Legal Electronics And Electrical Engineering 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_electronics_and_electrical_engineering_bert model, it is a Bert Sentence Embeddings Document Classifier, classifies if the document belongs to the class Electronics_and_Electrical_Engineering or not (Binary Classification) according to EuroVoc labels.

Predicted Entities

Electronics_and_Electrical_Engineering, 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_electronics_and_electrical_engineering_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|
+-------+
|[Electronics_and_Electrical_Engineering]|
|[Other]|
|[Other]|
|[Electronics_and_Electrical_Engineering]|

Model Information

References

Train dataset available here

Benchmarking

                                 label precision recall  f1-score  support
Electronics_and_Electrical_Engineering      0.86   0.83      0.84       58
                                 Other      0.85   0.88      0.86       64
                              accuracy         -      -      0.85      122
                             macro-avg      0.85   0.85      0.85      122
                          weighted-avg      0.85   0.85      0.85      122