JP-SystemsX
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e7849ae
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Parent(s):
e26c06b
Update super_eurlex.py
Browse files- super_eurlex.py +24 -16
super_eurlex.py
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""
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import csv
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import json
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import os
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import numpy as np
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import pandas as pd
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import datasets
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from tqdm.auto import tqdm
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# TODO: Add BibTeX citation
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@@ -32,7 +26,28 @@ _CITATION = """ """
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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@@ -40,13 +55,6 @@ _HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
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"second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
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}
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AVAILABLE_LANGUAGES=['DE']#, 'EN'
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SECTORS=['0', '1', '2', '3', '4', '5', '6', '8', '9', 'C', 'E']#'7',
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@@ -386,7 +394,7 @@ class SuperEurlex(datasets.GeneratorBasedBuilder):
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = {'text': self.config.text_data_url,
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'meta': self.config.meta_data_url}
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try:
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data_dir = dl_manager.download_and_extract(urls)
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except FileNotFoundError:
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""Super-EURLEX dataset containing legal documents from multiple languages"""
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import numpy as np
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import pandas as pd
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import datasets
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# TODO: Add BibTeX citation
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """
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Super-EURLEX dataset containing legal documents from multiple languages.
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The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html]
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With one split per language and sector, because the available features (metadata) differs for each
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sector. Therefore, each sample contains the content of a full legal document in up to 3 different
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formats. Those are raw HTML and cleaned HTML (if the HTML format was available on the EURLEX website
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during the scrapping process) and cleaned text.
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The cleaned text should be available for each sample and was extracted from HTML or PDF.
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'Cleaned' HTML stands here for minor cleaning that was done to preserve to a large extent the necessary
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HTML information like table structures while removing unnecessary complexity which was introduced to the
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original documents due to actions like writing each sentence into a new object.
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Additionally, each sample contains metadata which was scrapped on the fly, this implies the following
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2 things. First, not every sector contains the same metadata. Second, most metadata might be
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irrelevant for most use cases.
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In our minds the most interesting metadata is the celex-id which is used to identify the legal
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document at hand, but also contains a lot of information about the document
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see [https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf] as well as eurovoc-
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concepts, which are labels that define the content of the documents.
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Eurovoc-Concepts are, for example, only available for the sectors 1, 2, 3, 4, 5, 6, 9, C, and E.
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The Naming of most metadata is kept like it was on the eurlex website, except for converting
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it to lower case and replacing whitespaces with '_'.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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AVAILABLE_LANGUAGES=['DE']#, 'EN'
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SECTORS=['0', '1', '2', '3', '4', '5', '6', '8', '9', 'C', 'E']#'7',
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = {'text': self.config.text_data_url,
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'meta': self.config.meta_data_url}
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try:
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data_dir = dl_manager.download_and_extract(urls)
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except FileNotFoundError:
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