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
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")
sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_ade", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
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 documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("sentences")
.setOutputCol("token")
val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_ade", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documenter, 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)
import nlu
nlu.load("en.classify.ade.seq_biobert").predict("""I felt a bit drowsy and had blurred vision after taking Aspirin.""")
Results
+----------------------------------------------------------------+------+
|text |result|
+----------------------------------------------------------------+------+
|I felt a bit drowsy and had blurred vision after taking Aspirin.|[True]|
+----------------------------------------------------------------+------+
Model Information
| Model Name: | bert_sequence_classifier_ade |
| Compatibility: | Healthcare NLP 3.4.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 406.0 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
This model is trained on a custom dataset comprising of CADEC, DRUG-AE and Twimed.
Benchmarking
label precision recall f1-score support
False 0.97 0.97 0.97 6884
True 0.87 0.85 0.86 1398
accuracy 0.95 0.95 0.95 8282
macro-avg 0.92 0.91 0.91 8282
weighted-avg 0.95 0.95 0.95 8282