Sentence Embeddings - sbiobert (tuned)

Description

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

Live Demo Open in Colab Copy S3 URI

How to use

sbiobert_embeddings = BertSentenceEmbeddings.pretrained("sbiobert_jsl_cased","en","clinical/models").setInputCols(["sentence"]).setOutputCol("sbert_embeddings")
val sbiobert_embeddings = BertSentenceEmbeddings
.pretrained("sbiobert_jsl_cased","en","clinical/models")
.setInputCols(Array("sentence"))
.setOutputCol("sbert_embeddings")
import nlu
nlu.load("en.embed_sentence.biobert.jsl_cased").predict("""Put your text here.""")

Results

Gives a 768 dimensional vector representation of the sentence.

Model Information

Model Name: sbiobert_jsl_cased
Compatibility: Healthcare NLP 3.1.0+
License: Licensed
Edition: Official
Language: en
Case sensitive: true

Data Source

Tuned on MedNLI and UMLS dataset

Benchmarking

MedNLI    Score
Acc       0.788
STS(cos)  0.736