Description
Classification of tweets indicating self-reported COVID-19 vaccination status. This model involves the identification of self-reported COVID-19 vaccination status in English tweets.
Predicted Entities
Vaccine_chatter, Self_reports
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_vaccine_status_tweet_onnx", "en", "clinical/models")\
.setInputCols(["document", "token"])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequence_classifier
])
data = spark.createDataFrame(["I came to a point finally and i've vaccinated, didnt feel pain.Suggest everyone",
"If Pfizer believes we need a booster shot, we need it. Who knows their product better? Following the guidance of @CDCgov is how I wound up w/ Covid-19 and having to shut down my K-2 classroom for an entire week. I will do whatever it takes to protect my students, friends, family."], 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_vaccine_status_tweet_onnx", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("classes")
pipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
example = spark.createDataFrame(["I came to a point finally and i've vaccinated, didnt feel pain.Suggest everyone",
"If Pfizer believes we need a booster shot, we need it. Who knows their product better? Following the guidance of @CDCgov is how I wound up w/ Covid-19 and having to shut down my K-2 classroom for an entire week. I will do whatever it takes to protect my students, friends, family."], 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_vaccine_status_tweet_onnx", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq(Array("I came to a point finally and i've vaccinated, didnt feel pain.Suggest everyone",
"If Pfizer believes we need a booster shot, we need it. Who knows their product better? Following the guidance of @CDCgov is how I wound up w/ Covid-19 and having to shut down my K-2 classroom for an entire week. I will do whatever it takes to protect my students, friends, family.")).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+
|text |result |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+
|I came to a point finally and i've vaccinated, didnt feel pain. Suggest everyone |[Self_reports] |
|If Pfizer believes we need a booster shot, we need it. Who knows their product better? Following the guidance of @CDCgov is how I wound up w/ Covid-19 and having to shut down my K-2 classroom for an entire week. I will do whatever it takes to protect my students, friends, family.|[Vaccine_chatter]|
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------+
Model Information
| Model Name: | bert_sequence_classifier_self_reported_vaccine_status_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 |