Description
This pretrained pipeline is built on the top of ner_events_clinical_langtest model.
How to use
from sparknlp.pretrained import PretrainedPipeline
ner_pipeline = PretrainedPipeline("ner_events_clinical_langtest_pipeline", "en", "clinical/models")
result = ner_pipeline.annotate("""The patient presented to the emergency room last evening""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val ner_pipeline = PretrainedPipeline("ner_events_clinical_langtest_pipeline", "en", "clinical/models")
val result = ner_pipeline.annotate("""The patient presented to the emergency room last evening""")
Results
| | chunks | begin | end | entities |
|---:|:-------------------|--------:|------:|:--------------|
| 0 | presented | 12 | 20 | EVIDENTIAL |
| 1 | the emergency room | 25 | 42 | CLINICAL_DEPT |
| 2 | last evening | 44 | 55 | DATE |
Model Information
| Model Name: | ner_events_clinical_langtest_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 5.1.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter