Description
This pretrained pipeline is built on the top of ner_chemicals model.
How to use
pipeline = PretrainedPipeline("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("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.med_ner.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
+---------------------------+--------+
|chunks |entities|
+---------------------------+--------+
|p - choloroaniline |CHEM |
|chlorhexidine - digluconate|CHEM |
|kanamycin |CHEM |
|colistin |CHEM |
|povidone - iodine |CHEM |
+---------------------------+--------+
Model Information
| Model Name: | ner_chemicals_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter