Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_ncbi_disease model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
val text = "Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_ncbi_disease_pipeline", "en", "clinical/models")
text = '''Kniest dysplasia is a moderately severe type II collagenopathy, characterized by short trunk and limbs, kyphoscoliosis, midface hypoplasia, severe myopia, and hearing loss.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-----------------------|--------:|------:|:------------|-------------:|
| 0 | Kniest dysplasia | 0 | 15 | Disease | 0.999886 |
| 1 | type II collagenopathy | 40 | 61 | Disease | 0.999934 |
| 2 | kyphoscoliosis | 104 | 117 | Disease | 0.99994 |
| 3 | midface hypoplasia | 120 | 137 | Disease | 0.999911 |
| 4 | myopia | 147 | 152 | Disease | 0.999894 |
| 5 | hearing loss | 159 | 170 | Disease | 0.999351 |
Model Information
| Model Name: | bert_token_classifier_ner_ncbi_disease_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.8 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverterInternalModel