Description
This model is a BioBERT based classifier that can classify self-report the exact age into social media data.
Predicted Entities
self_report_age, no_report
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
sequence_classifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_self_reported_age_tweet_onnx", "en", "clinical/models")\
.setInputCols(["document", "token"])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequence_classifier
])
data = spark.createDataFrame(["Who knew I would spend my Saturday mornings at 21 still watching Disney channel",
"My girl, Fancy, just turned 17. She’s getting up there, but she still has the energy of a puppy"], StringType()).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
document_assembler = nlp.DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = nlp.Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
sequenceClassifier = medical.BertForSequenceClassification.pretrained("bert_sequence_classifier_self_reported_age_tweet_onnx", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("classes")
pipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
data = spark.createDataFrame(["Who knew I would spend my Saturday mornings at 21 still watching Disney channel",
"My girl, Fancy, just turned 17. She’s getting up there, but she still has the energy of a puppy"], StringType()).toDF("text")
model = pipeline.fit(data)
result = model.transform(data)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")
val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_self_reported_age_tweet_onnx", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val data = Seq(Array("Who knew I would spend my Saturday mornings at 21 still watching Disney channel",
"My girl, Fancy, just turned 17. She’s getting up there, but she still has the energy of a puppy")).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+-----------------------------------------------------------------------------------------------+-----------------+
|text |result |
+-----------------------------------------------------------------------------------------------+-----------------+
|Who knew I would spend my Saturday mornings at 21 still watching Disney channel |[self_report_age]|
|My girl, Fancy, just turned 17. She’s getting up there, but she still has the energy of a puppy|[no_report] |
+-----------------------------------------------------------------------------------------------+-----------------+
Model Information
| Model Name: | bert_sequence_classifier_self_reported_age_tweet_onnx |
| Compatibility: | Healthcare NLP 6.1.1+ |
| License: | Licensed |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [label] |
| Language: | en |
| Size: | 437.7 MB |
| Case sensitive: | true |