Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_chemprot model.
Predicted Entities
CHEMICAL, GENE-N, GENE-Y
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
text = '''Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_chemprot_pipeline", "en", "clinical/models")
val text = "Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.classify.token_bert.chemprot_pipeline").predict("""Keratinocyte growth factor and acidic fibroblast growth factor are mitogens for primary cultures of mammary epithelium.""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:------------|-------------:|
| 0 | Keratinocyte | 0 | 11 | GENE-Y | 0.999147 |
| 1 | growth | 13 | 18 | GENE-Y | 0.999752 |
| 2 | factor | 20 | 25 | GENE-Y | 0.999685 |
| 3 | acidic | 31 | 36 | GENE-Y | 0.999661 |
| 4 | fibroblast | 38 | 47 | GENE-Y | 0.999753 |
| 5 | growth | 49 | 54 | GENE-Y | 0.999771 |
| 6 | factor | 56 | 61 | GENE-Y | 0.999742 |
Model Information
| Model Name: | bert_token_classifier_ner_chemprot_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.7 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverter