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--- | ||
layout: model | ||
title: XlmRoBertaZero-Shot Classification Large xlm_roberta_large_zero_shot_classifier_xnli_anli | ||
author: John Snow Labs | ||
name: xlm_roberta_large_zero_shot_classifier_xnli_anli | ||
date: 2023-07-20 | ||
tags: [zero_shot, xx, open_source, tensorflow] | ||
task: Zero-Shot Classification | ||
language: xx | ||
edition: Spark NLP 5.0.2 | ||
spark_version: 3.0 | ||
supported: true | ||
engine: tensorflow | ||
annotator: XlmRoBertaForZeroShotClassification | ||
article_header: | ||
type: cover | ||
use_language_switcher: "Python-Scala-Java" | ||
--- | ||
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## Description | ||
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This model is intended to be used for zero-shot text classification, especially in English. It is fine-tuned on NLI by using XlmRoberta Large model. | ||
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XlmRoBertaForZeroShotClassificationusing a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of TFXLMRoBertaForZeroShotClassification 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. | ||
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We used TFXLMRobertaForSequenceClassification to train this model and used XlmRoBertaForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale! | ||
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## Predicted Entities | ||
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{:.btn-box} | ||
<button class="button button-orange" disabled>Live Demo</button> | ||
<button class="button button-orange" disabled>Open in Colab</button> | ||
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.arr.button-icon} | ||
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/xlm_roberta_large_zero_shot_classifier_xnli_anli_xx_5.0.2_3.0_1689886974932.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3} | ||
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## How to use | ||
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<div class="tabs-box" markdown="1"> | ||
{% include programmingLanguageSelectScalaPythonNLU.html %} | ||
```python | ||
document_assembler = DocumentAssembler() \ | ||
.setInputCol('text') \ | ||
.setOutputCol('document') | ||
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tokenizer = Tokenizer() \ | ||
.setInputCols(['document']) \ | ||
.setOutputCol('token') | ||
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zeroShotClassifier = XlmRobertaForSequenceClassification \ | ||
.pretrained('xlm_roberta_large_zero_shot_classifier_xnli_anli', 'xx') \ | ||
.setInputCols(['token', 'document']) \ | ||
.setOutputCol('class') \ | ||
.setCaseSensitive(True) \ | ||
.setMaxSentenceLength(512) \ | ||
.setCandidateLabels(["urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology"]) | ||
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pipeline = Pipeline(stages=[ | ||
document_assembler, | ||
tokenizer, | ||
zeroShotClassifier | ||
]) | ||
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example = spark.createDataFrame([['I have a problem with my iphone that needs to be resolved asap!!']]).toDF("text") | ||
result = pipeline.fit(example).transform(example) | ||
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``` | ||
```scala | ||
val document_assembler = DocumentAssembler() | ||
.setInputCol("text") | ||
.setOutputCol("document") | ||
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val tokenizer = Tokenizer() | ||
.setInputCols("document") | ||
.setOutputCol("token") | ||
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val zeroShotClassifier = XlmRobertaForSequenceClassification.pretrained("xlm_roberta_large_zero_shot_classifier_xnli_anli", "xx") | ||
.setInputCols("document", "token") | ||
.setOutputCol("class") | ||
.setCaseSensitive(true) | ||
.setMaxSentenceLength(512) | ||
.setCandidateLabels(Array("urgent", "mobile", "travel", "movie", "music", "sport", "weather", "technology")) | ||
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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) | ||
``` | ||
</div> | ||
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{:.model-param} | ||
## Model Information | ||
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{:.table-model} | ||
|---|---| | ||
|Model Name:|xlm_roberta_large_zero_shot_classifier_xnli_anli| | ||
|Compatibility:|Spark NLP 5.0.2+| | ||
|License:|Open Source| | ||
|Edition:|Official| | ||
|Input Labels:|[token, document]| | ||
|Output Labels:|[label]| | ||
|Language:|xx| | ||
|Size:|2.0 GB| | ||
|Case sensitive:|true| |