Description
This model is a BioBERT based sentimental analysis model that can extract information from COVID-19 Vaccine-related tweets. The model predicts whether a tweet contains positive, negative, or neutral sentiments about COVID-19 Vaccines.
Predicted Entities
neutral, positive, negative
How to use
document_assembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols(["document"]) \
.setOutputCol("token")
sequence_classifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_vaccine_sentiment_onnx", "en", "clinical/models")\
.setInputCols(["document", "token"])\
.setOutputCol("class")
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequence_classifier
])
text_list = ['A little bright light for an otherwise dark week. Thanks researchers, and frontline workers. Onwards.',
'People with a history of severe allergic reaction to any component of the vaccine should not take.',
'43 million doses of vaccines administrated worldwide...Production capacity of CHINA to reach 4 b']
data = spark.createDataFrame(text_list, 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_vaccine_sentiment_onnx", "en", "clinical/models")\
.setInputCols(["document","token"])\
.setOutputCol("classes")
pipeline = nlp.Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
text_list = ['A little bright light for an otherwise dark week. Thanks researchers, and frontline workers. Onwards.',
'People with a history of severe allergic reaction to any component of the vaccine should not take.',
'43 million doses of vaccines administrated worldwide...Production capacity of CHINA to reach 4 b']
data = spark.createDataFrame(text_list, 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_vaccine_sentiment_onnx", "en", "clinical/models")
.setInputCols(Array("document","token"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val data = Seq(Array("A little bright light for an otherwise dark week. Thanks researchers, and frontline workers. Onwards.",
"People with a history of severe allergic reaction to any component of the vaccine should not take.",
"43 million doses of vaccines administrated worldwide...Production capacity of CHINA to reach 4 b")).toDF("text")
val model = pipeline.fit(data)
val result = model.transform(data)
Results
+-----------------------------------------------------------------------------------------------------+----------+
|text |class |
+-----------------------------------------------------------------------------------------------------+----------+
|A little bright light for an otherwise dark week. Thanks researchers, and frontline workers. Onwards.|[positive]|
|People with a history of severe allergic reaction to any component of the vaccine should not take. |[negative]|
|43 million doses of vaccines administrated worldwide...Production capacity of CHINA to reach 4 b |[neutral] |
+-----------------------------------------------------------------------------------------------------+----------+
Model Information
| Model Name: | bert_sequence_classifier_vaccine_sentiment_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 |