BioBERT Sentence Embeddings (Discharge)

Description

This model contains a pre-trained weights of ClinicalBERT for discharge summaries. This domain-specific model has performance improvements on 3/5 clinical NLP tasks andd establishing a new state-of-the-art on the MedNLI dataset. The details are described in the paper “Publicly Available Clinical BERT Embeddings”.

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How to use

...
embeddings = BertSentenceEmbeddings.pretrained("sent_biobert_discharge_base_cased", "en") \
      .setInputCols("sentence") \
      .setOutputCol("sentence_embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame(pd.DataFrame({"text": ["I hate cancer, "Antibiotics aren't painkiller""]})))
...
val embeddings = BertSentenceEmbeddings.pretrained("sent_biobert_discharge_base_cased", "en")
      .setInputCols("sentence")
      .setOutputCol("sentence_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, embeddings))
val result = pipeline.fit(Seq.empty["I hate cancer, "Antibiotics aren't painkiller""].toDS.toDF("text")).transform(data)
import nlu

text = ["I hate cancer", "Antibiotics aren't painkiller"]
embeddings_df = nlu.load('en.embed_sentence.biobert.discharge_base_cased').predict(text, output_level='sentence')
embeddings_df

Results

        sentence	                en_embed_sentence_biobert_discharge_base_cased_embeddings

0	I hate cancer	                [0.3155321180820465, 0.37484583258628845, -0.4...
1	Antibiotics aren't painkiller	[0.3543206453323364, 0.0787968561053276, -0.08...

Model Information

Model Name: sent_biobert_discharge_base_cased
Type: embeddings
Compatibility: Spark NLP 2.6.2
License: Open Source
Edition: Official
Input Labels: [sentence]
Output Labels: [sentence_embeddings]
Language: [en]
Dimension: 768
Case sensitive: true

Data Source

The model is imported from https://github.com/EmilyAlsentzer/clinicalBERT