIPEDIA] = 4
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| 61 |
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NUMBER_OF_SHARDS["ga"][CASELAW] = 0
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| 62 |
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NUMBER_OF_SHARDS["ga"][CONTRACTS] = 0
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| 63 |
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NUMBER_OF_SHARDS["it"][LEGISLATION] = 2
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| 64 |
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NUMBER_OF_SHARDS["it"][WIKIPEDIA] = 3
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| 65 |
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NUMBER_OF_SHARDS["nl"][LEGISLATION] = 2
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| 66 |
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NUMBER_OF_SHARDS["nl"][WIKIPEDIA] = 2
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| 67 |
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NUMBER_OF_SHARDS["pl"][WIKIPEDIA] = 2
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| 68 |
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NUMBER_OF_SHARDS["pt"][CASELAW] = 24
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| 69 |
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NUMBER_OF_SHARDS["pt"][WIKIPEDIA] = 2
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| 70 |
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NUMBER_OF_SHARDS["ro"][LEGISLATION] = 2
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| 71 |
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| 72 |
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| 73 |
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class MultiLegalPileWikipediaFilteredConfig(datasets.BuilderConfig):
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| 74 |
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"""BuilderConfig for MultiLegalPileWikipediaFiltered."""
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| 75 |
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| 76 |
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def __init__(self, name: str, **kwargs):
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| 77 |
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"""BuilderConfig for MultiLegalPileWikipediaFiltered.
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| 78 |
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Args:
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| 79 |
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name: combination of language and type with _
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| 80 |
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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
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| 81 |
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type: One of caselaw,contracts,legislation,other,wikipedia or all
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| 82 |
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**kwargs: keyword arguments forwarded to super.
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| 83 |
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"""
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| 84 |
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super(MultiLegalPileWikipediaFilteredConfig, self).__init__(**kwargs)
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| 85 |
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self.name = name
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| 86 |
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self.language = name.split("_")[0]
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| 87 |
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self.type = name.split("_")[1]
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| 88 |
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| 89 |
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| 90 |
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class MultiLegalPileWikipediaFiltered(datasets.GeneratorBasedBuilder):
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| 91 |
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"""
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| 92 |
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MultiLegalPileWikipediaFiltered:
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| 93 |
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A filtered dataset of multilingual legal data and wikipedias in the EU languages
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| 94 |
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"""
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| 95 |
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BUILDER_CONFIG_CLASS = MultiLegalPileWikipediaFilteredConfig
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| 96 |
+
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| 97 |
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BUILDER_CONFIGS = [MultiLegalPileWikipediaFilteredConfig(f"{language}_{type}")
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| 98 |
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for type in _TYPES + ["all"]
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| 99 |
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for language in _LANGUAGES + ["all"]]
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| 100 |
+
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| 101 |
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def _info(self):
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| 102 |
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return datasets.DatasetInfo(
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| 103 |
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description=_DESCRIPTION,
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| 104 |
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features=datasets.Features(
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| 105 |
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{
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| 106 |
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"language": datasets.Value("string"), # one of _LANGUAGES
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| 107 |
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"type": datasets.Value("string"), # one of _TYPES
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| 108 |
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"jurisdiction": datasets.Value("string"), # one of _JURISDICTONS
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| 109 |
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"text": datasets.Value("string"),
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| 110 |
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}
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| 111 |
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),
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| 112 |
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supervised_keys=None,
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| 113 |
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homepage=_URL,
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| 114 |
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citation=_CITATION,
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| 115 |
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)
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| 116 |
+
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| 117 |
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def _split_generators(self, dl_manager):
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| 118 |
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def download_url(file_name):
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| 119 |
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url = hf_hub_url(repo_id=_REPO_ID, filename=f"data/{file_name}.jsonl.xz", repo_type="dataset")
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| 120 |
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return dl_manager.download(url)
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| 121 |
+
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| 122 |
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languages = _LANGUAGES if self.config.language == "all" else [self.config.language]
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| 123 |
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types = _TYPES if self.config.type == "all" else [self.config.type]
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| 124 |
+
|
| 125 |
+
split_generators = []
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| 126 |
+
for split in [datasets.Split.TRAIN, datasets.Split.VALIDATION]:
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| 127 |
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filepaths = []
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| 128 |
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for language in languages:
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| 129 |
+
for type in types:
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| 130 |
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max_num_shards = NUMBER_OF_SHARDS[language][type] if split == datasets.Split.TRAIN else 1
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| 131 |
+
for shard in range(max_num_shards):
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| 132 |
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try:
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| 133 |
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url = download_url(f"{language}_{type}_{split}.{shard}")
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| 134 |
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filepaths.append(url)
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| 135 |
+
except Exception:
|
| 136 |
+
logger.exception(f"Error while processing url {url}")
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| 137 |
+
split_generators.append(
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| 138 |
+
datasets.SplitGenerator(name=split, gen_kwargs={"filepaths": filepaths})
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| 139 |
+
)
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| 140 |
+
return split_generators
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| 141 |
+
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| 142 |
+
def _generate_examples(self, filepaths):
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| 143 |
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"""This function returns the examples in the raw (text) form by iterating on all the files."""
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| 144 |
+
id_ = 0
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| 145 |
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for filepath in filepaths:
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| 146 |
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logger.info(f"Generating examples from = {filepath}", )
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| 147 |
+
try:
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| 148 |
+
with xz.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
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| 149 |
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for line in f:
|
| 150 |
+
if line:
|
| 151 |
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example = json.loads(line)
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| 152 |
+
if example is not None and isinstance(example, dict):
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| 153 |
+
yield id_, example
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| 154 |
+
id_ += 1
|
| 155 |
+
except Exception:
|
| 156 |
+
logger.exception(f"Error while processing file {filepath}")
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README.md
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@@ -0,0 +1,592 @@
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| 1 |
+
---
|
| 2 |
+
annotations_creators:
|
| 3 |
+
- other
|
| 4 |
+
language_creators:
|
| 5 |
+
- found
|
| 6 |
+
language:
|
| 7 |
+
- bg
|
| 8 |
+
- cs
|
| 9 |
+
- da
|
| 10 |
+
- de
|
| 11 |
+
- el
|
| 12 |
+
- en
|
| 13 |
+
- es
|
| 14 |
+
- et
|
| 15 |
+
- fi
|
| 16 |
+
- fr
|
| 17 |
+
- ga
|
| 18 |
+
- hr
|
| 19 |
+
- hu
|
| 20 |
+
- it
|
| 21 |
+
- lt
|
| 22 |
+
- lv
|
| 23 |
+
- mt
|
| 24 |
+
- nl
|
| 25 |
+
- pl
|
| 26 |
+
- pt
|
| 27 |
+
- ro
|
| 28 |
+
- sk
|
| 29 |
+
- sl
|
| 30 |
+
- sv
|
| 31 |
+
license:
|
| 32 |
+
- cc-by-4.0
|
| 33 |
+
multilinguality:
|
| 34 |
+
- multilingual
|
| 35 |
+
paperswithcode_id: null
|
| 36 |
+
pretty_name: "MultiLegalPileWikipediaFiltered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles."
|
| 37 |
+
size_categories:
|
| 38 |
+
- 10M<n<100M
|
| 39 |
+
source_datasets:
|
| 40 |
+
- original
|
| 41 |
+
task_categories:
|
| 42 |
+
- fill-mask
|
| 43 |
+
|
| 44 |
+
---
|
| 45 |
+
|
| 46 |
+
# Dataset Card for MultiLegalPileWikipediaFiltered: A filtered version of the MultiLegalPile dataset, together with wikipedia articles
|
| 47 |
+
|
| 48 |
+
## Table of Contents
|
| 49 |
+
|
| 50 |
+
- [Table of Contents](#table-of-contents)
|
| 51 |
+
- [Dataset Description](#dataset-description)
|
| 52 |
+
- [Dataset Summary](#dataset-summary)
|
| 53 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 54 |
+
- [Languages](#languages)
|
| 55 |
+
- [Dataset Structure](#dataset-structure)
|
| 56 |
+
- [Data Instances](#data-instances)
|
| 57 |
+
- [Data Fields](#data-fields)
|
| 58 |
+
- [Data Splits](#data-splits)
|
| 59 |
+
- [Dataset Creation](#dataset-creation)
|
| 60 |
+
- [Curation Rationale](#curation-rationale)
|
| 61 |
+
- [Source Data](#source-data)
|
| 62 |
+
- [Annotations](#annotations)
|
| 63 |
+
- [Personal and Sensitive Information](#personal-and-sensitive-information)
|
| 64 |
+
- [Considerations for Using the Data](#considerations-for-using-the-data)
|
| 65 |
+
- [Social Impact of Dataset](#social-impact-of-dataset)
|
| 66 |
+
- [Discussion of Biases](#discussion-of-biases)
|
| 67 |
+
- [Other Known Limitations](#other-known-limitations)
|
| 68 |
+
- [Additional Information](#additional-information)
|
| 69 |
+
- [Dataset Curators](#dataset-curators)
|
| 70 |
+
- [Licensing Information](#licensing-information)
|
| 71 |
+
- [Citation Information](#citation-information)
|
| 72 |
+
- [Contributions](#contributions)
|
| 73 |
+
|
| 74 |
+
## Dataset Description
|
| 75 |
+
|
| 76 |
+
- **Homepage:**
|
| 77 |
+
- **Repository:**
|
| 78 |
+
- **Paper:**
|
| 79 |
+
- **Leaderboard:**
|
| 80 |
+
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
|
| 81 |
+
|
| 82 |
+
### Dataset Summary
|
| 83 |
+
|
| 84 |
+
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
|
| 85 |
+
It spans over 24 languages and four legal text types.
|
| 86 |
+
|
| 87 |
+
### Supported Tasks and Leaderboards
|
| 88 |
+
|
| 89 |
+
The dataset supports the tasks of fill-mask.
|
| 90 |
+
|
| 91 |
+
### Languages
|
| 92 |
+
|
| 93 |
+
The following languages are supported:
|
| 94 |
+
bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
|
| 95 |
+
|
| 96 |
+
## Dataset Structure
|
| 97 |
+
|
| 98 |
+
It is structured in the following format: {language}_{text_type}_{shard}.jsonl.xz
|
| 99 |
+
|
| 100 |
+
text_type is one of the following:
|
| 101 |
+
|
| 102 |
+
- caselaw
|
| 103 |
+
- contracts
|
| 104 |
+
- legislation
|
| 105 |
+
- other
|
| 106 |
+
- wikipedia
|
| 107 |
+
|
| 108 |
+
|
| 109 |
+
Use the dataset like this:
|
| 110 |
+
```python
|
| 111 |
+
from datasets import load_dataset
|
| 112 |
+
|
| 113 |
+
config = 'en_contracts' # {language}_{text_type}
|
| 114 |
+
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
|
| 118 |
+
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
|
| 119 |
+
all_legislation').
|
| 120 |
+
|
| 121 |
+
### Data Instances
|
| 122 |
+
|
| 123 |
+
The file format is jsonl.xz and there is a `train` and `validation` split available.
|
| 124 |
+
Since some configurations are very small or non-existent, they might not contain a train split or not be present at all.
|
| 125 |
+
|
| 126 |
+
The complete dataset consists of five large subsets:
|
| 127 |
+
- [Native Multi Legal Pile](https://huggingface.co/datasets/joelito/Multi_Legal_Pile)
|
| 128 |
+
- [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources)
|
| 129 |
+
- [MC4 Legal](https://huggingface.co/datasets/joelito/mc4_legal)
|
| 130 |
+
- [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law)
|
| 131 |
+
- [EU Wikipedias](https://huggingface.co/datasets/joelito/EU_Wikipedias)
|
| 132 |
+
|
| 133 |
+
### Data Fields
|
| 134 |
+
|
| 135 |
+
[More Information Needed]
|
| 136 |
+
|
| 137 |
+
### Data Splits
|
| 138 |
+
|
| 139 |
+
There are two splits: train and validation. The validation split contains 1000 examples and the training split contains the rest of the data.
|
| 140 |
+
|
| 141 |
+
#### Data Size
|
| 142 |
+
|
| 143 |
+
```bash
|
| 144 |
+
$ xz --list data/*.xz
|
| 145 |
+
Strms Blocks Compressed Uncompressed Ratio Check Filename
|
| 146 |
+
1 1 167.6 MiB 3’276.3 MiB 0.051 CRC64 data/bg_caselaw_train.0.jsonl.xz
|
| 147 |
+
1 1 502.3 KiB 9’398.0 KiB 0.053 CRC64 data/bg_caselaw_validation.0.jsonl.xz
|
| 148 |
+
1 1 33.4 MiB 700.3 MiB 0.048 CRC64 data/bg_contracts_train.0.jsonl.xz
|
| 149 |
+
1 1 5’989.6 KiB 123.0 MiB 0.048 CRC64 data/bg_contracts_validation.0.jsonl.xz
|
| 150 |
+
1 1 418.5 MiB 8’931.0 MiB 0.047 CRC64 data/bg_legislation_train.0.jsonl.xz
|
| 151 |
+
1 1 5’029.4 KiB 103.1 MiB 0.048 CRC64 data/bg_legislation_validation.0.jsonl.xz
|
| 152 |
+
1 0 32 B 0 B --- CRC64 data/bg_other_validation.0.jsonl.xz
|
| 153 |
+
1 1 192.2 MiB 2’488.6 MiB 0.077 CRC64 data/bg_wikipedia_train.0.jsonl.xz
|
| 154 |
+
1 1 1’757.8 KiB 22.9 MiB 0.075 CRC64 data/bg_wikipedia_validation.0.jsonl.xz
|
| 155 |
+
1 1 476.9 MiB 4’126.1 MiB 0.116 CRC64 data/cs_caselaw_train.0.jsonl.xz
|
| 156 |
+
1 1 259.8 MiB 2’556.9 MiB 0.102 CRC64 data/cs_caselaw_train.1.jsonl.xz
|
| 157 |
+
1 1 420.1 KiB 3’370.3 KiB 0.125 CRC64 data/cs_caselaw_validation.0.jsonl.xz
|
| 158 |
+
1 1 24.9 MiB 237.9 MiB 0.105 CRC64 data/cs_contracts_train.0.jsonl.xz
|
| 159 |
+
1 1 4’412.1 KiB 41.7 MiB 0.103 CRC64 data/cs_contracts_validation.0.jsonl.xz
|
| 160 |
+
1 1 361.2 MiB 3’488.9 MiB 0.104 CRC64 data/cs_legislation_train.0.jsonl.xz
|
| 161 |
+
1 1 10.3 MiB 91.6 MiB 0.112 CRC64 data/cs_legislation_validation.0.jsonl.xz
|
| 162 |
+
1 0 32 B 0 B --- CRC64 data/cs_other_validation.0.jsonl.xz
|
| 163 |
+
1 1 390.6 MiB 1’939.4 MiB 0.201 CRC64 data/cs_wikipedia_train.0.jsonl.xz
|
| 164 |
+
1 1 2’604.7 KiB 12.2 MiB 0.209 CRC64 data/cs_wikipedia_validation.0.jsonl.xz
|
| 165 |
+
1 1 252.5 MiB 1’529.7 MiB 0.165 CRC64 data/da_caselaw_train.0.jsonl.xz
|
| 166 |
+
1 1 555.9 KiB 3’227.1 KiB 0.172 CRC64 data/da_caselaw_validation.0.jsonl.xz
|
| 167 |
+
1 1 30.1 MiB 233.9 MiB 0.129 CRC64 data/da_contracts_train.0.jsonl.xz
|
| 168 |
+
1 1 2’897.6 KiB 23.6 MiB 0.120 CRC64 data/da_contracts_validation.0.jsonl.xz
|
| 169 |
+
1 1 476.9 MiB 3’325.8 MiB 0.143 CRC64 data/da_legislation_train.0.jsonl.xz
|
| 170 |
+
1 1 237.3 MiB 1’444.5 MiB 0.164 CRC64 data/da_legislation_train.1.jsonl.xz
|
| 171 |
+
1 1 3’232.5 KiB 60.6 MiB 0.052 CRC64 data/da_legislation_validation.0.jsonl.xz
|
| 172 |
+
1 0 32 B 0 B --- CRC64 data/da_other_validation.0.jsonl.xz
|
| 173 |
+
1 1 128.8 MiB 512.1 MiB 0.252 CRC64 data/da_wikipedia_train.0.jsonl.xz
|
| 174 |
+
1 1 1’514.1 KiB 5’476.3 KiB 0.276 CRC64 data/da_wikipedia_validation.0.jsonl.xz
|
| 175 |
+
1 1 476.9 MiB 2’803.8 MiB 0.170 CRC64 data/de_caselaw_train.0.jsonl.xz
|
| 176 |
+
1 1 476.9 MiB 2’821.4 MiB 0.169 CRC64 data/de_caselaw_train.1.jsonl.xz
|
| 177 |
+
1 1 476.9 MiB 2’720.2 MiB 0.175 CRC64 data/de_caselaw_train.2.jsonl.xz
|
| 178 |
+
1 1 476.9 MiB 2’704.1 MiB 0.176 CRC64 data/de_caselaw_train.3.jsonl.xz
|
| 179 |
+
1 1 460.5 MiB 2’504.5 MiB 0.184 CRC64 data/de_caselaw_train.4.jsonl.xz
|
| 180 |
+
1 1 594.0 KiB 3’416.4 KiB 0.174 CRC64 data/de_caselaw_validation.0.jsonl.xz
|
| 181 |
+
1 1 32.0 MiB 255.8 MiB 0.125 CRC64 data/de_contracts_train.0.jsonl.xz
|
| 182 |
+
1 1 3’037.7 KiB 24.7 MiB 0.120 CRC64 data/de_contracts_validation.0.jsonl.xz
|
| 183 |
+
1 1 476.9 MiB 3’386.0 MiB 0.141 CRC64 data/de_legislation_train.0.jsonl.xz
|
| 184 |
+
1 1 93.3 MiB 592.3 MiB 0.158 CRC64 data/de_legislation_train.1.jsonl.xz
|
| 185 |
+
1 1 3’265.9 KiB 20.5 MiB 0.156 CRC64 data/de_legislation_validation.0.jsonl.xz
|
| 186 |
+
1 0 32 B 0 B --- CRC64 data/de_other_validation.0.jsonl.xz
|
| 187 |
+
1 1 476.9 MiB 1’883.7 MiB 0.253 CRC64 data/de_wikipedia_train.0.jsonl.xz
|
| 188 |
+
1 1 476.9 MiB 1’891.6 MiB 0.252 CRC64 data/de_wikipedia_train.1.jsonl.xz
|
| 189 |
+
1 1 476.9 MiB 1’893.7 MiB 0.252 CRC64 data/de_wikipedia_train.2.jsonl.xz
|
| 190 |
+
1 1 476.9 MiB 1’894.1 MiB 0.252 CRC64 data/de_wikipedia_train.3.jsonl.xz
|
| 191 |
+
1 1 407.9 MiB 1’622.0 MiB 0.251 CRC64 data/de_wikipedia_train.4.jsonl.xz
|
| 192 |
+
1 1 1’172.5 KiB 4’210.2 KiB 0.278 CRC64 data/de_wikipedia_validation.0.jsonl.xz
|
| 193 |
+
1 1 344.7 MiB 6’908.3 MiB 0.050 CRC64 data/el_caselaw_train.0.jsonl.xz
|
| 194 |
+
1 1 870.4 KiB 14.3 MiB 0.060 CRC64 data/el_caselaw_validation.0.jsonl.xz
|
| 195 |
+
1 1 49.7 MiB 1’083.8 MiB 0.046 CRC64 data/el_contracts_train.0.jsonl.xz
|
| 196 |
+
1 1 4’701.3 KiB 101.6 MiB 0.045 CRC64 data/el_contracts_validation.0.jsonl.xz
|
| 197 |
+
1 1 476.9 MiB 10.2 GiB 0.046 CRC64 data/el_legislation_train.0.jsonl.xz
|
| 198 |
+
1 1 203.0 MiB 3’994.0 MiB 0.051 CRC64 data/el_legislation_train.1.jsonl.xz
|
| 199 |
+
1 1 9’744.3 KiB 186.6 MiB 0.051 CRC64 data/el_legislation_validation.0.jsonl.xz
|
| 200 |
+
1 0 32 B 0 B --- CRC64 data/el_other_validation.0.jsonl.xz
|
| 201 |
+
1 1 246.4 MiB 3’465.7 MiB 0.071 CRC64 data/el_wikipedia_train.0.jsonl.xz
|
| 202 |
+
1 1 2’591.7 KiB 35.6 MiB 0.071 CRC64 data/el_wikipedia_validation.0.jsonl.xz
|
| 203 |
+
1 1 476.9 MiB 2’188.6 MiB 0.218 CRC64 data/en_caselaw_train.0.jsonl.xz
|
| 204 |
+
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| 378 |
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1 0 32 B 0 B --- CRC64 data/hr_other_validation.0.jsonl.xz
|
| 379 |
+
1 1 110.3 MiB 425.5 MiB 0.259 CRC64 data/hr_wikipedia_train.0.jsonl.xz
|
| 380 |
+
1 1 1’743.8 KiB 6’170.1 KiB 0.283 CRC64 data/hr_wikipedia_validation.0.jsonl.xz
|
| 381 |
+
1 1 150.6 MiB 1’320.5 MiB 0.114 CRC64 data/hu_caselaw_train.0.jsonl.xz
|
| 382 |
+
1 1 423.8 KiB 3’496.6 KiB 0.121 CRC64 data/hu_caselaw_validation.0.jsonl.xz
|
| 383 |
+
1 1 26.9 MiB 266.0 MiB 0.101 CRC64 data/hu_contracts_train.0.jsonl.xz
|
| 384 |
+
1 1 3’532.6 KiB 36.1 MiB 0.096 CRC64 data/hu_contracts_validation.0.jsonl.xz
|
| 385 |
+
1 1 337.6 MiB 3’129.4 MiB 0.108 CRC64 data/hu_legislation_train.0.jsonl.xz
|
| 386 |
+
1 1 3’913.7 KiB 94.8 MiB 0.040 CRC64 data/hu_legislation_validation.0.jsonl.xz
|
| 387 |
+
1 0 32 B 0 B --- CRC64 data/hu_other_validation.0.jsonl.xz
|
| 388 |
+
1 1 364.2 MiB 1’835.0 MiB 0.198 CRC64 data/hu_wikipedia_train.0.jsonl.xz
|
| 389 |
+
1 1 1’719.5 KiB 8’000.8 KiB 0.215 CRC64 data/hu_wikipedia_validation.0.jsonl.xz
|
| 390 |
+
1 1 459.8 MiB 2’742.8 MiB 0.168 CRC64 data/it_caselaw_train.0.jsonl.xz
|
| 391 |
+
1 1 577.8 KiB 3’194.2 KiB 0.181 CRC64 data/it_caselaw_validation.0.jsonl.xz
|
| 392 |
+
1 1 31.2 MiB 240.4 MiB 0.130 CRC64 data/it_contracts_train.0.jsonl.xz
|
| 393 |
+
1 1 3’068.9 KiB 24.0 MiB 0.125 CRC64 data/it_contracts_validation.0.jsonl.xz
|
| 394 |
+
1 1 476.9 MiB 3’362.3 MiB 0.142 CRC64 data/it_legislation_train.0.jsonl.xz
|
| 395 |
+
1 1 38.9 MiB 238.7 MiB 0.163 CRC64 data/it_legislation_train.1.jsonl.xz
|
| 396 |
+
1 1 3’211.3 KiB 25.3 MiB 0.124 CRC64 data/it_legislation_validation.0.jsonl.xz
|
| 397 |
+
1 0 32 B 0 B --- CRC64 data/it_other_validation.0.jsonl.xz
|
| 398 |
+
1 1 476.9 MiB 1’864.5 MiB 0.256 CRC64 data/it_wikipedia_train.0.jsonl.xz
|
| 399 |
+
1 1 476.9 MiB 1’864.8 MiB 0.256 CRC64 data/it_wikipedia_train.1.jsonl.xz
|
| 400 |
+
1 1 184.6 MiB 726.2 MiB 0.254 CRC64 data/it_wikipedia_train.2.jsonl.xz
|
| 401 |
+
1 1 1’334.0 KiB 4’843.5 KiB 0.275 CRC64 data/it_wikipedia_validation.0.jsonl.xz
|
| 402 |
+
1 1 136.6 MiB 975.7 MiB 0.140 CRC64 data/lt_caselaw_train.0.jsonl.xz
|
| 403 |
+
1 1 397.0 KiB 2’660.9 KiB 0.149 CRC64 data/lt_caselaw_validation.0.jsonl.xz
|
| 404 |
+
1 1 24.9 MiB 211.8 MiB 0.118 CRC64 data/lt_contracts_train.0.jsonl.xz
|
| 405 |
+
1 1 3’275.5 KiB 26.1 MiB 0.123 CRC64 data/lt_contracts_validation.0.jsonl.xz
|
| 406 |
+
1 1 274.0 MiB 2’174.1 MiB 0.126 CRC64 data/lt_legislation_train.0.jsonl.xz
|
| 407 |
+
1 1 9’780.7 KiB 73.4 MiB 0.130 CRC64 data/lt_legislation_validation.0.jsonl.xz
|
| 408 |
+
1 0 32 B 0 B --- CRC64 data/lt_other_validation.0.jsonl.xz
|
| 409 |
+
1 1 72.6 MiB 349.5 MiB 0.208 CRC64 data/lt_wikipedia_train.0.jsonl.xz
|
| 410 |
+
1 1 1’251.2 KiB 5’369.5 KiB 0.233 CRC64 data/lt_wikipedia_validation.0.jsonl.xz
|
| 411 |
+
1 1 141.0 MiB 1’106.7 MiB 0.127 CRC64 data/lv_caselaw_train.0.jsonl.xz
|
| 412 |
+
1 1 410.3 KiB 3’004.0 KiB 0.137 CRC64 data/lv_caselaw_validation.0.jsonl.xz
|
| 413 |
+
1 1 24.9 MiB 224.5 MiB 0.111 CRC64 data/lv_contracts_train.0.jsonl.xz
|
| 414 |
+
1 1 3’629.0 KiB 33.6 MiB 0.106 CRC64 data/lv_contracts_validation.0.jsonl.xz
|
| 415 |
+
1 1 271.5 MiB 2’377.4 MiB 0.114 CRC64 data/lv_legislation_train.0.jsonl.xz
|
| 416 |
+
1 1 10.5 MiB 87.5 MiB 0.120 CRC64 data/lv_legislation_validation.0.jsonl.xz
|
| 417 |
+
1 0 32 B 0 B --- CRC64 data/lv_other_validation.0.jsonl.xz
|
| 418 |
+
1 1 47.5 MiB 254.7 MiB 0.186 CRC64 data/lv_wikipedia_train.0.jsonl.xz
|
| 419 |
+
1 1 984.1 KiB 4’559.4 KiB 0.216 CRC64 data/lv_wikipedia_validation.0.jsonl.xz
|
| 420 |
+
1 1 132.2 MiB 956.6 MiB 0.138 CRC64 data/mt_caselaw_train.0.jsonl.xz
|
| 421 |
+
1 1 396.1 KiB 2’680.0 KiB 0.148 CRC64 data/mt_caselaw_validation.0.jsonl.xz
|
| 422 |
+
1 1 25.6 MiB 201.0 MiB 0.127 CRC64 data/mt_contracts_train.0.jsonl.xz
|
| 423 |
+
1 1 4’178.4 KiB 34.0 MiB 0.120 CRC64 data/mt_contracts_validation.0.jsonl.xz
|
| 424 |
+
1 1 270.7 MiB 2’121.7 MiB 0.128 CRC64 data/mt_legislation_train.0.jsonl.xz
|
| 425 |
+
1 1 11.4 MiB 84.2 MiB 0.135 CRC64 data/mt_legislation_validation.0.jsonl.xz
|
| 426 |
+
1 0 32 B 0 B --- CRC64 data/mt_other_validation.0.jsonl.xz
|
| 427 |
+
1 1 4’608.3 KiB 19.5 MiB 0.231 CRC64 data/mt_wikipedia_train.0.jsonl.xz
|
| 428 |
+
1 1 1’405.0 KiB 5’754.4 KiB 0.244 CRC64 data/mt_wikipedia_validation.0.jsonl.xz
|
| 429 |
+
1 1 223.1 MiB 1’338.9 MiB 0.167 CRC64 data/nl_caselaw_train.0.jsonl.xz
|
| 430 |
+
1 1 566.0 KiB 3’152.2 KiB 0.180 CRC64 data/nl_caselaw_validation.0.jsonl.xz
|
| 431 |
+
1 1 31.6 MiB 242.3 MiB 0.130 CRC64 data/nl_contracts_train.0.jsonl.xz
|
| 432 |
+
1 1 2’663.9 KiB 22.4 MiB 0.116 CRC64 data/nl_contracts_validation.0.jsonl.xz
|
| 433 |
+
1 1 476.9 MiB 3’311.9 MiB 0.144 CRC64 data/nl_legislation_train.0.jsonl.xz
|
| 434 |
+
1 1 41.1 MiB 268.7 MiB 0.153 CRC64 data/nl_legislation_train.1.jsonl.xz
|
| 435 |
+
1 1 3’678.8 KiB 72.9 MiB 0.049 CRC64 data/nl_legislation_validation.0.jsonl.xz
|
| 436 |
+
1 0 32 B 0 B --- CRC64 data/nl_other_validation.0.jsonl.xz
|
| 437 |
+
1 1 476.9 MiB 1’856.9 MiB 0.257 CRC64 data/nl_wikipedia_train.0.jsonl.xz
|
| 438 |
+
1 1 59.9 MiB 236.4 MiB 0.253 CRC64 data/nl_wikipedia_train.1.jsonl.xz
|
| 439 |
+
1 1 979.4 KiB 3’414.8 KiB 0.287 CRC64 data/nl_wikipedia_validation.0.jsonl.xz
|
| 440 |
+
1 1 147.9 MiB 1’034.1 MiB 0.143 CRC64 data/pl_caselaw_train.0.jsonl.xz
|
| 441 |
+
1 1 416.2 KiB 2’737.2 KiB 0.152 CRC64 data/pl_caselaw_validation.0.jsonl.xz
|
| 442 |
+
1 1 24.8 MiB 208.9 MiB 0.119 CRC64 data/pl_contracts_train.0.jsonl.xz
|
| 443 |
+
1 1 4’241.9 KiB 34.6 MiB 0.120 CRC64 data/pl_contracts_validation.0.jsonl.xz
|
| 444 |
+
1 1 325.0 MiB 2’646.2 MiB 0.123 CRC64 data/pl_legislation_train.0.jsonl.xz
|
| 445 |
+
1 1 3’593.0 KiB 29.0 MiB 0.121 CRC64 data/pl_legislation_validation.0.jsonl.xz
|
| 446 |
+
1 0 32 B 0 B --- CRC64 data/pl_other_validation.0.jsonl.xz
|
| 447 |
+
1 1 476.9 MiB 2’144.7 MiB 0.222 CRC64 data/pl_wikipedia_train.0.jsonl.xz
|
| 448 |
+
1 1 189.5 MiB 864.0 MiB 0.219 CRC64 data/pl_wikipedia_train.1.jsonl.xz
|
| 449 |
+
1 1 1’233.2 KiB 4’965.9 KiB 0.248 CRC64 data/pl_wikipedia_validation.0.jsonl.xz
|
| 450 |
+
1 1 476.9 MiB 3’494.2 MiB 0.136 CRC64 data/pt_caselaw_train.0.jsonl.xz
|
| 451 |
+
1 1 476.9 MiB 3’392.1 MiB 0.141 CRC64 data/pt_caselaw_train.10.jsonl.xz
|
| 452 |
+
1 1 476.9 MiB 3’505.3 MiB 0.136 CRC64 data/pt_caselaw_train.11.jsonl.xz
|
| 453 |
+
1 1 476.9 MiB 3’524.1 MiB 0.135 CRC64 data/pt_caselaw_train.12.jsonl.xz
|
| 454 |
+
1 1 476.9 MiB 3’458.4 MiB 0.138 CRC64 data/pt_caselaw_train.13.jsonl.xz
|
| 455 |
+
1 1 476.9 MiB 3’602.9 MiB 0.132 CRC64 data/pt_caselaw_train.14.jsonl.xz
|
| 456 |
+
1 1 476.9 MiB 4’923.4 MiB 0.097 CRC64 data/pt_caselaw_train.15.jsonl.xz
|
| 457 |
+
1 1 476.9 MiB 6’648.8 MiB 0.072 CRC64 data/pt_caselaw_train.16.jsonl.xz
|
| 458 |
+
1 1 476.9 MiB 7’461.0 MiB 0.064 CRC64 data/pt_caselaw_train.17.jsonl.xz
|
| 459 |
+
1 1 476.9 MiB 6’866.4 MiB 0.069 CRC64 data/pt_caselaw_train.18.jsonl.xz
|
| 460 |
+
1 1 476.9 MiB 3’455.7 MiB 0.138 CRC64 data/pt_caselaw_train.19.jsonl.xz
|
| 461 |
+
1 1 476.9 MiB 3’513.7 MiB 0.136 CRC64 data/pt_caselaw_train.1.jsonl.xz
|
| 462 |
+
1 1 476.9 MiB 3’477.3 MiB 0.137 CRC64 data/pt_caselaw_train.20.jsonl.xz
|
| 463 |
+
1 1 476.9 MiB 3’492.8 MiB 0.137 CRC64 data/pt_caselaw_train.21.jsonl.xz
|
| 464 |
+
1 1 476.9 MiB 3’528.6 MiB 0.135 CRC64 data/pt_caselaw_train.22.jsonl.xz
|
| 465 |
+
1 1 94.1 MiB 694.3 MiB 0.135 CRC64 data/pt_caselaw_train.23.jsonl.xz
|
| 466 |
+
1 1 476.9 MiB 3’436.5 MiB 0.139 CRC64 data/pt_caselaw_train.2.jsonl.xz
|
| 467 |
+
1 1 476.9 MiB 3’527.9 MiB 0.135 CRC64 data/pt_caselaw_train.3.jsonl.xz
|
| 468 |
+
1 1 476.9 MiB 3’492.2 MiB 0.137 CRC64 data/pt_caselaw_train.4.jsonl.xz
|
| 469 |
+
1 1 476.9 MiB 3’554.8 MiB 0.134 CRC64 data/pt_caselaw_train.5.jsonl.xz
|
| 470 |
+
1 1 476.9 MiB 3’494.7 MiB 0.136 CRC64 data/pt_caselaw_train.6.jsonl.xz
|
| 471 |
+
1 1 476.9 MiB 3’439.1 MiB 0.139 CRC64 data/pt_caselaw_train.7.jsonl.xz
|
| 472 |
+
1 1 476.9 MiB 3’625.6 MiB 0.132 CRC64 data/pt_caselaw_train.8.jsonl.xz
|
| 473 |
+
1 1 476.9 MiB 3’726.4 MiB 0.128 CRC64 data/pt_caselaw_train.9.jsonl.xz
|
| 474 |
+
1 1 798.9 KiB 4’820.6 KiB 0.166 CRC64 data/pt_caselaw_validation.0.jsonl.xz
|
| 475 |
+
1 1 28.4 MiB 243.2 MiB 0.117 CRC64 data/pt_contracts_train.0.jsonl.xz
|
| 476 |
+
1 1 3’899.7 KiB 32.6 MiB 0.117 CRC64 data/pt_contracts_validation.0.jsonl.xz
|
| 477 |
+
1 1 406.2 MiB 3’217.5 MiB 0.126 CRC64 data/pt_legislation_train.0.jsonl.xz
|
| 478 |
+
1 1 8’350.4 KiB 58.4 MiB 0.140 CRC64 data/pt_legislation_validation.0.jsonl.xz
|
| 479 |
+
1 0 32 B 0 B --- CRC64 data/pt_other_validation.0.jsonl.xz
|
| 480 |
+
1 1 476.9 MiB 2’050.4 MiB 0.233 CRC64 data/pt_wikipedia_train.0.jsonl.xz
|
| 481 |
+
1 1 140.6 MiB 617.4 MiB 0.228 CRC64 data/pt_wikipedia_train.1.jsonl.xz
|
| 482 |
+
1 1 1’480.0 KiB 6’344.8 KiB 0.233 CRC64 data/pt_wikipedia_validation.0.jsonl.xz
|
| 483 |
+
1 1 124.9 MiB 956.9 MiB 0.131 CRC64 data/ro_caselaw_train.0.jsonl.xz
|
| 484 |
+
1 1 400.4 KiB 2’785.0 KiB 0.144 CRC64 data/ro_caselaw_validation.0.jsonl.xz
|
| 485 |
+
1 1 24.6 MiB 210.5 MiB 0.117 CRC64 data/ro_contracts_train.0.jsonl.xz
|
| 486 |
+
1 1 3’886.3 KiB 34.3 MiB 0.111 CRC64 data/ro_contracts_validation.0.jsonl.xz
|
| 487 |
+
1 1 476.9 MiB 4’496.4 MiB 0.106 CRC64 data/ro_legislation_train.0.jsonl.xz
|
| 488 |
+
1 1 97.6 MiB 1’053.6 MiB 0.093 CRC64 data/ro_legislation_train.1.jsonl.xz
|
| 489 |
+
1 1 3’691.3 KiB 33.4 MiB 0.108 CRC64 data/ro_legislation_validation.0.jsonl.xz
|
| 490 |
+
1 0 32 B 0 B --- CRC64 data/ro_other_validation.0.jsonl.xz
|
| 491 |
+
1 1 179.7 MiB 833.0 MiB 0.216 CRC64 data/ro_wikipedia_train.0.jsonl.xz
|
| 492 |
+
1 1 2’089.4 KiB 9’053.5 KiB 0.231 CRC64 data/ro_wikipedia_validation.0.jsonl.xz
|
| 493 |
+
1 1 143.6 MiB 1’094.2 MiB 0.131 CRC64 data/sk_caselaw_train.0.jsonl.xz
|
| 494 |
+
1 1 415.8 KiB 3’012.4 KiB 0.138 CRC64 data/sk_caselaw_validation.0.jsonl.xz
|
| 495 |
+
1 1 25.9 MiB 226.7 MiB 0.114 CRC64 data/sk_contracts_train.0.jsonl.xz
|
| 496 |
+
1 1 3’933.6 KiB 35.2 MiB 0.109 CRC64 data/sk_contracts_validation.0.jsonl.xz
|
| 497 |
+
1 1 322.4 MiB 2’745.5 MiB 0.117 CRC64 data/sk_legislation_train.0.jsonl.xz
|
| 498 |
+
1 1 3’735.8 KiB 31.7 MiB 0.115 CRC64 data/sk_legislation_validation.0.jsonl.xz
|
| 499 |
+
1 0 32 B 0 B --- CRC64 data/sk_other_validation.0.jsonl.xz
|
| 500 |
+
1 1 91.2 MiB 435.3 MiB 0.210 CRC64 data/sk_wikipedia_train.0.jsonl.xz
|
| 501 |
+
1 1 1’724.4 KiB 7’568.3 KiB 0.228 CRC64 data/sk_wikipedia_validation.0.jsonl.xz
|
| 502 |
+
1 1 131.9 MiB 815.8 MiB 0.162 CRC64 data/sl_caselaw_train.0.jsonl.xz
|
| 503 |
+
1 1 392.8 KiB 2’328.2 KiB 0.169 CRC64 data/sl_caselaw_validation.0.jsonl.xz
|
| 504 |
+
1 1 22.9 MiB 172.4 MiB 0.133 CRC64 data/sl_contracts_train.0.jsonl.xz
|
| 505 |
+
1 1 3’493.7 KiB 27.2 MiB 0.125 CRC64 data/sl_contracts_validation.0.jsonl.xz
|
| 506 |
+
1 1 388.1 MiB 2’732.3 MiB 0.142 CRC64 data/sl_legislation_train.0.jsonl.xz
|
| 507 |
+
1 1 3’429.8 KiB 24.3 MiB 0.138 CRC64 data/sl_legislation_validation.0.jsonl.xz
|
| 508 |
+
1 0 32 B 0 B --- CRC64 data/sl_other_validation.0.jsonl.xz
|
| 509 |
+
1 1 104.6 MiB 425.6 MiB 0.246 CRC64 data/sl_wikipedia_train.0.jsonl.xz
|
| 510 |
+
1 1 1’392.8 KiB 5’004.9 KiB 0.278 CRC64 data/sl_wikipedia_validation.0.jsonl.xz
|
| 511 |
+
1 1 189.5 MiB 1’325.4 MiB 0.143 CRC64 data/sv_caselaw_train.0.jsonl.xz
|
| 512 |
+
1 1 581.2 KiB 3’566.7 KiB 0.163 CRC64 data/sv_caselaw_validation.0.jsonl.xz
|
| 513 |
+
1 1 25.3 MiB 211.7 MiB 0.119 CRC64 data/sv_contracts_train.0.jsonl.xz
|
| 514 |
+
1 1 2’890.6 KiB 26.0 MiB 0.108 CRC64 data/sv_contracts_validation.0.jsonl.xz
|
| 515 |
+
1 1 324.5 MiB 2’570.4 MiB 0.126 CRC64 data/sv_legislation_train.0.jsonl.xz
|
| 516 |
+
1 1 6’984.8 KiB 50.1 MiB 0.136 CRC64 data/sv_legislation_validation.0.jsonl.xz
|
| 517 |
+
1 0 32 B 0 B --- CRC64 data/sv_other_validation.0.jsonl.xz
|
| 518 |
+
1 1 333.4 MiB 1’668.1 MiB 0.200 CRC64 data/sv_wikipedia_train.0.jsonl.xz
|
| 519 |
+
1 1 1’088.6 KiB 4’372.9 KiB 0.249 CRC64 data/sv_wikipedia_validation.0.jsonl.xz
|
| 520 |
+
-------------------------------------------------------------------------------
|
| 521 |
+
374 351 90.1 GiB 579.9 GiB 0.155 CRC64 374 files
|
| 522 |
+
```
|
| 523 |
+
|
| 524 |
+
## Dataset Creation
|
| 525 |
+
|
| 526 |
+
This dataset has been created by combining the following datasets:
|
| 527 |
+
Native Multi Legal Pile, Eurlex Resources, MC4 Legal, Pile of Law, EU Wikipedias.
|
| 528 |
+
It has been filtered to remove short documents (less than 64 whitespace-separated tokens) and
|
| 529 |
+
documents with more than 30% punctuation or numbers (see prepare_legal_data.py for more details).
|
| 530 |
+
|
| 531 |
+
### Curation Rationale
|
| 532 |
+
|
| 533 |
+
[More Information Needed]
|
| 534 |
+
|
| 535 |
+
### Source Data
|
| 536 |
+
|
| 537 |
+
#### Initial Data Collection and Normalization
|
| 538 |
+
|
| 539 |
+
[More Information Needed]
|
| 540 |
+
|
| 541 |
+
#### Who are the source language producers?
|
| 542 |
+
|
| 543 |
+
[More Information Needed]
|
| 544 |
+
|
| 545 |
+
|
| 546 |
+
### Annotations
|
| 547 |
+
|
| 548 |
+
#### Annotation process
|
| 549 |
+
|
| 550 |
+
[More Information Needed]
|
| 551 |
+
|
| 552 |
+
#### Who are the annotators?
|
| 553 |
+
|
| 554 |
+
[More Information Needed]
|
| 555 |
+
|
| 556 |
+
### Personal and Sensitive Information
|
| 557 |
+
|
| 558 |
+
[More Information Needed]
|
| 559 |
+
|
| 560 |
+
## Considerations for Using the Data
|
| 561 |
+
|
| 562 |
+
### Social Impact of Dataset
|
| 563 |
+
|
| 564 |
+
[More Information Needed]
|
| 565 |
+
|
| 566 |
+
### Discussion of Biases
|
| 567 |
+
|
| 568 |
+
[More Information Needed]
|
| 569 |
+
|
| 570 |
+
### Other Known Limitations
|
| 571 |
+
|
| 572 |
+
[More Information Needed]
|
| 573 |
+
|
| 574 |
+
## Additional Information
|
| 575 |
+
|
| 576 |
+
### Dataset Curators
|
| 577 |
+
|
| 578 |
+
[More Information Needed]
|
| 579 |
+
|
| 580 |
+
### Licensing Information
|
| 581 |
+
|
| 582 |
+
[More Information Needed]
|
| 583 |
+
|
| 584 |
+
### Citation Information
|
| 585 |
+
|
| 586 |
+
```
|
| 587 |
+
TODO add citation
|
| 588 |
+
```
|
| 589 |
+
|
| 590 |
+
### Contributions
|
| 591 |
+
|
| 592 |
+
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
prepare_legal_data.py
ADDED
|
@@ -0,0 +1,195 @@
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|
| 1 |
+
# No chunks, one doc per line
|
| 2 |
+
|
| 3 |
+
# remove new lines, etc.
|
| 4 |
+
# create a corpus of min 200-400 GB ==> ~100B tokens
|
| 5 |
+
# max file size: 4GB because of huggingface
|
| 6 |
+
# validation set: ~100M tokens ==> 200-400MB
|
| 7 |
+
|
| 8 |
+
import json
|
| 9 |
+
import logging
|
| 10 |
+
import multiprocessing
|
| 11 |
+
import sys
|
| 12 |
+
|
| 13 |
+
import tqdm
|
| 14 |
+
import os
|
| 15 |
+
import re
|
| 16 |
+
from multiprocessing import Pool
|
| 17 |
+
|
| 18 |
+
from datasets import load_dataset
|
| 19 |
+
from tokenizers import normalizers
|
| 20 |
+
|
| 21 |
+
try:
|
| 22 |
+
import lzma as xz
|
| 23 |
+
except ImportError:
|
| 24 |
+
import pylzma as xz
|
| 25 |
+
|
| 26 |
+
root = logging.getLogger()
|
| 27 |
+
root.setLevel(logging.INFO)
|
| 28 |
+
|
| 29 |
+
handler = logging.StreamHandler(sys.stdout)
|
| 30 |
+
handler.setLevel(logging.INFO)
|
| 31 |
+
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
|
| 32 |
+
handler.setFormatter(formatter)
|
| 33 |
+
root.addHandler(handler)
|
| 34 |
+
logger = logging.getLogger(__name__)
|
| 35 |
+
|
| 36 |
+
_LANGUAGES = ['bg', 'cs', 'da', 'de', 'el', 'en', 'es', 'et', 'fi', 'fr', 'ga', 'hr',
|
| 37 |
+
'hu', 'it', 'lt', 'lv', 'mt', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl', 'sv']
|
| 38 |
+
_DOMAIN_TYPES = ['legislation', 'caselaw', 'contracts', 'other', 'mc4-legal' 'wikipedia']
|
| 39 |
+
|
| 40 |
+
custom_normalizer = normalizers.NFKD()
|
| 41 |
+
|
| 42 |
+
VALIDATION_SIZE = 1_000 # ~1MB per configuration ==> some low-resource configs will only have a validation file
|
| 43 |
+
MAX_FILE_SIZE = int(5e8) # 500 MB per train file
|
| 44 |
+
|
| 45 |
+
data_dir = 'data'
|
| 46 |
+
os.makedirs(data_dir, exist_ok=True)
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def preprocess_dataset(languages=None, domain_types=None):
|
| 50 |
+
lang_type_datasets = []
|
| 51 |
+
# set defaults if they are not set
|
| 52 |
+
if languages is None:
|
| 53 |
+
languages = _LANGUAGES
|
| 54 |
+
if domain_types is None:
|
| 55 |
+
domain_types = _DOMAIN_TYPES
|
| 56 |
+
|
| 57 |
+
for LANG in languages:
|
| 58 |
+
for DOMAIN_TYPE in domain_types:
|
| 59 |
+
try:
|
| 60 |
+
if DOMAIN_TYPE == 'wikipedia':
|
| 61 |
+
# get from EU_Wikipedias
|
| 62 |
+
dataset = load_dataset("joelito/EU_Wikipedias", date="20221120", language=LANG,
|
| 63 |
+
split='train', streaming=True, use_auth_token=True)
|
| 64 |
+
else:
|
| 65 |
+
# get from Multi_Legal_Pile
|
| 66 |
+
dataset = load_dataset("joelito/Multi_Legal_Pile", f'{LANG}_{DOMAIN_TYPE}',
|
| 67 |
+
split='train', streaming=True, use_auth_token=True)
|
| 68 |
+
dataset = dataset.shuffle(seed=42, buffer_size=10_000)
|
| 69 |
+
logger.info(f'Found data for `{DOMAIN_TYPE}` in language `{LANG}`.')
|
| 70 |
+
except:
|
| 71 |
+
logger.info(f'There is no data for `{DOMAIN_TYPE}` in language `{LANG}`.')
|
| 72 |
+
continue
|
| 73 |
+
lang_type_datasets.append(dataset)
|
| 74 |
+
return lang_type_datasets
|
| 75 |
+
|
| 76 |
+
|
| 77 |
+
def write_samples(dataset_number):
|
| 78 |
+
dataset, dataset_name = dataset_number
|
| 79 |
+
if len(dataset_name.split('_')) == 1: # wikipedia
|
| 80 |
+
language = dataset_name.split('.')[1]
|
| 81 |
+
domain_type = "wikipedia"
|
| 82 |
+
dataset_name = f"{language}_{domain_type}" # reformat the config name so that we have wikipedia in the name
|
| 83 |
+
else:
|
| 84 |
+
language, domain_type = dataset_name.split('_')
|
| 85 |
+
total_count, temp_count, all_samples, file_number = 0, 0, 0, 0
|
| 86 |
+
filepath = get_filepath(dataset_name, 'validation', file_number) # we save the first examples to the validation set
|
| 87 |
+
out_file = open_file(filepath)
|
| 88 |
+
logger.info(f'Processing for dataset {dataset_name} started!')
|
| 89 |
+
# Read each document
|
| 90 |
+
for sample in tqdm.tqdm(dataset):
|
| 91 |
+
try:
|
| 92 |
+
if "validation" in filepath and temp_count >= VALIDATION_SIZE:
|
| 93 |
+
# if we are saving to eval, and we have enough samples in the eval set, switch to train
|
| 94 |
+
logger.info(
|
| 95 |
+
f'Processing validation split in dataset {dataset_name} finished with {temp_count}/{all_samples}!')
|
| 96 |
+
out_file.close()
|
| 97 |
+
temp_count = 0
|
| 98 |
+
filepath = get_filepath(dataset_name, 'train', file_number)
|
| 99 |
+
out_file = open_file(filepath)
|
| 100 |
+
if "train" in filepath and os.path.getsize(filepath) > MAX_FILE_SIZE:
|
| 101 |
+
# if we are saving to train, and we reached the max size per file, switch to the next file
|
| 102 |
+
logger.info(
|
| 103 |
+
f'Processing file {file_number} of train split in dataset {dataset_name} finished with {temp_count}/{all_samples}!')
|
| 104 |
+
out_file.close()
|
| 105 |
+
file_number += 1
|
| 106 |
+
temp_count = 0
|
| 107 |
+
filepath = get_filepath(dataset_name, 'train', file_number)
|
| 108 |
+
out_file = open_file(filepath)
|
| 109 |
+
|
| 110 |
+
text = normalize_text(sample['text'])
|
| 111 |
+
# if the text is usable for pretraining, save it
|
| 112 |
+
if is_text_usable(text):
|
| 113 |
+
jurisdiction = sample.get('jurisdiction', "N/A") # set defaults for wikipedia
|
| 114 |
+
type = sample.get("type", "wikipedia") # set defaults for wikipedia
|
| 115 |
+
entry = {"language": sample["language"], "type": type, "jurisdiction": jurisdiction, "text": text}
|
| 116 |
+
out_file.write(json.dumps(entry) + '\n')
|
| 117 |
+
total_count += 1
|
| 118 |
+
temp_count += 1
|
| 119 |
+
all_samples += 1
|
| 120 |
+
except:
|
| 121 |
+
continue
|
| 122 |
+
|
| 123 |
+
try:
|
| 124 |
+
out_file.close()
|
| 125 |
+
except:
|
| 126 |
+
pass
|
| 127 |
+
|
| 128 |
+
logger.info(f'Processing for dataset {dataset_name} finished with {total_count}/{all_samples}!')
|
| 129 |
+
return
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
def is_text_usable(text):
|
| 133 |
+
# Compute percentage of alphabetical characters in relation to full sequence length
|
| 134 |
+
punctuation = '!\"#$%&\'()*+,\-\./:;<=>?@\[\\\]\^_`{\|}~'
|
| 135 |
+
alpha_text = re.sub(rf'[{punctuation}\d]', '', text) # remove numbers and punctuation
|
| 136 |
+
alpha_percent = len(alpha_text) / len(text)
|
| 137 |
+
# Compute total chunk length
|
| 138 |
+
text_length = len(text.split())
|
| 139 |
+
# Ignore sequences with more than 30% numbers or short sequences (less than 64 tokens)
|
| 140 |
+
return alpha_percent > 0.7 and text_length > 64
|
| 141 |
+
|
| 142 |
+
|
| 143 |
+
def normalize_text(text):
|
| 144 |
+
# Normalize the document
|
| 145 |
+
text = custom_normalizer.normalize_str(text)
|
| 146 |
+
# Replace multiple newline and whitespaces
|
| 147 |
+
return re.sub(r'(\n )+', r'\n ', re.sub(r'( *[\n\r]+ *)+', r'\n ', re.sub(r'[\t ]+', r' ', text)))
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
def open_file(filepath):
|
| 151 |
+
logger.info(f'Writing to file {filepath}')
|
| 152 |
+
return xz.open(filepath, 'wt')
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
def get_filepath(dataset_name, split, file_number):
|
| 156 |
+
return os.path.join(data_dir, f'{dataset_name}_{split}.{file_number}.jsonl.xz')
|
| 157 |
+
|
| 158 |
+
|
| 159 |
+
def clean_and_filter_documents(languages=None, domain_types=None):
|
| 160 |
+
# Load all datasets across languages and types
|
| 161 |
+
lang_type_datasets = preprocess_dataset(languages=languages, domain_types=domain_types)
|
| 162 |
+
# also pass in dataset_name
|
| 163 |
+
lang_type_datasets = [(dataset, dataset.config_name) for dataset in lang_type_datasets]
|
| 164 |
+
logger.info(lang_type_datasets)
|
| 165 |
+
|
| 166 |
+
# Launch pool to preprocess datasets in parallel
|
| 167 |
+
max_num_processes = min(multiprocessing.cpu_count() - 4, len(lang_type_datasets))
|
| 168 |
+
num_processes = max(max_num_processes, 1)
|
| 169 |
+
logger.info(f'Launching a Pool with maximum {num_processes} processes...')
|
| 170 |
+
with Pool(num_processes) as pool:
|
| 171 |
+
pool.map(write_samples, lang_type_datasets)
|
| 172 |
+
|
| 173 |
+
logger.info(f"Finished preparing legal data")
|
| 174 |
+
|
| 175 |
+
|
| 176 |
+
if __name__ == '__main__':
|
| 177 |
+
# CURRENTLY RUNNING ON DGX STATION BFH
|
| 178 |
+
"""
|
| 179 |
+
Run with
|
| 180 |
+
export PYTHONPATH=. && python prepare_legal_data.py | tee prepare_legal_data.log
|
| 181 |
+
"""
|
| 182 |
+
# clean_and_filter_documents(["mt"], ["caselaw"]) # for testing
|
| 183 |
+
domains = ['legislation', 'caselaw', 'contracts', 'other', 'wikipedia'] # 'mc4-legal' is not ready yet
|
| 184 |
+
clean_and_filter_documents(languages=None, domain_types=domains)
|
| 185 |
+
|
| 186 |
+
# Get locally
|
| 187 |
+
# def get_file(LANG, DOMAIN_TYPE, split, number):
|
| 188 |
+
# base_folder = "data/mlm_dataset/chunks_512"
|
| 189 |
+
# return f'{base_folder}/{LANG}_{DOMAIN_TYPE}_{split}_{number}.jsonl.xz'
|
| 190 |
+
|
| 191 |
+
# files = [get_file(LANG, DOMAIN_TYPE, 'train', i) for i in range(1, 5)]
|
| 192 |
+
# files = [f for f in files if os.path.exists(f)] # make sure the file actually exists
|
| 193 |
+
# dataset = load_dataset("json", data_files={'train': files}, split='train', streaming=True)
|
| 194 |
+
|
| 195 |
+
# TODO write dataset cards for chunked, eu wikipedia and filtered dataset
|