Description
This pretrained pipeline is built on the top of bert_token_classifier_ade_tweet_binary model.
Predicted Entities
How to use
from sparknlp.pretrained import PretrainedPipeline
pipeline = PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
text = '''I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs.'''
result = pipeline.fullAnnotate(text)
import com.johnsnowlabs.nlp.pretrained.PretrainedPipeline
val pipeline = new PretrainedPipeline("bert_token_classifier_ade_tweet_binary_pipeline", "en", "clinical/models")
val text = "I used to be on paxil but that made me more depressed and prozac made me angry. Maybe cos of the insulin blocking effect of seroquel but i do feel sugar crashes when eat fast carbs."
val result = pipeline.fullAnnotate(text)
Results
| | ner_chunk | begin | end | ner_label | confidence |
|---:|:-----------------|--------:|------:|:------------|-------------:|
| 0 | depressed | 44 | 52 | ADE | 0.999755 |
| 1 | angry | 73 | 77 | ADE | 0.999608 |
| 2 | insulin blocking | 97 | 112 | ADE | 0.738712 |
| 3 | sugar crashes | 147 | 159 | ADE | 0.993742 |
Model Information
| Model Name: | bert_token_classifier_ade_tweet_binary_pipeline |
| Type: | pipeline |
| Compatibility: | Healthcare NLP 4.4.4+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 404.8 MB |
Included Models
- DocumentAssembler
- SentenceDetectorDLModel
- TokenizerModel
- MedicalBertForTokenClassifier
- NerConverterInternalModel