2023-04-20-distilbert_base_uncased_mnli_en

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

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+---
+layout: model
+title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_allnli
+author: John Snow Labs
+name: distilbert_base_zero_shot_classifier_turkish_cased_allnli
+date: 2023-04-20
+tags: [zero_shot, distilbert, base, tr, turkish, cased, open_source, tensorflow]
+task: Zero-Shot Classification
+language: tr
+edition: Spark NLP 4.4.1
+spark_version: [3.2, 3.0]
+supported: true
+engine: tensorflow
+annotator: DistilBertForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+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.
+
+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.
+
+We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+
+
+{:.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/distilbert_base_zero_shot_classifier_turkish_cased_allnli_4.4.1_3.2_1681950583033.zip){:.button.button-orange}
+[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}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+.setInputCol('text') \
+.setOutputCol('document')
+
+tokenizer = Tokenizer() \
+.setInputCols(['document']) \
+.setOutputCol('token')
+
+zeroShotClassifier = DistilBertForZeroShotClassification \
+.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_allnli', 'en') \
+.setInputCols(['token', 'document']) \
+.setOutputCol('class') \
+.setCaseSensitive(True) \
+.setMaxSentenceLength(512) \
+.setCandidateLabels(["olumsuz", "olumlu"])
+
+pipeline = Pipeline(stages=[
+document_assembler,
+tokenizer,
+zeroShotClassifier
+])
+
+example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+```
+```scala
+val document_assembler = DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("document")
+
+val tokenizer = Tokenizer()
+.setInputCols("document")
+.setOutputCol("token")
+
+val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_allnli", "en")
+.setInputCols("document", "token")
+.setOutputCol("class")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+.setCandidateLabels(Array("olumsuz", "olumlu"))
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
+
+val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text")
+
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_allnli|
+|Compatibility:|Spark NLP 4.4.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[multi_class]|
+|Language:|tr|
+|Size:|254.3 MB|
+|Case sensitive:|true|

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

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+---
+layout: model
+title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_multinli
+author: John Snow Labs
+name: distilbert_base_zero_shot_classifier_turkish_cased_multinli
+date: 2023-04-20
+tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow]
+task: Zero-Shot Classification
+language: tr
+edition: Spark NLP 4.4.1
+spark_version: [3.2, 3.0]
+supported: true
+engine: tensorflow
+annotator: DistilBertForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+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.
+
+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.
+
+We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+
+
+{:.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/distilbert_base_zero_shot_classifier_turkish_cased_multinli_tr_4.4.1_3.2_1681952299918.zip){:.button.button-orange}
+[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}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+.setInputCol('text') \
+.setOutputCol('document')
+
+tokenizer = Tokenizer() \
+.setInputCols(['document']) \
+.setOutputCol('token')
+
+zeroShotClassifier = DistilBertForZeroShotClassification \
+.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_multinli', 'en') \
+.setInputCols(['token', 'document']) \
+.setOutputCol('class') \
+.setCaseSensitive(True) \
+.setMaxSentenceLength(512) \
+.setCandidateLabels(["ekonomi", "siyaset","spor"])
+
+pipeline = Pipeline(stages=[
+document_assembler,
+tokenizer,
+zeroShotClassifier
+])
+
+example = spark.createDataFrame([['Dolar yükselmeye devam ediyor.']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+
+```
+```scala
+val document_assembler = DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("document")
+
+val tokenizer = Tokenizer()
+.setInputCols("document")
+.setOutputCol("token")
+
+val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_multinli", "en")
+.setInputCols("document", "token")
+.setOutputCol("class")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+.setCandidateLabels(Array("ekonomi", "siyaset","spor"))
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
+
+val example = Seq("Dolar yükselmeye devam ediyor.").toDS.toDF("text")
+
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_multinli|
+|Compatibility:|Spark NLP 4.4.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[multi_class]|
+|Language:|tr|
+|Size:|254.3 MB|
+|Case sensitive:|true|

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

@@ -0,0 +1,107 @@
+---
+layout: model
+title: DistilBERTZero-Shot Classification Base - distilbert_base_zero_shot_classifier_turkish_cased_snli
+author: John Snow Labs
+name: distilbert_base_zero_shot_classifier_turkish_cased_snli
+date: 2023-04-20
+tags: [zero_shot, tr, turkish, distilbert, base, cased, open_source, tensorflow]
+task: Zero-Shot Classification
+language: tr
+edition: Spark NLP 4.4.1
+spark_version: [3.2, 3.0]
+supported: true
+engine: tensorflow
+annotator: DistilBertForZeroShotClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+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.
+
+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.
+
+We used TFDistilBertForSequenceClassification to train this model and used DistilBertForZeroShotClassification annotator in Spark NLP 🚀 for prediction at scale!
+
+## Predicted Entities
+
+
+
+{:.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/distilbert_base_zero_shot_classifier_turkish_cased_snli_tr_4.4.1_3.2_1681951486863.zip){:.button.button-orange}
+[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}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+document_assembler = DocumentAssembler() \
+.setInputCol('text') \
+.setOutputCol('document')
+
+tokenizer = Tokenizer() \
+.setInputCols(['document']) \
+.setOutputCol('token')
+
+zeroShotClassifier = DistilBertForZeroShotClassification \
+.pretrained('distilbert_base_zero_shot_classifier_turkish_cased_snli', 'en') \
+.setInputCols(['token', 'document']) \
+.setOutputCol('class') \
+.setCaseSensitive(True) \
+.setMaxSentenceLength(512) \
+.setCandidateLabels(["olumsuz", "olumlu"])
+
+pipeline = Pipeline(stages=[
+document_assembler,
+tokenizer,
+zeroShotClassifier
+])
+
+example = spark.createDataFrame([['Senaryo çok saçmaydı, beğendim diyemem.']]).toDF("text")
+result = pipeline.fit(example).transform(example)
+```
+```scala
+val document_assembler = DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("document")
+
+val tokenizer = Tokenizer()
+.setInputCols("document")
+.setOutputCol("token")
+
+val zeroShotClassifier = DistilBertForZeroShotClassification.pretrained("distilbert_base_zero_shot_classifier_turkish_cased_snli", "en")
+.setInputCols("document", "token")
+.setOutputCol("class")
+.setCaseSensitive(true)
+.setMaxSentenceLength(512)
+.setCandidateLabels(Array("olumsuz", "olumlu"))
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, zeroShotClassifier))
+
+val example = Seq("Senaryo çok saçmaydı, beğendim diyemem.").toDS.toDF("text")
+
+val result = pipeline.fit(example).transform(example)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbert_base_zero_shot_classifier_turkish_cased_snli|
+|Compatibility:|Spark NLP 4.4.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[token, document]|
+|Output Labels:|[multi_class]|
+|Language:|tr|
+|Size:|254.3 MB|
+|Case sensitive:|true|