Ner DL Model Clinical (Large)

Description

Named Entity recognition annotator allows for a generic model to be trained by utilizing a deep learning algorithm (Char CNNs - BiLSTM - CRF - word embeddings) inspired on a former state of the art model for NER: Chiu & Nicols, Named Entity Recognition with Bidirectional LSTM,CNN.

Predicted Entities

PROBLEM,TEST,TREATMENT

Live Demo Open in Colab Download

How to use

model = NerDLModel.pretrained("ner_large_clinical","en","clinical/models")\
	.setInputCols("sentence","token","word_embeddings")\
	.setOutputCol("ner")
val model = NerDLModel.pretrained("ner_large_clinical","en","clinical/models")
	.setInputCols("sentence","token","word_embeddings")
	.setOutputCol("ner")

Model Information

Name: ner_large_clinical
Type: NerDLModel
Compatibility: Spark NLP 2.5.0+
License: Licensed
Edition: Official
Input labels: [sentence, token, word_embeddings]
Output labels: [ner]
Language: en
Dependencies: embeddings_clinical

Data Source

Trained on data gathered and manually annotated by John Snow Labs https://www.johnsnowlabs.com/data/