Description
This pretrained pipeline is built on the top of bert_token_classifier_ner_bacteria model.
Predicted Entities
SPECIES
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
text = '''Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ner_bacteria_pipeline", "en", "clinical/models")
val text = "Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T))."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.classify.token_bert.bacteria_ner.pipeline").predict("""Based on these genetic and phenotypic properties, we propose that strain SMSP (T) represents a novel species of the genus Methanoregula, for which we propose the name Methanoregula formicica sp. nov., with the type strain SMSP (T) (= NBRC 105244 (T) = DSM 22288 (T)).""")
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:------------------------|--------:|------:|:------------|-------------:|
| 0 | SMSP (T) | 73 | 80 | SPECIES | 0.99985 |
| 1 | Methanoregula formicica | 167 | 189 | SPECIES | 0.999787 |
| 2 | SMSP (T) | 222 | 229 | SPECIES | 0.999871 |
Model Information
| Model Name: | bert_token_classifier_ner_bacteria_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.9 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverterInternalModel