Description
This model is a BioBERT based classifier that can classify self-report the exact age into social media forum (Reddit) posts.
Predicted Entities
self_report_age, no_report
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_exact_age_reddit", "en", "clinical/models")\
.setInputCols(["document",'token'])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
data = spark.createDataFrame(["Is it bad for a 19 year old it's been getting worser.",
"I was about 10. So not quite as young as you but young."], StringType()).toDF("text")
result = pipeline.fit(data).transform(data)
result.select("text", "class.result").show(truncate=False)
val documenter = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_exact_age_reddit", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documenter, tokenizer, sequenceClassifier))
val data = Seq(Array("Is it bad for a 19 year old it's been getting worser.",
"I was about 10. So not quite as young as you but young.")).toDS.toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.exact_age").predict("""I was about 10. So not quite as young as you but young.""")
Results
+-------------------------------------------------------+-----------------+
|text |result |
+-------------------------------------------------------+-----------------+
|Is it bad for a 19 year old it's been getting worser. |[self_report_age]|
|I was about 10. So not quite as young as you but young.|[no_report] |
+-------------------------------------------------------+-----------------+
Model Information
| Model Name: | bert_sequence_classifier_exact_age_reddit |
| Compatibility: | Healthcare NLP 4.0.0+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 406.5 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
The dataset is disease-specific and consists of posts collected via a series of keywords associated with dry eye disease.
Benchmarking
label precision recall f1-score support
no_report 0.9324 0.9577 0.9449 1325
self_report_age 0.9124 0.8637 0.8874 675
accuracy - - 0.9260 2000
macro-avg 0.9224 0.9107 0.9161 2000
weighted-avg 0.9256 0.9260 0.9255 2000