Description
This pretrained pipeline is built on the top of ner_drugprot_clinical model.
Predicted Entities
CHEMICAL, GENE, GENE_AND_CHEMICAL
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
text = '''Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_drugprot_clinical_pipeline", "en", "clinical/models")
val text = "Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.clinical_drugprot.pipeline").predict("""Anabolic effects of clenbuterol on skeletal muscle are mediated by beta 2-adrenoceptor activation.""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:--------------------|--------:|------:|:------------|-------------:|
| 0 | clenbuterol | 20 | 30 | CHEMICAL | 0.9691 |
| 1 | beta 2-adrenoceptor | 67 | 85 | GENE | 0.89855 |
Model Information
| Model Name: | ner_drugprot_clinical_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