Sentence Embeddings - sbert medium (tuned)

Description

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

Download

How to use

sbiobert_embeddings = BertSentenceEmbeddings\
.pretrained("sbert_jsl_medium_umls_uncased","en","clinicalmodels")\
.setInputCols(["sentence"])\
.setOutputCol("sbert_embeddings")
val sbiobert_embeddings = BertSentenceEmbeddings
.pretrained("sbert_jsl_medium_umls_uncased","en","clinical/models")
.setInputCols(Array("sentence"))
.setOutputCol("sbert_embeddings")
import nlu
nlu.load("en.embed_sentence.bert.jsl_medium_umls_uncased").predict("""Put your text here.""")

Results

Gives a 768 dimensional vector representation of the sentence.

Model Information

Model Name: sbert_jsl_medium_umls_uncased
Compatibility: Spark NLP for Healthcare 3.0.3+
License: Licensed
Edition: Official
Language: en
Case sensitive: false

Data Source

Tuned on MedNLI and UMLS dataset

Benchmarking

MedNLI   Score
Acc      0.744 
STS(cos) 0.695