Description
This model has been trained on a curated list of clinical and biomedical datasets. It has been fine-tuned for Few-Shot Assertion but can also be utilized for other purposes, such as Classification and Retrieval-Augmented Generation (RAG).
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
e5_embeddings = E5Embeddings.pretrained("e5_base_v2_embeddings_medical_assertion_base", "en", "clinical/models")\
.setInputCols(["document"])\
.setOutputCol("e5_embeddings")
pipeline = Pipeline().setStages([document_assembler, e5_embeddings])
text = [
["I feel a bit drowsy after taking an insulin."],
["Peter Parker is a nice guy 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 e5_embeddings = E5Embeddings.pretrained("e5_base_v2_embeddings_medical_assertion_base", "en", "clinical/models")
.setInputCols(Array("document"))
.setOutputCol("e5_embeddings")
val pipeline = new Pipeline().setStages(Array(document_assembler, e5_embeddings))
val result = pipeline.fit(data).transform(data)
Results
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| embeddings|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[{sentence_embeddings, 0, 43, I feel a bit drowsy after taking an insulin., {sentence -> 0}, [-0.010884863, 0.007896974, 0.03736705, -0.057596024, 0.028397387, -9.937644E-4, -0.06973768, -0.0125734...|
|[{sentence_embeddings, 0, 47, Peter Parker is a nice guy and lives in New York, {sentence -> 0}, [0.108221725, 0.050705638, 0.025993714, 0.04520446, -0.018801026, -0.01583123, 0.020007214, 0.036804...|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Model Information
| Model Name: | e5_base_v2_embeddings_medical_assertion_base |
| Compatibility: | Healthcare NLP 5.3.3+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document] |
| Output Labels: | [embeddings] |
| Language: | en |
| Size: | 393.0 MB |
| Case sensitive: | false |