Legal Iron Steel And Other Metal Industries 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_iron_steel_and_other_metal_industries_bert model, it is a Bert Sentence Embeddings Document Classifier, classifies if the document belongs to the class Iron_Steel_and_Other_Metal_Industries or not (Binary Classification) according to EuroVoc labels.

Predicted Entities

Iron_Steel_and_Other_Metal_Industries, Other

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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_iron_steel_and_other_metal_industries_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|
+-------+
|[Iron_Steel_and_Other_Metal_Industries]|
|[Other]|
|[Other]|
|[Iron_Steel_and_Other_Metal_Industries]|

Model Information

Model Name: legclf_iron_steel_and_other_metal_industries_bert
Compatibility: Legal NLP 1.0.0+
License: Licensed
Edition: Official
Input Labels: [sentence_embeddings]
Output Labels: [class]
Language: en
Size: 21.6 MB

References

Train dataset available here

Benchmarking


                                label precision recall  f1-score  support
Iron_Steel_and_Other_Metal_Industries      0.84   0.91      0.87       99
                                Other      0.90   0.83      0.86       98
                             accuracy         -      -      0.87      197
                            macro-avg      0.87   0.87      0.87      197
                         weighted-avg      0.87   0.87      0.87      197