# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """TODO: Add a description here.""" import csv import json import os import numpy as np import pandas as pd import datasets from tqdm.auto import tqdm # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """ """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """ """ # 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 = "" # TODO: Add link to the official dataset URLs here # 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 = { "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", } AVAILABLE_LANGUAGES=['DE']#, 'EN' SECTORS=['0', '1', '2', '3', '4', '5', '6', '8', '9', 'C', 'E']#'7', # Features to override the standard most Features were scrapped as a sequence # of strings with only the following exceptions FEATURES = { 'celex_id': datasets.Value("string"), 'text_cleaned': datasets.Value("string"), 'text_html_cleaned': 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'13.30.11.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_agricultural_and_forestry_tractors', 'harmonisation_of_customs_law_warehouses,_free_zones'], 'C':['celex_id', 'text_html_raw', 'text_html_cleaned', 'text_cleaned', 'form', 'eurovoc'], 'E':['celex_id', 'text_html_raw', 'text_html_cleaned', 'text_cleaned', 'form', 'directory_code', 'subject_matter', 'eurovoc', '13.30.14.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_foodstuffs', '13.30.10.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_motor_vehicles', '13.30.18.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_dangerous_substances', '13.30.15.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_proprietary_medicinal_products', '13.20.60.00_industrial_policy_and_internal_market_/_industrial_policy_sectoral_operations_/_information_technology,_telecommunications_and_data-processing', '13.30.99.00_industrial_policy_and_internal_market_/_internal_market_approximation_of_laws_/_other_sectors_for_approximation_of_laws'], } AVAILABLE_FEATURES={sector: datasets.Features({feature:(FEATURES[feature] if feature in FEATURES else datasets.Sequence(datasets.Value("string"))) for feature in FEATURES_IN_SECTOR[sector]}) for sector in SECTORS} SECTOR_DESCRIPTIONS={ '0':"Consolidated acts ", '1':"Treaties", '2':"International agreements", '3':"Legislation", '4':"Complementary legislation", '5':"Preparatory acts and working documents", '6':"Case-law", '7':"National transposition measures", '8':"References to national case-law concerning EU law", '9':"Parliamentary questions", 'C':"Other documents published in the Official Journal C series", 'E':"EFTA documents", } class SuperEurlexConfig(datasets.BuilderConfig): """BuilderConfig for SuperGLUE.""" def __init__(self, sector, language, features, citation, url, **kwargs): """BuilderConfig for SuperGLUE. Args: sector: sector of the wanted data language: the language code for the language in which the text shall be written in features: *list[string]*, list of the features that will appear in the feature dict. citation: *string*, citation for the data set. url: *string*, url for information about the data set. **kwargs: keyword arguments forwarded to super. """ name=sector+'.'+language super().__init__(name=name, version=datasets.Version("0.1.0"), **kwargs) self.features = features self.language = language self.sector = sector self.text_data_url = f"text_data/{language}/{sector}.parquet" self.meta_data_url = f"meta_data/{sector}.parquet" self.citation = citation self.url = url # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case class SuperEurlex(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.1.0") # 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 = [ SuperEurlexConfig(#version=VERSION, sector=sect, language=lang, description=SECTOR_DESCRIPTIONS[sect], features=AVAILABLE_FEATURES[sect], citation=_CITATION, url=_HOMEPAGE) for lang in AVAILABLE_LANGUAGES for sect in SECTORS ] #DEFAULT_CONFIG_NAME = "3.DE" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = AVAILABLE_FEATURES[self.config.sector] info = 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, ) return info def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # 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 = {'text': self.config.text_data_url, 'meta': self.config.meta_data_url} #_URLS[self.config.name] try: data_dir = dl_manager.download_and_extract(urls) except FileNotFoundError: raise Exception("""The demanded Files weren't found. It could be that the demanded sector isn't yet available in your language of choice""") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "text": data_dir['text'], "meta": data_dir['meta'], "language": self.config.language, "sector": self.config.sector, 'split': 'train' }, ) ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, text, meta, sector, language, split): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. text_data = pd.read_parquet(text) text_data['celex_id'] = text_data['celex_id'].apply(lambda x: str(x.tolist()[0]) if isinstance(x,list) else x) meta_data = pd.read_parquet(meta) meta_data['celex_id'] = meta_data['celex_id'].apply(lambda x: str(x.tolist()[0]) if isinstance(x, np.ndarray) else x) combined_data = pd.merge(text_data, meta_data, on='celex_id') dataset = datasets.Dataset.from_pandas(combined_data) dataset = dataset.remove_columns('__index_level_0__') for i, sample in enumerate(dataset): yield i, sample if __name__ == '__main__': import datasets as ds import sys print(sys.argv[0]) for sector in SECTORS: for lang in AVAILABLE_LANGUAGES: print(f'{sector}.{lang}') dataset = ds.load_dataset(sys.argv[0],f'{sector}.{lang}') print(dataset) print('\n')