Description
Classify if a text contains sarcasm.
Predicted Entities
normal, sarcasm
Live Demo Open in Colab Download
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')
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