Description
This pretrained pipeline is built on the top of ner_biomedical_bc2gm model.
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
result = pipeline.fullAnnotate("""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.""")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_biomedical_bc2gm_pipeline", "en", "clinical/models")
val result = pipeline.fullAnnotate("""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""")
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
+-----------+------------+
|chunk |ner_label |
+-----------+------------+
|S-100 |GENE_PROTEIN|
|HMB-45 |GENE_PROTEIN|
|cytokeratin|GENE_PROTEIN|
+-----------+------------+
Model Information
| Model Name: | ner_biomedical_bc2gm_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 3.5.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter