Description
Automatically identify messages as being regular messages or Spam.
Predicted Entities
spam, ham
Live Demo
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How to use
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained(lang="en") \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
document_classifier = ClassifierDLModel.pretrained('classifierdl_use_spam', 'en') \
.setInputCols(["document", "sentence_embeddings"]) \
.setOutputCol("class")
nlpPipeline = Pipeline(stages=[documentAssembler, use, document_classifier])
light_pipeline = LightPipeline(nlp_pipeline.fit(spark.createDataFrame([['']]).toDF("text")))
annotations = light_pipeline.fullAnnotate('Congratulations! You've won a $1,000 Walmart gift card. Go to http://bit.ly/1234 to claim now.')
val documentAssembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val use = UniversalSentenceEncoder.pretrained(lang="en")
.setInputCols(Array("document"))
.setOutputCol("sentence_embeddings")
val document_classifier = ClassifierDLModel.pretrained('classifierdl_use_spam', 'en')
.setInputCols(Array("document", "sentence_embeddings"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, use, document_classifier))
val data = Seq("Congratulations! You've won a $1,000 Walmart gift card. Go to http://bit.ly/1234 to claim now.").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""Congratulations! You've won a $1,000 Walmart gift card. Go to http://bit.ly/1234 to claim now."""]
spam_df = nlu.load('classify.spam.use').predict(text, output_level='document')
spam_df[["document", "spam"]]
Results
+------------------------------------------------------------------------------------------------+------------+
|document |class |
+------------------------------------------------------------------------------------------------+------------+
|Congratulations! You've won a $1,000 Walmart gift card. Go to http://bit.ly/1234 to claim now. | spam |
+------------------------------------------------------------------------------------------------+------------+
Model Information
| Model Name | classifierdl_use_spam |
| Model Class | ClassifierDLModel |
| Spark Compatibility | 2.5.3 |
| Spark NLP Compatibility | 2.4 |
| License | open source |
| Edition | public |
| Input Labels | [document, sentence_embeddings] |
| Output Labels | [class] |
| Language | en |
| Upstream Dependencies | tfhub_use |
Data Source
This model is trained on UCI spam dataset. https://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip
Benchmarking
Accuracy of the model with USE Embeddings is 0.86
precision recall f1-score support
ham 0.86 1.00 0.92 1440
spam 0.00 0.00 0.00 238
accuracy 0.86 1678
macro avg 0.43 0.50 0.46 1678
weighted avg 0.74 0.86 0.79 1678