Sentence Embeddings - sbert medium (tuned)

Description

This model maps sentences & documents to a 768 dimensional dense vector space by using average pooling on top of BioBert model. It’s also fine-tuned on RxNorm dataset to help generalization over medication-related datasets.

Predicted Entities

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How to use

sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_jsl_rxnorm_cased", "en", "clinical/models")\
    .setInputCols(["sentence"])\
    .setOutputCol("sbioert_embeddings")

val sentence_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_jsl_rxnorm_cased", "en","clinical/models")
    .setInputCols("sentence")
    .setOutputCol("sbioert_embeddings")

import nlu
nlu.load("en.embed_sentence.biobert.rxnorm").predict("""Put your text here.""")

Results

Gives a 768-dimensional vector representation of the sentence.

Model Information