Update Readme

README.md

@@ -1,3 +1,490 @@
 ---
-license: mit
+annotations_creators:
+- found
+language:
+- bg
+- cs
+- da
+- de
+- el
+- en
+- es
+- et
+- fi
+- fr
+- ga
+- hr
+- hu
+- it
+- lt
+- lv
+- mt
+- nl
+- pl
+- pt
+- ro
+- sk
+- sl
+- sv
+language_creators:
+- found
+license:
+- mit
+multilinguality:
+- multilingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- original
+tags:
+- legal documents
+- corpus
+- eurlex
+- html
+task_categories:
+- text-classification
+- fill-mask
+task_ids:
+- multi-class-classification
+- multi-label-classification
+pretty_name: 'SuperEURLEX: A Corpus of Plain Text and HTML from EURLEX, Annotated for multiple Legal Domain Text Classification Tasks.'
 ---
+
+# Dataset Card for Dataset Name
+
+This dataset contains over 4.6M Legal Documents from EURLEX with Annotations.
+Over 3.7M of this 4.6M documents are also available in HTML format.
+This dataset can be used for pretraining language models as well as for testing them on legal text classification tasks.
+
+Use this dataset as follows:
+
+```python
+from datasets import load_dataset
+config = "0.DE" # {sector}.{lang}[.html]
+dataset = load_dataset("ddrg/super_eurlex", config, split='train')
+```
+
+## Dataset Details
+
+### Dataset Description
+
+This Dataset was scrapped from [EURLEX](https://eur-lex.europa.eu/homepage.html).
+It contains more than 4.6M Legal Documents in Plain Text and over 3.7M In HTML Format.
+Those Documents are separated by their language (This Dataset includes a total of 24 official European Languages) 
+and by their Sector.
+
+
+#### The Table below shows the number of documents per language:
+
+|    |     Raw |    HTML |
+|---:|--------:|--------:|
+| BG |  29,778 |  27,718 |
+| CS |  94,439 |  91,754 |
+| DA | 398,559 | 300,488 |
+| DE | 384,179 | 265,724 |
+| EL | 167,502 | 117,009 |
+| EN | 456,212 | 354,186 |
+| ES | 253,821 | 201,400 |
+| ET | 142,183 | 139,690 |
+| FI | 238,143 | 214,206 |
+| FR | 427,011 | 305,592 |
+| GA |  19,673 |  19,437 |
+| HR |  37,200 |  35,944 |
+| HU |  69,275 |  66,334 |
+| IT | 358,637 | 259,936 |
+| LT |  62,975 |  61,139 |
+| LV | 105,433 | 102,105 |
+| MT |  46,695 |  43,969 |
+| NL | 345,276 | 237,366 |
+| PL | 146,502 | 143,490 |
+| PT | 369,571 | 314,148 |
+| RO |  47,398 |  45,317 |
+| SK | 100,718 |  98,192 |
+| SL | 170,583 | 166,646 |
+| SV | 172,926 | 148,656 |
+
+
+- **Curated by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+
+### Dataset Sources [optional]
+
+- **Repository:** https://huggingface.co/datasets/ddrg/super_eurlex/tree/main
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+### As Corpus for:
+- **Pretraining of Language Models with self supervised tasks** like Masked Language Modeling and Next Sentence Prediction
+- Legal Text Analysis
+
+### As Dataset for evaluation on the following task:
+- *eurovoc*-Concepts Prediction i.e. which tags apply? (Muli-Label Classification (large Scale))
+  - Example for this task is given[below
+- *subject-matter* Prediction i.e. which other tags apply (Multi-Label Classification)
+- *form* Classification i.e. What Kind of Document is it? (Multi-Class)
+- And more
+
+### Example for Use Of EUROVOC-Concepts
+
+```python
+from datasets import load_dataset
+import transformers as tr
+from sklearn.preprocessing import MultiLabelBinarizer
+import numpy as np 
+import evaluate
+import uuid
+
+# ==================== #
+#     Prepare Data     #
+# ==================== #
+CONFIG = "3.EN" # {sector}.{lang}[.html]
+MODEL_NAME = "distilroberta-base"
+dataset = load_dataset("ddrg/super_eurlex", CONFIG, split='train')
+tokenizer = tr.AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# Remove Unlabeled Columns
+def remove_nulls(batch):
+  return [(sample != None) for sample in batch["eurovoc"]]
+dataset = dataset.filter(remove_nulls, batched=True, keep_in_memory=True)
+
+# Tokenize Text
+def tokenize(batch):
+  return tokenizer(batch["text_cleaned"], truncation=True, padding="max_length")
+# Keep in Memory is optional (The Dataset is large though and can easily use up alot of memory)
+dataset = dataset.map(tokenize, batched=True, keep_in_memory=True)
+
+# Create Label Column by encoding Eurovoc Concepts 
+encoder = MultiLabelBinarizer()
+# List of all Possible Labels 
+eurovoc_concepts = dataset["eurovoc"]
+encoder.fit(eurovoc_concepts)
+def encode_labels(batch):
+    batch["label"] = encoder.transform(batch["eurovoc"])
+    return batch
+dataset = dataset.map(encode_labels, batched=True, keep_in_memory=True)
+
+# Split into train and Test set
+dataset = dataset.train_test_split(0.2)
+
+# ==================== #
+#  Load & Train Model  #
+# ==================== #
+model = tr.AutoModelForSequenceClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=len(encoder.classes_),
+    problem_type="multi_label_classification",
+)
+
+metric = evaluate.load("JP-SystemsX/nDCG", experiment_id=uuid.uuid4())
+def compute_metric(eval_pred):
+    predictions, labels = eval_pred
+    return metric.compute(predictions=predictions, references=labels, k=5)
+
+# Set Hyperparameter 
+# Note: We stay mostly with default values to keep example short
+# Though more hyperparameter should be set and tuned in praxis
+train_args = tr.TrainingArguments(
+    output_dir="./cache",
+    per_device_train_batch_size=16,
+    num_train_epochs=20
+)
+trainer = tr.Trainer(
+    model=model,
+    args=train_args,
+    train_dataset=dataset["train"],
+    compute_metrics=compute_metric,
+)
+trainer.train() # This will take a while
+print(trainer.evaluate(dataset["test"]))
+# >>> {'eval_loss': 0.0018887673504650593, 'eval_nDCG@5': 0.8072531683578489, 'eval_runtime': 663.8582, 'eval_samples_per_second': 32.373, 'eval_steps_per_second': 4.048, 'epoch': 20.0}
+```
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+[More Information Needed]
+
+## Dataset Structure
+
+This dataset is divided into multiple split by _Sector x Language x Format_
+
+Sector refers to the kind of Document it belongs to:
+- **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
+
+Language refers to each of the 24 official European Languages that were included at the date of the dataset creation:
+- BG ~ Bulgarian
+- CS ~ Czech
+- DA ~ Danish
+- DE ~ German
+- EL ~ Greek
+- EN ~ English
+- ES ~ Spanish
+- ET ~ Estonian
+- FI ~ Finnish
+- FR ~ French
+- GA ~ Irish
+- HR ~ Croatian
+- HU ~ Hungarian
+- IT ~ Italian
+- LT ~ Lithuanian
+- LV ~ Latvian
+- MT ~ Maltese
+- NL ~ Dutch
+- PL ~ Polish
+- PT ~ Portuguese
+- RO ~ Romanian
+- SK ~ Slovak
+- SL ~ Slovenian
+- SV ~ Swedish
+
+Format refers to plain Text (default) or HTML format (.html)
+> Note: Plain Text contains generally more documents because not all documents were available in HTML format but those that were are included in both formats
+
+Those Splits are named the following way:
+`{sector}.{lang}[.html]` 
+
+For Example:
+- `3.EN` would be English legislative documents in plain text format 
+- `3.EN.html` would be the same in HTML Format 
+
+Each _Sector_ has its own set of meta data:
+
+<details><summary>Sector 0 (Consolidated acts)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+
+</p>
+</details>
+
+<details><summary>Sector 1 (Treaties)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _current_consolidated_version_ ~ date when this version of the document was consolidated `Format DD/MM/YYYY`
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+
+</p>
+</details>
+
+
+<details><summary>Sector 2 (International agreements)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
+- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
+
+</p>
+</details>
+
+
+<details><summary>Sector 3 (Legislation)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
+- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
+
+</p>
+</details>
+
+
+<details><summary>Sector 4 (Complementary legislation)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
+- _current_consolidated_version_ ~ `Format DD/MM/YYYY`
+
+</p>
+</details>
+
+
+<details><summary>Sector 5 (Preparatory acts and working documents)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+- _latest_consolidated_version_ ~ `Format DD/MM/YYYY`
+
+</p>
+</details>
+
+
+<details><summary>Sector 6 (Case-law)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+- _case-law_directory_code_before_lisbon_  ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
+
+</p>
+</details>
+
+
+<details><summary>Sector 7 (National transposition measures)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _transposed_legal_acts_ ~ national laws that exist in EU member states as a direct result of the need to comply with EU directives
+
+</p>
+</details>
+
+
+<details><summary>Sector 8 (References to national case-law concerning EU law)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _case-law_directory_code_before_lisbon_  ~ Classification system used for case law before Treaty of Lisbon came into effect (2009), each code reflects a particular area of EU law
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+
+</p>
+</details>
+
+
+<details><summary>Sector 9 (Parliamentary questions)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+
+</p>
+</details>
+
+
+<details><summary>Sector C (Other documents published in the Official Journal C series)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+
+</p>
+</details>
+
+
+<details><summary>Sector E (EFTA documents)</summary><p>
+
+- _celex_id_ ~ Unique Identifier for each document 
+- _text_cleaned_ (Plain Text) **or** _text_html_raw_ (HTML Format)
+- _form_ ~ Kind of Document e.g. Consolidated text, or Treaty
+- _directory_code_ ~ Information to structure documents in some kind of directory structure by topic e.g. `'03.50.30.00 Agriculture / Approximation of laws and health measures / Animal health and zootechnics'`
+- _subject_matter_ ~ Keywords that provide general overview of content in a document see [here](https://eur-lex.europa.eu/content/e-learning/browsing_options.html) for more information
+- _eurovoc_ ~ Keywords that describe document content based on the European Vocabulary see [here](https://eur-lex.europa.eu/browse/eurovoc.html) for more information
+
+</p>
+</details>
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created for the creation and/or evaluation of pretrained Legal Language Models.
+
+### Source Data
+
+#### Data Collection and Processing
+
+We used the [EURLEX-Web-Scrapper Repo](https://github.com/JP-SystemsX/Eurlex-Web-Scrapper) for the data collection process.
+
+
+#### Who are the source data producers?
+
+The Source data stems from the [EURLEX-Website](https://eur-lex.europa.eu/) and was therefore produced by various entities within the European Union
+
+
+#### Personal and Sensitive Information
+
+No Personal or Sensitive Information is included to the best of our knowledge.
+
+## Bias, Risks, and Limitations
+
+- We removed HTML documents from which we couldn't extract plain text under the assumption that those are **corrupted files**.
+However, we can't guarantee that we removed all. 
+- The Extraction of plain text from legal HTML documents can lead to **formatting issues** 
+e.g. the extraction of text from tables might mix up the order such that it becomes nearly incomprehensible.
+- This dataset might contain many **missing values** in the meta-data columns as not every document was annotated in the same way
+
+[More Information Needed]
+
+### Recommendations
+
+- Consider Removing rows with missing values for the task before training a model on it 
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Dataset Card Authors [optional]
+
+[More Information Needed]
+
+## Dataset Card Contact
+
+[More Information Needed]