Ner DL Model Bionlp

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. Pretrained named entity recognition deep learning model for biology and genetics terms.

Predicted Entities

Amino_acid, Anatomical_system, Cancer, Cell, Cellular_component, Developing_anatomical_structure, Gene_or_gene_product, Immaterial_anatomical_entity, Organ, Organism, Organism_subdivision, Organism_substance, Pathological_formation, Simple_chemical, Tissue, tissue_structure

Live DemoOpen in ColabDownload

How to use

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

Model Information

Name: ner_bionlp  
Type: NerDLModel  
Compatibility: Spark NLP 2.4.0+  
License: Licensed  
Edition: Official  
Input labels: [sentence, token, word_embeddings]  
Output labels: [ner]  
Language: en  
Case sensitive: False  
Dependencies: embeddings_clinical  

Data Source

Trained on Cancer Genetics (CG) task of the BioNLP Shared Task 2013 with embeddings_clinical http://2013.bionlp-st.org/tasks/cancer-genetics