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  • John Snow Labs
  • May 29, 2020

Embeddings Healthcare 100 dims

  • clinical
  • embeddings
  • en

Description

Word Embeddings lookup annotator that maps tokens to vectors.

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How to use

model = WordEmbeddingsModel.pretrained("embeddings_healthcare_100d","en","clinical/models")\
	.setInputCols(["document","token"])\
	.setOutputCol("word_embeddings")

val model = WordEmbeddingsModel.pretrained("embeddings_healthcare_100d","en","clinical/models")
	.setInputCols("document","token")
	.setOutputCol("word_embeddings")

import nlu
nlu.load("en.embed.glove.healthcare_100d").predict("""Put your text here.""")

Results

Word2Vec feature vectors based on embeddings_healthcare_100d.

Model Information

Name: embeddings_healthcare_100d
Type: WordEmbeddingsModel
Compatibility: Spark NLP 2.5.0+
License: Licensed
Edition: Official
Input labels: [document, token]
Output labels: [word_embeddings]
Language: en
Dimension: 100.0

Data Source

Trained on PubMed + ICD10 + UMLS + MIMIC III corpora https://www.nlm.nih.gov/databases/download/pubmed_medline.html