Description
Pretrained DeBertaForZeroShotClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.deberta_base_zero_shot_classifier_mnli_anli_v3 is a English model originally trained by MoritzLaurer.
Predicted Entities
How to use
document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')
tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')
zeroShotClassifier = DeBertaForZeroShotClassification \
.pretrained('deberta_base_zero_shot_classifier_mnli_anli_v3', '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 = DeBertaForZeroShotClassification.pretrained("deberta_base_zero_shot_classifier_mnli_anli_v3", "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: | deberta_base_zero_shot_classifier_mnli_anli_v3 |
| Compatibility: | Spark NLP 5.5.1+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [class] |
| Language: | en |
| Size: | 439.7 MB |
References
References
References
https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli