Description
Classify if a text contains sarcasm.
Predicted Entities
normal, sarcasm
Live Demo Open in Colab Download Copy S3 URI
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_sarcasm', '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('If I could put into words how much I love waking up at am on Tuesdays I would')
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_sarcasm", "en")
.setInputCols(Array("document", "sentence_embeddings"))
.setOutputCol("class")
val pipeline = new Pipeline().setStages(Array(documentAssembler, use, document_classifier))
val data = Seq("If I could put into words how much I love waking up at am on Tuesdays I would").toDF("text")
val result = pipeline.fit(data).transform(data)
import nlu
text = ["""If I could put into words how much I love waking up at am on Tuesdays I would"""]
sarcasm_df = nlu.load('classify.sarcasm.use').predict(text, output_level='document')
sarcasm_df[["document", "sarcasm"]]
Results
+--------------------------------------------------------------------------------------------------------+------------+
|document |class |
+--------------------------------------------------------------------------------------------------------+------------+
|If I could put into words how much I love waking up at am on Tuesdays I would | sarcasm |
+--------------------------------------------------------------------------------------------------------+------------+
Model Information
| Model Name: | classifierdl_use_sarcasm |
| 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
http://www.cs.utah.edu/~riloff/pdfs/official-emnlp13-sarcasm.pdf
Benchmarking
precision recall f1-score support
normal 0.98 0.89 0.93 495
sarcasm 0.60 0.91 0.73 93
accuracy 0.89 588
macro avg 0.79 0.90 0.83 588
weighted avg 0.92 0.89 0.90 588
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