Description
This pretrained pipeline is built on the top of ner_biomedical_bc2gm model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
text = '''Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
val text = "Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections."
val result = pipeline.fullAnnotate(text)
import nlu
nlu.load("en.med_ner.biomedical_bc2gm.pipeline").predict("""Immunohistochemical staining was positive for S-100 in all 9 cases stained, positive for HMB-45 in 9 (90%) of 10, and negative for cytokeratin in all 9 cases in which myxoid melanoma remained in the block after previous sections.""")
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-------------|--------:|------:|:-------------|-------------:|
| 0 | S-100 | 46 | 50 | GENE_PROTEIN | 0.9911 |
| 1 | HMB-45 | 89 | 94 | GENE_PROTEIN | 0.9944 |
| 2 | cytokeratin | 131 | 141 | GENE_PROTEIN | 0.9951 |
Model Information
| Model Name: | ner_biomedical_bc2gm_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
- NerConverter