Sentence Embeddings - Biobert cased (MedNLI)

Description

This model is trained to generate contextual sentence embeddings of input sentences. It has been fine-tuned on MedNLI dataset to provide sota performance on STS and SentEval Benchmarks.

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How to use

Use as part of an nlp pipeline with the following stages: DocumentAssembler, SentenceDetector, BertSentenceEmbeddings. The output of this model can be used in tasks like NER, Classification, Entity Resolution etc.

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

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

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

Results

Gives a 768 dimensional vector representation of the sentence.

Model Information

Data Source

Tuned on MedNLI dataset using Biobert weights.