# 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 pandas as pd
import datasets
# 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=['1']#, '1', '2', '3', '4', '5', '6', '7', '8', '9', 'C', 'E']
AVAILABLE_FEATURES={
'1': datasets.Features({
'celex_id': datasets.Value("string"),
'text_html_raw': datasets.Value("string"),
'text_html_cleaned': datasets.Value("string"),
'text_cleaned': datasets.Value("string"),
'form': datasets.Sequence(datasets.Value("string")),
'subject_matter': datasets.Sequence(datasets.Value("string")),
'current_consolidated_version': datasets.Sequence(datasets.Value("string")),
'harmonisation_of_customs_law_community_transit': datasets.Sequence(datasets.Value("string")),
'harmonisation_of_customs_law_customs_territory': datasets.Sequence(datasets.Value("string")),
'harmonisation_of_customs_law_value_for_customs_purposes': datasets.Sequence(datasets.Value("string")),
'directory_code': datasets.Sequence(datasets.Value("string")),
'eurovoc': datasets.Sequence(datasets.Value("string")),
'customs_duties_community_tariff_quotas': datasets.Sequence(datasets.Value("string")),
'customs_duties_authorisation_to_defer_application_of_cct': datasets.Sequence(datasets.Value("string")),
'harmonisation_of_customs_law_various': datasets.Sequence(datasets.Value("string")),
'customs_duties_suspensions': datasets.Sequence(datasets.Value("string"))})
}
SECTOR_DESCRIPTIONS={
'1':""
}
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]
data_dir = dl_manager.download_and_extract(urls)
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.
print(text)
print(meta)
print(sector)
print(split)
print(sector)
print("Reading Text Data...")
text_data = pd.read_parquet(text)#, lines=True)
text_data['celex_id'] = text_data['celex_id'].apply(lambda x: x[0] if isinstance(x,list) else x)
print("Reading Meta Data...")
meta_data = pd.read_parquet(meta)#, lines=True)
meta_data['celex_id'] = meta_data['celex_id'].apply(lambda x: x[0] if isinstance(x, list) else x)
print("Combining Text & Meta Data...")
combined_data = pd.merge(text_data, meta_data, on='celex_id')
print("Converting To final dataset...")
dataset = datasets.Dataset.from_pandas(combined_data)
dataset = dataset.remove_columns('__index_level_0__')#.cache_files()
for i, sample in enumerate(dataset):
yield i, sample
print("Hello World")
if __name__ == '__main__':
import datasets as ds
import sys
print(sys.argv[0])
dataset = ds.load_dataset(sys.argv[0],'1.DE')
print(dataset)
for sample in dataset['train']:
continue
#print(sample)