Description
This model is a few-shot classification model designed to identify and classify tweets reporting Adverse Drug Events (ADEs). Utilizing the few-shot learning approach, it can effectively learn from a small number of labeled examples, making it highly adaptable to new and unseen classes.
Predicted Entities
ADE, noADE
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
large_few_shot_classifier = LargeFewShotClassifierModel()\
.pretrained('large_fewshot_classifier_ade')\
.setInputCols("document")\
.setOutputCol("prediction")
pipeline = Pipeline().setStages([
document_assembler,
large_few_shot_classifier
])
text_list = [
["The patient developed severe liver toxicity after taking the medication for three weeks"],
["He experienced no complications during the treatment and reported feeling much better."],
["She experienced a sudden drop in blood pressure after the administration of the new drug."],
["The doctor recommended a daily dosage of the vitamin supplement to improve her health."]
]
data = spark.createDataFrame(text_list, ["text"])
result = pipeline.fit(data).transform(data)
result.select("text", col("prediction.result").getItem(0).alias("result")).show(truncate=False)
val documentAssembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val largeFewShotClassifier = LargeFewShotClassifierModel()
.pretrained("large_fewshot_classifier_ade")
.setInputCols("document")
.setOutputCol("prediction")
val pipeline = new Pipeline().setStages(Array(
documentAssembler,
largeFewShotClassifier
))
val textList = Seq(
("The patient developed severe liver toxicity after taking the medication for three weeks"),
("He experienced no complications during the treatment and reported feeling much better."),
("She experienced a sudden drop in blood pressure after the administration of the new drug."),
("The doctor recommended a daily dosage of the vitamin supplement to improve her health.")
)
val data = spark.createDataFrame(textList).toDF("text")
val result = pipeline.fit(data).transform(data)
result.select(col("text"), col("prediction.result").getItem(0).alias("result")).show(truncate = false)
Results
+-----------------------------------------------------------------------------------------+------+
|text |result|
+-----------------------------------------------------------------------------------------+------+
|The patient developed severe liver toxicity after taking the medication for three weeks |ADE |
|He experienced no complications during the treatment and reported feeling much better. |noADE |
|She experienced a sudden drop in blood pressure after the administration of the new drug.|ADE |
|The doctor recommended a daily dosage of the vitamin supplement to improve her health. |noADE |
+-----------------------------------------------------------------------------------------+------+
Model Information
| Model Name: | large_fewshot_classifier_ade |
| Compatibility: | Healthcare NLP 5.4.0+ |
| License: | Licensed |
| Edition: | Official |
| Language: | en |
| Size: | 406.9 MB |
| Case sensitive: | false |
References
This model has been trained using internal datasets.
Benchmarking
label precision recall f1-score support
ADE 0.9560 0.9136 0.9343 2732
noADE 0.9329 0.9661 0.9492 3397
accuracy - - 0.9427 6129
macro-avg 0.9444 0.9399 0.9418 6129
weighted-avg 0.9432 0.9427 0.9426 6129