Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_chemicals model.
How to use
pipeline = PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
pipeline.annotate("The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.")
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemicals_pipeline", "en", "clinical/models")
pipeline.annotate("The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.")
import nlu
nlu.load("en.classify.token_bert.chemicals_pipeline").predict("""The results have shown that the product p - choloroaniline is not a significant factor in chlorhexidine - digluconate associated erosive cystitis. A high percentage of kanamycin - colistin and povidone - iodine irrigations were associated with erosive cystitis.""")
Results
+---------------------------+---------+
|chunk |ner_label|
+---------------------------+---------+
|p - choloroaniline |CHEM |
|chlorhexidine - digluconate|CHEM |
|kanamycin |CHEM |
|colistin |CHEM |
|povidone - iodine |CHEM |
+---------------------------+---------+
Model Information
| Model Name: | bert_token_classifier_ner_chemicals_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.7 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverter