Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_anatem model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
text = '''Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_anatem_pipeline", "en", "clinical/models")
val text = "Malignant cells often display defects in autophagy, an evolutionarily conserved pathway for degrading long-lived proteins and cytoplasmic organelles. However, as yet, there is no genetic evidence for a role of autophagy genes in tumor suppression. The beclin 1 autophagy gene is monoallelically deleted in 40 - 75 % of cases of human sporadic breast, ovarian, and prostate cancer."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-----------------------|--------:|------:|:------------|-------------:|
| 0 | Malignant cells | 0 | 14 | Anatomy | 0.999951 |
| 1 | cytoplasmic organelles | 126 | 147 | Anatomy | 0.999937 |
| 2 | tumor | 229 | 233 | Anatomy | 0.999871 |
| 3 | breast | 343 | 348 | Anatomy | 0.999842 |
| 4 | ovarian | 351 | 357 | Anatomy | 0.99998 |
| 5 | prostate cancer | 364 | 378 | Anatomy | 0.999968 |
Model Information
| Model Name: | bert_token_classifier_ner_anatem_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