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. Neoplasms NER is a Named Entity Recognition model that annotates text to find references to tumors. The only entity it annotates is MalignantNeoplasm. Neoplasms NER is trained with the ‘embeddings_scielowiki_300d’ word embeddings model, so be sure to use the same embeddings in the pipeline.
Predicted Entities
MORFOLOGIA_NEOPLASIA
How to use
model = NerDLModel.pretrained("ner_neoplasms","es","clinical/models")\
.setInputCols("sentence","token","word_embeddings")\
.setOutputCol("ner")
val model = NerDLModel.pretrained("ner_neoplasms","es","clinical/models")
.setInputCols("sentence","token","word_embeddings")
.setOutputCol("ner")
Model Information
| Name: | ner_neoplasms | |
| Type: | NerDLModel | |
| Compatibility: | Spark NLP 2.5.3+ | |
| License: | Licensed | |
| Edition: | Official | |
| Input labels: | [sentence, token, word_embeddings] | |
| Output labels: | [ner] | |
| Language: | es | |
| Case sensitive: | False | |
| Dependencies: | embeddings_scielowiki_300d |
Data Source
Named Entity Recognition model for Neoplasic Morphology https://temu.bsc.es/cantemist/