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  • John Snow Labs
  • Dec 20, 2022

English Legal Word2Vec Embeddings (Lemmatized , NNP)

  • fasttext
  • en
  • english
  • embeddings
  • open_source

Description

Legal Word Embeddings lookup annotator that maps tokens to vectors. Trained on legal text after lemmatization, also replacing the text with NNP.

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How to use

 
model = WordEmbeddingsModel.pretrained("word2vec_osf_replaced_lemmatized_legal","en")\
	            .setInputCols(["document","token"])\
	            .setOutputCol("word_embeddings")



val model = WordEmbeddingsModel.pretrained("word2vec_osf_replaced_lemmatized_legal","en")
	                .setInputCols("document","token")
	                .setOutputCol("word_embeddings")


import nlu
nlu.load("en.embed.legal.osf_replaced_lemmatized_legal").predict("""Put your text here.""")

Model Information

Model Name: word2vec_osf_replaced_lemmatized_legal
Type: embeddings
Compatibility: Spark NLP 4.2.5+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [embeddings]
Language: en
Size: 39.8 MB
Case sensitive: false
Dimension: 100

References

https://osf.io/