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 = BertEmbeddings.pretrained("biobert_clinical_base_cased", "en") \
.setInputCols("sentence", "token") \
.setOutputCol("embeddings")
nlp_pipeline = Pipeline(stages=[document_assembler, sentence_detector, tokenizer, embeddings])
pipeline_model = nlp_pipeline.fit(spark.createDataFrame([[""]]).toDF("text"))
result = pipeline_model.transform(spark.createDataFrame([['I hate cancer']], ["text"]))
val embeddings = BertEmbeddings.pretrained("biobert_clinical_base_cased", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val data = Seq("I hate cancer").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["I hate cancer"]
embeddings_df = nlu.load('en.embed.biobert.clinical_base_cased').predict(text, output_level='token')
embeddings_df
Results
token en_embed_biobert_clinical_base_cased_embeddings
I [0.2206662893295288, 0.41324421763420105, -0.3...
hate [-0.19311018288135529, 0.6037888526916504, -0....
cancer [0.2895708680152893, 0.22499887645244598, -0.5...
Model Information
| Model Name: | biobert_clinical_base_cased |
| Type: | embeddings |
| Compatibility: | Spark NLP 2.6.2 |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence, token] |
| Output Labels: | [word_embeddings] |
| Language: | [en] |
| Dimension: | 768 |
| Case sensitive: | true |
Data Source
The model is imported from https://github.com/EmilyAlsentzer/clinicalBERT