Legal English Bert Embeddings (Base, Uncased)

Description

Legal Pretrained Bert Embeddings model, trained with uncased text, uploaded to Hugging Face, adapted and imported into Spark NLP. legal-bert-base-uncased is a English model orginally trained by nlpaueb.

Live Demo Open in Colab Download Copy S3 URI

How to use

documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")

tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler() 
.setInputCol("text") 
.setOutputCol("document")

val tokenizer = new Tokenizer() 
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = BertEmbeddings.pretrained("bert_embeddings_legal_bert_base_uncased","en") 
.setInputCols(Array("document", "token")) 
.setOutputCol("embeddings")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, embeddings))

val data = Seq("I love Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("en.embed.legal_bert_base_uncased").predict("""I love Spark NLP""")

Model Information

References

• https://huggingface.co/nlpaueb/legal-bert-base-uncased
• https://aclanthology.org/2020.findings-emnlp.261/
• https://eur-lex.europa.eu/
• https://www.legislation.gov.uk/
• https://case.law/
• https://www.sec.gov/edgar.shtml
• https://archive.org/details/legal_bert_fp
• http://nlp.cs.aueb.gr/