Description
This pretrained pipeline includes the Medical Bert for Sequence Classification model to identify whether the suggestion that is mentioned in the text is medically sound. The pipeline is built on the top of bert_sequence_classifier_vop_sound_medical model.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models")
pipeline.annotate("I had a lung surgery for emphyema and after surgery my xray showing some recovery.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_sequence_classifier_vop_sound_medical_pipeline", "en", "clinical/models")
val result = pipeline.annotate(I had a lung surgery for emphyema and after surgery my xray showing some recovery.)
Results
| text | prediction |
|:-----------------------------------------------------------------------------------|:-------------|
| I had a lung surgery for emphyema and after surgery my xray showing some recovery. | True |
Model Information
| Model Name: | bert_sequence_classifier_vop_sound_medical_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 406.4 MB |
Included Models
- DocumentAssembler
- TokenizerModel
- MedicalBertForSequenceClassification