Description
This pretrained pipeline is built on the top of ner_deid_subentity_bert model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
text = '''Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
Tel: +40(235)413773
Data setului de analize: 25 May 2022 15:36:00
Nume si Prenume : BUREAN MARIA, Varsta: 77
Medic : Agota Evelyn Tımar
C.N.P : 2450502264401'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("ner_deid_subentity_bert_pipeline", "ro", "clinical/models")
val text = "Spitalul Pentru Ochi de Deal, Drumul Oprea Nr. 972 Vaslui, 737405 România
Tel: +40(235)413773
Data setului de analize: 25 May 2022 15:36:00
Nume si Prenume : BUREAN MARIA, Varsta: 77
Medic : Agota Evelyn Tımar
C.N.P : 2450502264401"
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunks | begin | end | ner_label | confidence |
|---:|:-----------------------------|--------:|------:|:------------|-------------:|
| 0 | Spitalul Pentru Ochi de Deal | 0 | 27 | HOSPITAL | 0.84306 |
| 1 | Drumul Oprea Nr. 972 | 30 | 49 | STREET | 0.99784 |
| 2 | Vaslui | 51 | 56 | CITY | 0.9896 |
| 3 | 737405 | 59 | 64 | ZIP | 1 |
| 4 | +40(235)413773 | 79 | 92 | PHONE | 1 |
| 5 | 25 May 2022 | 119 | 129 | DATE | 1 |
| 6 | BUREAN MARIA | 158 | 169 | PATIENT | 0.7259 |
| 7 | 77 | 180 | 181 | AGE | 1 |
| 8 | Agota Evelyn Tımar | 191 | 208 | DOCTOR | 0.803667 |
| 9 | 2450502264401 | 218 | 230 | IDNUM | 0.9995 |
Model Information
| Model Name: | ner_deid_subentity_bert_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | ro |
| Size: | 484.0 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- BertEmbeddings
- MedicalNerModel
- NerConverterInternalModel