Description
Classify texts/sentences in two categories:
-
True: The sentence is talking about a possible ADE. -
False: The sentence doesn’t have any information about an ADE.
This model is a BioBERT-based classifier.
Predicted Entities
Live Demo Open in Colab Copy S3 URI
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols("document")\
.setOutputCol("token")
sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_ade_augmented_v2", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("classes")
pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])
data = spark.createDataFrame([["I felt a bit drowsy and had blurred vision after taking Aspirin."]]).toDF("text")
result = pipeline.fit(data).transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_ade_augmented_v2", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val data = Seq("I felt a bit drowsy and had blurred vision after taking Aspirin.").toDF("text")
val result = pipeline.fit(data).transform(data)
Results
+------+----------------------------------------------------------------+
|result|text |
+------+----------------------------------------------------------------+
|[True]|I felt a bit drowsy and had blurred vision after taking Aspirin.|
+------+----------------------------------------------------------------+
Model Information
| Model Name: | bert_sequence_classifier_ade_augmented_v2 |
| Compatibility: | Healthcare NLP 5.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 406.6 MB |
| Case sensitive: | false |
| Max sentence length: | 512 |
Benchmarking
label precision recall f1-score support
False 0.97 0.97 0.97 2000
True 0.90 0.89 0.90 530
accuracy - - 0.96 2530
macro-avg 0.93 0.93 0.93 2530
weighted-avg 0.96 0.96 0.96 2530