Description
This pretrained pipeline is built on the top of ner_chemicals model.
Predicted Entities
CHEM
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
text = '''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.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_chemicals_pipeline", "en", "clinical/models")
val text = "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 result = pipeline.fullAnnotate(text)
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
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:----------------------------|--------:|------:|:------------|-------------:|
| 0 | p - choloroaniline | 40 | 57 | CHEM | 0.935767 |
| 1 | chlorhexidine - digluconate | 90 | 116 | CHEM | 0.855367 |
| 2 | kanamycin | 168 | 176 | CHEM | 0.9824 |
| 3 | colistin | 180 | 187 | CHEM | 0.9911 |
| 4 | povidone - iodine | 193 | 209 | CHEM | 0.8111 |
Model Information
| Model Name: | ner_chemicals_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverterInternalModel