Description
Given a proposal on a socially important issue, this model classifies it according to its topic.
Predicted Entities
Democracy, Digital, EU_In_The_World, Economy, Education, Green_Deal, Health, Migration, Other
How to use
document_assembler = nlp.DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentence_embeddings = nlp.UniversalSentenceEncoder.pretrained()\
.setInputCols("document")\
.setOutputCol("sentence_embeddings")
classifier = legal.ClassifierDLModel.pretrained("legclf_proposal_topic", "en", "legal/models")\
.setInputCols(["sentence_embeddings"])\
.setOutputCol("class")
clf_pipeline = nlp.Pipeline(stages=[
document_assembler,
sentence_embeddings,
classifier
])
empty_df = spark.createDataFrame([['']]).toDF("text")
model = clf_pipeline.fit(empty_df)
text = ["""In order to involve young people in the European Union, they need to understand the role, importance, and impact of the European Union on their lives and how they can contribute to the EU. I believe that many Europeans do not know the values of Europe, how they can contribute to the EU, etc. To do this, it was necessary to create an education program on the European Union that could cut across all countries, including a discipline on the EU, visits by young people to the European institutions, and a 'channel of communication' between young people and the EU. The same could be done for older people in senior universities."""]
data = spark.createDataFrame([text]).toDF("text")
result = model.transform(data)
Results
+-----------+
| result|
+-----------+
|[Education]|
+-----------+
Model Information
| Model Name: | legclf_proposal_topic |
| Compatibility: | Legal NLP 1.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 22.6 MB |
References
Training dataset available here
Benchmarking
label precision recall f1-score support
Democracy 0.86 0.90 0.88 62
Digital 0.85 0.80 0.82 35
EU_In_The_World 0.78 0.72 0.75 39
Economy 0.82 0.77 0.80 43
Education 0.89 0.87 0.88 46
Green_Deal 0.85 0.92 0.88 49
Health 0.87 0.95 0.91 21
Migration 0.86 0.89 0.87 27
Other 1.00 0.97 0.98 32
accuracy - - 0.86 354
macro-avg 0.86 0.87 0.86 354
weighted-avg 0.86 0.86 0.86 354