Update super_eurlex.py

super_eurlex.py

@@ -12,18 +12,12 @@
 # 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
+"""Super-EURLEX dataset containing legal documents from multiple languages"""
 
 import numpy as np
 import pandas as pd
 
 import datasets
-from tqdm.auto import tqdm
 
 
 # TODO: Add BibTeX citation
@@ -32,7 +26,28 @@ _CITATION = """ """
 
 # TODO: Add description of the dataset here
 # You can copy an official description
-_DESCRIPTION = """ """
+_DESCRIPTION = """
+                Super-EURLEX dataset containing legal documents from multiple languages.
+                The datasets are build/scrapped from the EURLEX Website [https://eur-lex.europa.eu/homepage.html]
+                With one split per language and sector, because the available features (metadata) differs for each 
+                sector. Therefore, each sample contains the content of a full legal document in up to 3 different 
+                formats. Those are raw HTML and cleaned HTML (if the HTML format was available on the EURLEX website 
+                during the scrapping process) and cleaned text.
+                The cleaned text should be available for each sample and was extracted from HTML or PDF.
+                'Cleaned' HTML stands here for minor cleaning that was done to preserve to a large extent the necessary 
+                HTML information like table structures while removing unnecessary complexity which was introduced to the 
+                original documents due to actions like writing each sentence into a new object. 
+                Additionally, each sample contains metadata which was scrapped on the fly, this implies the following 
+                2 things. First, not every sector contains the same metadata. Second, most metadata might be 
+                irrelevant for most use cases. 
+                In our minds the most interesting metadata is the celex-id which is used to identify the legal 
+                document at hand, but also contains a lot of information about the document 
+                see [https://eur-lex.europa.eu/content/tools/eur-lex-celex-infographic-A3.pdf] as well as eurovoc-
+                concepts, which are labels that define the content of the documents. 
+                Eurovoc-Concepts are, for example, only available for the sectors 1, 2, 3, 4, 5, 6, 9, C, and E.
+                The Naming of most metadata is kept like it was on the eurlex website, except for converting 
+                it to lower case and replacing whitespaces with '_'.
+                """
 
 # TODO: Add a link to an official homepage for the dataset here
 _HOMEPAGE = ""
@@ -40,13 +55,6 @@ _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',
 
@@ -386,7 +394,7 @@ class SuperEurlex(datasets.GeneratorBasedBuilder):
         # 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]
+                'meta': self.config.meta_data_url}
         try:
             data_dir = dl_manager.download_and_extract(urls)
         except FileNotFoundError: