Description
This pretrained pipeline is built on the top of nerdl_tumour_demo model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
val text = "The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2."
val result = pipeline.fullAnnotate(text)
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("nerdl_tumour_demo_pipeline", "en", "clinical/models")
text = '''The final diagnosis was metastatic breast carcinoma, and it was classified as T2N1M1 stage IV. The histological grade of this 4 cm tumor was grade 2.'''
result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------|--------:|------:|:-------------|:-------------|
| 0 | breast carcinoma | 35 | 50 | Localization | |
Model Information
| Model Name: | nerdl_tumour_demo_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