Description
This model contains a pre-trained weights of ClinicalBERT for generic clinical text. 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”.
How to use
...
embeddings = BertSentenceEmbeddings.pretrained("sent_biobert_clinical_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([['I hate cancer', "Antibiotics aren't painkiller"]], ["text"]))
...
val embeddings = BertSentenceEmbeddings.pretrained("sent_biobert_clinical_base_cased", "en")
.setInputCols("sentence")
.setOutputCol("sentence_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, embeddings))
val data = Seq("I hate cancer, "Antibiotics aren't painkiller").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["I hate cancer", "Antibiotics aren't painkiller"]
embeddings_df = nlu.load('en.embed_sentence.biobert.clinical_base_cased').predict(text, output_level='sentence')
embeddings_df
Results
sentence en_embed_sentence_biobert_clinical_base_cased_embeddings
I hate cancer [0.397987425327301, 0.6472950577735901, -0.551...
Antibiotics aren't painkiller [0.19467104971408844, 0.3496762812137604, -0.3...
Model Information
| Model Name: | sent_biobert_clinical_base_cased |
| Type: | embeddings |
| Compatibility: | Spark NLP 2.6.0 |
| 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