Cancer Genetics Entity Extracter

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

DNA, RNA, cell_line, cell_type, protein

Live Demo Open in ColabDownload

How to use

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

Model Information

Name: ner_cancer_genetics
Type: NerDLModel
Compatibility: 2.4.2
License: Licensed
Edition: Official
Input labels: sentence, token, word_embeddings
Output labels: ner
Language: en
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