Description
“
DeBERTa v3 model with sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for multi-class document classification tasks.
deberta_v3_base_sequence_classifier_imdb is a fine-tuned DeBERTa model that is ready to be used for Sequence Classification tasks such as sentiment analysis or multi-class text classification and it achieves state-of-the-art performance.
We used TFDebertaV2ForSequenceClassification to train this model and used DeBertaForSequenceClassification annotator in Spark NLP 🚀 for prediction at scale!
How to use
document_assembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
tokenizer = Tokenizer()\
.setInputCols(['document'])\
.setOutputCol('token')
sequenceClassifier = DeBertaForSequenceClassification.pretrained("deberta_classification_base_prompt", "en")\
.setInputCols(["document", "token"])\
.setOutputCol("class")\
.setCaseSensitive(True)\
.setMaxSentenceLength(512)
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
sequenceClassifier
])
example = spark.createDataFrame([['I really liked that movie!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val sequenceClassifier = DeBertaForSequenceClassification.pretrained("deberta_classification_base_prompt", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))
val example = Seq("I really liked that movie!").toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
Model Information
| Model Name: | deberta_classification_base_prompt |
| Compatibility: | Spark NLP 5.5.0+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [label] |
| Language: | en |
| Size: | 710.8 MB |
| Case sensitive: | true |