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  • John Snow Labs
  • Aug 07, 2023

Bart Zero Shot Classifier Large -MNLI (bart_large_zero_shot_classifier_mnli)

  • bart
  • zero_shot
  • en
  • open_source
  • tensorflow

Description

This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on MNLI by using large BART model.

BartForZeroShotClassification using a ModelForSequenceClassification trained on MNLI tasks. Equivalent of BartForSequenceClassification 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 TFBartForSequenceClassification to train this model and used BartForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!

Predicted Entities

Download Copy S3 URI

How to use

document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

zeroShotClassifier = BartForZeroShotClassification \
.pretrained('bart_large_zero_shot_classifier_mnli', '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 = BartForSequenceClassification.pretrained("bart_large_zero_shot_classifier_mnli", "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: bart_large_zero_shot_classifier_mnli
Compatibility: Spark NLP 5.1.0+
License: Open Source
Edition: Official
Input Labels: [token, document]
Output Labels: [label]
Language: en
Size: 467.1 MB
Case sensitive: true