Description
This pretrained pipeline is built on the top of ner_anatomy_coarse model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
text = '''content in the lung tissue'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_anatomy_coarse_pipeline", "en", "clinical/models")
val text = "content in the lung tissue"
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.anatomy_coarse.pipeline").predict("""content in the lung tissue""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:------------|-------------:|
| 0 | lung tissue | 15 | 25 | Anatomy | 0.99655 |
Model Information
| Model Name: | ner_anatomy_coarse_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