Description
This model is trained on a list of clinical and biomedical datasets curated in-house
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
mpnet_embedding = MPNetEmbeddings.pretrained("mpnet_embeddings_medical_assertion_jsl", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("mpnet_embeddings")
pipeline = Pipeline().setStages([document_assembler, mpnet_embedding])
text = [
["I feel a bit drowsy after taking an insulin."],
["Peter Parker is a nice lad and lives in New York"]
]
data = spark.createDataFrame(text).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val mpnet_embedding = MPNetEmbeddings.pretrained("mpnet_embeddings_medical_assertion_jsl", "en", "clinical/models")
.setInputCols(Array("document"))
.setOutputCol("mpnet_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, mpnet_embedding))
val result = pipeline.fit(data).transform(data)
Results
| | assertion_embedding |
|---:|:--------------------------------------------------------------------------|
| 0 | [Row(annotatorType='sentence_embeddings', begin=0, end=43, result='I feel a bit drowsy after taking an insulin.', metadata={'sentence': '0'}, embeddings=[-0.014557844027876854, -0.04016261175274849, 0.013381453230977058, 0.07861644774675369, -0.030201803892850876, -0.016034666448831558, ...])] |
| 1 | [Row(annotatorType='sentence_embeddings', begin=0, end=47, result='Peter Parker is a nice lad and lives in New York', metadata={'sentence': '0'}, embeddings=[-0.019502660259604454, -0.05983254685997963, 0.011300831101834774, 0.00489014433696866, 0.012493547983467579, -0.027176303789019585...])] |
Model Information
| Model Name: | mpnet_embeddings_medical_assertion_jsl |
| Compatibility: | Healthcare NLP 5.3.2+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [assertion_embedding] |
| Language: | en |
| Size: | 406.9 MB |
| Case sensitive: | false |