Legal Maritime And Inland Waterway Transport 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_maritime_and_inland_waterway_transport_bert model, it is a Bert Sentence Embeddings Document Classifier, classifies if the document belongs to the class Maritime_and_Inland_Waterway_Transport or not (Binary Classification) according to EuroVoc labels.

Predicted Entities

Maritime_and_Inland_Waterway_Transport, 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_maritime_and_inland_waterway_transport_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|
+-------+
|[Maritime_and_Inland_Waterway_Transport]|
|[Other]|
|[Other]|
|[Maritime_and_Inland_Waterway_Transport]|

Model Information

References

Train dataset available here

Benchmarking

                                 label precision recall  f1-score  support
Maritime_and_Inland_Waterway_Transport      0.98   0.91      0.94      190
                                 Other      0.90   0.98      0.94      157
                              accuracy         -      -      0.94      347
                             macro-avg      0.94   0.94      0.94      347
                          weighted-avg      0.94   0.94      0.94      347