BERT Sentence Embeddings trained on MEDLINE/PubMed

Description

This model uses a BERT base architecture pretrained from scratch on MEDLINE/PubMed. This is a BERT base architecture but some changes have been made to the original training and export scheme based on more recent learnings that improve its accuracy over the original BERT base checkpoint.

This model is intended to be used for a variety of English NLP tasks in the medical domain. The pre-training data contains more medical text and the model may not generalize to text outside of that domain.

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How to use

sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_pubmed", "en") \
.setInputCols("sentence") \
.setOutputCol("bert_sentence")

nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, sent_embeddings ])

val sent_embeddings = BertSentenceEmbeddings.pretrained("sent_bert_pubmed", "en")
.setInputCols("sentence")
.setOutputCol("bert_sentence")

val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, sent_embeddings ))

import nlu

text = ["I love NLP"]
sent_embeddings_df = nlu.load('en.embed_sentence.bert.pubmed').predict(text, output_level='sentence')
sent_embeddings_df

Model Information

Data Source

This Model has been imported from: https://tfhub.dev/google/experts/bert/pubmed/2