Description
This pretrained pipeline is built on the top of re_test_problem_finding model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("re_test_problem_finding_pipeline", "en", "clinical/models")
pipeline.fullAnnotate("Targeted biopsy of this lesion for histological correlation should be considered.")
import nlu
nlu.load("en.relation.test_problem_finding.pipeline").predict("""Targeted biopsy of this lesion for histological correlation should be considered.""")
Results
| index | relations | entity1 | chunk1 | entity2 | chunk2 |
|-------|--------------|--------------|---------------------|--------------|---------|
| 0 | 1 | PROCEDURE | biopsy | SYMPTOM | lesion |
Model Information
| Model Name: | re_test_problem_finding_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 1.7 GB |
Included Models
- DocumentAssembler
- SentenceDetector
- TokenizerModel
- PerceptronModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter
- DependencyParserModel
- RelationExtractionModel