Description
This pipeline extracts mentions of temporal entities from health-related text in colloquial language.
Live Demo Open in Colab Copy S3 URI
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_vop_temporal_pipeline", "en", "clinical/models")
pipeline.annotate("
I broke my arm playing football last month and had to get surgery in the orthopedic department. The cast just came off yesterday and I'm excited to start physical therapy and get back to the game.")
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_vop_temporal_pipeline", "en", "clinical/models")
val result = pipeline.annotate("
I broke my arm playing football last month and had to get surgery in the orthopedic department. The cast just came off yesterday and I'm excited to start physical therapy and get back to the game.")
Results
| chunk | ner_label |
|:-----------|:------------|
| last month | DateTime |
| yesterday | DateTime |
Model Information
| Model Name: | ner_vop_temporal_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.3+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 791.6 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- WordEmbeddingsModel
- MedicalNerModel
- NerConverter