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”.
How to use
...
embeddings = BertEmbeddings.pretrained("biobert_discharge_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(pd.DataFrame({"text": ["I hate cancer"]})))
...
val embeddings = BertEmbeddings.pretrained("biobert_discharge_base_cased", "en")
.setInputCols("sentence", "token")
.setOutputCol("embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, sentence_detector, tokenizer, embeddings))
val result = pipeline.fit(Seq.empty["I hate cancer"].toDS.toDF("text")).transform(data)
import nlu
text = ["I hate cancer"]
embeddings_df = nlu.load('en.embed.biobert.discharge_base_cased').predict(text, output_level='token')
embeddings_df
Results
token en_embed_biobert_discharge_base_cased_embeddings
I [0.0036486536264419556, 0.3796533942222595, -0...
hate [0.1914958357810974, 0.6709488034248352, -0.49...
cancer [0.04618441313505173, -0.04562612622976303, -0...
Model Information
| Model Name: | biobert_discharge_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