Description
This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using Roberta Base model.
RoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of RoBertaForZeroShotClassification models, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible.
We used TFRobertaForSequenceClassification to train this model and used RoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
Predicted Entities
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
zeroShotClassifier = RobertaForSequenceClassification \
.pretrained('roberta_base_zero_shot_classifier_nli', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setCaseSensitive(True) \
.setMaxSentenceLength(512) \
.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"])
pipeline = Pipeline(stages=[
document_assembler,
tokenizer,
zeroShotClassifier
])
example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text")
result = pipeline.fit(example).transform(example)
val document_assembler = DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")
val tokenizer = Tokenizer()
.setInputCols("document")
.setOutputCol("token")
val zeroShotClassifier = RobertaForSequenceClassification.pretrained("roberta_base_zero_shot_classifier_nli", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setCaseSensitive(true)
.setMaxSentenceLength(512)
.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"))
val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
val example = Seq("I have a problem with my iphone that needs to be resolved asap!!").toDS.toDF("text")
val result = pipeline.fit(example).transform(example)
Model Information
| Model Name: | roberta_base_zero_shot_classifier_nli |
| Compatibility: | Spark NLP 4.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [token, document] |
| Output Labels: | [multi_class] |
| Language: | en |
| Size: | 466.4 MB |
| Case sensitive: | true |