Sentence Embeddings - sbert medium (tuned)

Description

This model maps sentences & documents to a 512-dimensional dense vector space by using average pooling on top of BERT model. It’s also fine-tuned on the RxNorm dataset to help generalization over medication-related datasets.

Predicted Entities

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How to use

sentence_embeddings = BertSentenceEmbeddings.pretrained("sbert_jsl_medium_rxnorm_uncased", "en", "clinical/models")\
.setInputCols("sentence")\
.setOutputCol("sbert_embeddings")

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

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

Results

Gives a 512-dimensional vector representation of the sentence.

Model Information