Description
Classify text/sentence in two categories:
True : The sentence is talking about a possible ADE
False : The sentences doesn’t have any information about an ADE.
Predicted Entities
True, False
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
tokenizer = Tokenizer().setInputCols(['document']).setOutputCol('token')
embeddings = BertEmbeddings.pretrained('biobert_clinical_base_cased')\
.setInputCols(["document", 'token'])\
.setOutputCol("word_embeddings")
sentence_embeddings = SentenceEmbeddings() \
.setInputCols(["document", "word_embeddings"]) \
.setOutputCol("sentence_embeddings") \
.setPoolingStrategy("AVERAGE")
classifier = ClassifierDLModel.pretrained('classifierdl_ade_clinicalbert', 'en', 'clinical/models')\
.setInputCols(['document', 'token', 'sentence_embeddings']).setOutputCol('class')
nlp_pipeline = Pipeline(stages=[document_assembler, tokenizer, embeddings, sentence_embeddings, classifier])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
annotations = light_pipeline.fullAnnotate(["I feel a bit drowsy & have a little blurred vision after taking an insulin", "I feel great after taking tylenol"])
import nlu
nlu.load("en.classify.ade.clinicalbert").predict("""I feel a bit drowsy & have a little blurred vision after taking an insulin""")
Results
| | text | label |
|--:|:---------------------------------------------------------------------------|:------|
| 0 | I feel a bit drowsy & have a little blurred vision after taking an insulin | True |
| 1 | I feel great after taking tylenol | False |
Model Information
| Model Name: | classifierdl_ade_clinicalbert |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | en |
| Dependencies: | biobert_clinical_base_cased |
Data Source
Trained on a custom dataset comprising of CADEC, DRUG-AE and Twimed.
Benchmarking
precision recall f1-score support
False 0.95 0.92 0.93 6923
True 0.64 0.78 0.70 1359
micro avg 0.89 0.89 0.89 8282
macro avg 0.80 0.85 0.82 8282
weighted avg 0.90 0.89 0.90 8282