Description
Automatically identify messages as being regular messages or Spam.
Predicted Entities
spam, ham
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
use = UniversalSentenceEncoder.pretrained('tfhub_use', lang="en") \
.setInputCols(["document"])\
.setOutputCol("sentence_embeddings")
document_classifier = ClassifierDLModel.pretrained('classifierdl_use_spam', 'en') \
.setInputCols(["document", "sentence_embeddings"]) \
.setOutputCol("class")
nlpPipeline = Pipeline(stages=[document_assembler, 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.')
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 |
| Compatibility: | Spark NLP 2.7.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [sentence_embeddings] |
| Output Labels: | [class] |
| Language: | en |
| 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
precision recall f1-score support
ham 0.99 0.99 0.99 966
spam 0.95 0.95 0.95 149
accuracy 0.99 1115
macro avg 0.97 0.97 0.97 1115
weighted avg 0.99 0.99 0.99 1115