Sentence Embeddings - sbert mini (tuned)

Description

This model is trained to generate contextual sentence embeddings of input sentences.

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How to use

sbiobert_embeddings = BertSentenceEmbeddings\
.pretrained("sbert_jsl_mini_umls_uncased","en","clinical/models")\
.setInputCols(["sentence"])\
.setOutputCol("sbert_embeddings")

val sbiobert_embeddings = BertSentenceEmbeddings
.pretrained("sbert_jsl_mini_umls_uncased","en","clinical/models")
.setInputCols(Array("sentence"))
.setOutputCol("sbert_embeddings")

import nlu
nlu.load("en.embed_sentence.bert.jsl_mini_umlsuncased").predict("""Put your text here.""")

Results

Gives a 768 dimensional vector representation of the sentence.

Model Information

Data Source

Tuned on MedNLI and UMLS dataset

Benchmarking

MedNLI    Score
Acc       0.677
STS(cos)  0.681