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2023-04-20-distilbert_base_uncased_mnli_en (#13761)
* Add model 2023-04-20-distilbert_base_uncased_mnli_en * Add model 2023-04-20-distilbert_base_turkish_cased_allnli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_snli_tr * Add model 2023-04-20-distilbert_base_turkish_cased_multinli_tr * Update and rename 2023-04-20-distilbert_base_turkish_cased_allnli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_multinli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update and rename 2023-04-20-distilbert_base_turkish_cased_snli_tr.md to 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update and rename 2023-04-20-distilbert_base_uncased_mnli_en.md to distilbert_base_zero_shot_classifier_turkish_cased_snli * Rename distilbert_base_zero_shot_classifier_turkish_cased_snli to distilbert_base_zero_shot_classifier_turkish_cased_snli_en.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md * Update 2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md --------- Co-authored-by: ahmedlone127 <[email protected]>
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‎docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr.md

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---
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layout: model
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title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_allnli
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author: John Snow Labs
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name: distilbert_base_zero_shot_classifier_turkish_cased_allnli
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date: 2023-04-20
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tags: [zero_shot, distilbert, base, tr, turkish, cased, open_source, tensorflow]
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task: Zero-Shot Classification
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language: tr
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edition: Spark NLP 4.4.1
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spark_version: [3.2, 3.0]
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supported: true
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engine: tensorflow
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annotator: DistilBertForZeroShotClassification
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article_header:
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type: cover
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use_language_switcher: "Python-Scala-Java"
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---
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## Description
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This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model.
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DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
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## Predicted Entities
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{:.btn-box}
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<button class="button button-orange" disabled>Live Demo</button>
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<button class="button button-orange" disabled>Open in Colab</button>
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[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_4.4.1_3.2_1681950583033.zip){:.button.button-orange}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_allnli_tr_4.4.1_3.2_1681950583033.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">
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{% include programmingLanguageSelectScalaPythonNLU.html %}
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```python
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document_assembler = DocumentAssembler() \
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.setInputCol('text') \
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.setOutputCol('document')
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tokenizer = Tokenizer() \
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.setInputCols(['document']) \
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.setOutputCol('token')
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zeroShotClassifier = DistilBertForZeroShotClassification \
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.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_allnli', 'en') \
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.setInputCols(['token', 'document']) \
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.setOutputCol('class') \
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.setCaseSensitive(True) \
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.setMaxSentenceLength(512) \
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.setCandidateLabels(["olumsuz", "olumlu"])
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pipeline = Pipeline(stages=[
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document_assembler,
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tokenizer,
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zeroShotClassifier
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])
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example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text")
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result = pipeline.fit(example).transform(example)
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```
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```scala
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val document_assembler = DocumentAssembler()
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.setInputCol("text")
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.setOutputCol("document")
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val tokenizer = Tokenizer()
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.setInputCols("document")
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.setOutputCol("token")
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val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_allnli", "en")
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.setInputCols("document", "token")
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.setOutputCol("class")
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.setCaseSensitive(true)
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.setMaxSentenceLength(512)
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.setCandidateLabels(Array("olumsuz", "olumlu"))
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val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
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val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text")
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val result = pipeline.fit(example).transform(example)
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```
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</div>
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{:.model-param}
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## Model Information
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{:.table-model}
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|---|---|
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|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_allnli|
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|Compatibility:|Spark NLP 4.4.1+|
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|License:|Open Source|
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|Edition:|Official|
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|Input Labels:|[token, document]|
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|Output Labels:|[multi_class]|
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|Language:|tr|
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|Size:|254.3 MB|
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|Case sensitive:|true|

‎docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr.md

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---
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layout: model
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title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_multinli
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author: John Snow Labs
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name: distilbert_base_zero_shot_classifier_turkish_cased_multinli
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date: 2023-04-20
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tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow]
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task: Zero-Shot Classification
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language: tr
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edition: Spark NLP 4.4.1
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spark_version: [3.2, 3.0]
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supported: true
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engine: tensorflow
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annotator: DistilBertForZeroShotClassification
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article_header:
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type: cover
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use_language_switcher: "Python-Scala-Java"
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---
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## Description
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This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model.
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DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
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## Predicted Entities
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{:.btn-box}
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<button class="button button-orange" disabled>Live Demo</button>
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<button class="button button-orange" disabled>Open in Colab</button>
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[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.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">
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{% include programmingLanguageSelectScalaPythonNLU.html %}
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```python
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document_assembler = DocumentAssembler() \
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.setInputCol('text') \
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.setOutputCol('document')
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tokenizer = Tokenizer() \
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.setInputCols(['document']) \
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.setOutputCol('token')
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zeroShotClassifier = DistilBertForZeroShotClassification \
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.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_multinli', 'en') \
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.setInputCols(['token', 'document']) \
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.setOutputCol('class') \
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.setCaseSensitive(True) \
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.setMaxSentenceLength(512) \
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.setCandidateLabels(["ekonomi", "siyaset","spor"])
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pipeline = Pipeline(stages=[
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document_assembler,
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tokenizer,
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zeroShotClassifier
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])
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example = spark.createDataFrame([['Dolar yükselmeye devam ediyor.']]).toDF("text")
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result = pipeline.fit(example).transform(example)
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```
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```scala
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val document_assembler = DocumentAssembler()
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.setInputCol("text")
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.setOutputCol("document")
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val tokenizer = Tokenizer()
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.setInputCols("document")
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.setOutputCol("token")
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val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_multinli", "en")
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.setInputCols("document", "token")
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.setOutputCol("class")
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.setCaseSensitive(true)
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.setMaxSentenceLength(512)
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.setCandidateLabels(Array("ekonomi", "siyaset","spor"))
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val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
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val example = Seq("Dolar yükselmeye devam ediyor.").toDS.toDF("text")
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val result = pipeline.fit(example).transform(example)
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```
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</div>
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{:.model-param}
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## Model Information
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{:.table-model}
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|---|---|
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|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_multinli|
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|Compatibility:|Spark NLP 4.4.1+|
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|License:|Open Source|
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|Edition:|Official|
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|Input Labels:|[token, document]|
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|Output Labels:|[multi_class]|
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|Language:|tr|
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|Size:|254.3 MB|
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|Case sensitive:|true|

‎docs/_posts/ahmedlone127/2023-04-20-distilbert_base_zero_shot_classifier_turkish_cased_snli_tr.md

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---
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layout: model
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title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_snli
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author: John Snow Labs
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name: distilbert_base_zero_shot_classifier_turkish_cased_snli
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date: 2023-04-20
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tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow]
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task: Zero-Shot Classification
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language: tr
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edition: Spark NLP 4.4.1
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spark_version: [3.2, 3.0]
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supported: true
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engine: tensorflow
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annotator: DistilBertForZeroShotClassification
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article_header:
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type: cover
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use_language_switcher: "Python-Scala-Java"
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---
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## Description
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This model is intended to be used for zero-shot text classification, especially in Trukish. It is fine-tuned on MNLI by using DistilBERT Base Uncased model.
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DistilBertForZeroShotClassification using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of DistilBertForSequenceClassification 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 TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
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## Predicted Entities
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{:.btn-box}
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<button class="button button-orange" disabled>Live Demo</button>
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<button class="button button-orange" disabled>Open in Colab</button>
35+
[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange}
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[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.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">
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{% include programmingLanguageSelectScalaPythonNLU.html %}
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```python
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document_assembler = DocumentAssembler() \
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.setInputCol('text') \
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.setOutputCol('document')
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tokenizer = Tokenizer() \
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.setInputCols(['document']) \
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.setOutputCol('token')
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zeroShotClassifier = DistilBertForZeroShotClassification \
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.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_snli', 'en') \
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.setInputCols(['token', 'document']) \
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.setOutputCol('class') \
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.setCaseSensitive(True) \
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.setMaxSentenceLength(512) \
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.setCandidateLabels(["olumsuz", "olumlu"])
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pipeline = Pipeline(stages=[
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document_assembler,
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tokenizer,
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zeroShotClassifier
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])
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example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text")
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result = pipeline.fit(example).transform(example)
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```
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```scala
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val document_assembler = DocumentAssembler()
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.setInputCol("text")
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.setOutputCol("document")
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val tokenizer = Tokenizer()
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.setInputCols("document")
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.setOutputCol("token")
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val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en")
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.setInputCols("document", "token")
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.setOutputCol("class")
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.setCaseSensitive(true)
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.setMaxSentenceLength(512)
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.setCandidateLabels(Array("olumsuz", "olumlu"))
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val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
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val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text")
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val result = pipeline.fit(example).transform(example)
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```
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</div>
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{:.model-param}
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## Model Information
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{:.table-model}
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|---|---|
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|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_snli|
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|Compatibility:|Spark NLP 4.4.1+|
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|License:|Open Source|
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|Edition:|Official|
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|Input Labels:|[token, document]|
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|Output Labels:|[multi_class]|
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|Language:|tr|
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|Size:|254.3 MB|
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|Case sensitive:|true|

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