Legal Eu Institutions And European Civil Service Document Classifier (EURLEX)

Description

European Union (EU) legislation is published in the EUR-Lex portal. All EU laws are annotated by the EU’s Publications Office with multiple concepts from the EuroVoc thesaurus, a multilingual thesaurus maintained by the Publications Office.

Given a document, the legclf_eu_institutions_and_european_civil_service_bert model, it is a Bert Sentence Embeddings Document Classifier, classifies if the document belongs to the class Eu_Institutions_and_European_Civil_Service or not (Binary Classification) according to EuroVoc labels.

Predicted Entities

Eu_Institutions_and_European_Civil_Service, Other

Live Demo Open in Colab Download Copy S3 URI

How to use

document_assembler = nlp.DocumentAssembler()\
    .setInputCol("text")\
    .setOutputCol("document")

embeddings = nlp.BertSentenceEmbeddings.pretrained("sent_bert_base_cased", "en")\
    .setInputCols("document")\
    .setOutputCol("sentence_embeddings")

doc_classifier = legal.ClassifierDLModel.pretrained("legclf_eu_institutions_and_european_civil_service_bert", "en", "legal/models")\
    .setInputCols(["sentence_embeddings"])\
    .setOutputCol("category")

nlpPipeline = nlp.Pipeline(stages=[
    document_assembler, 
    embeddings,
    doc_classifier])

df = spark.createDataFrame([["YOUR TEXT HERE"]]).toDF("text")

model = nlpPipeline.fit(df)

result = model.transform(df)

Results

+-------+
|result|
+-------+
|[Eu_Institutions_and_European_Civil_Service]|
|[Other]|
|[Other]|
|[Eu_Institutions_and_European_Civil_Service]|

Model Information

References

Train dataset available here

Benchmarking

                                     label precision recall  f1-score  support
Eu_Institutions_and_European_Civil_Service      0.85   1.00      0.92       45
                                     Other      1.00   0.83      0.91       47
                                  accuracy         -      -      0.91       92
                                 macro-avg      0.92   0.91      0.91       92
                              weighted-avg      0.93   0.91      0.91       92