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* Add model 2024-01-01-ad_distilbert111_en

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---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2024-01-01-1030_1_en.md
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---
layout: model
title: English 1030_1 DistilBertForSequenceClassification from tingchih
author: John Snow Labs
name: 1030_1
date: 2024-01-01
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`1030_1` is a English model originally trained by tingchih.

{:.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/1030_1_en_5.2.2_3.0_1704117975641.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/1030_1_en_5.2.2_3.0_1704117975641.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")

sequenceClassifier = DistilBertForSequenceClassification.pretrained("1030_1","en")\
.setInputCols(["document","token"])\
.setOutputCol("class")

pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

```
```scala

val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val sequenceClassifier = DistilBertForSequenceClassification.pretrained("1030_1","en")
.setInputCols(Array("document","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)


```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|1030_1|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/tingchih/1030-1
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2024-01-01-17775_en.md
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---
layout: model
title: English 17775 DistilBertForSequenceClassification from joohwan
author: John Snow Labs
name: 17775
date: 2024-01-01
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`17775` is a English model originally trained by joohwan.

{:.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/17775_en_5.2.2_3.0_1704128045381.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/17775_en_5.2.2_3.0_1704128045381.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")

sequenceClassifier = DistilBertForSequenceClassification.pretrained("17775","en")\
.setInputCols(["document","token"])\
.setOutputCol("class")

pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

```
```scala

val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val sequenceClassifier = DistilBertForSequenceClassification.pretrained("17775","en")
.setInputCols(Array("document","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)


```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|17775|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/joohwan/17775
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2024-01-01-ad_distilbert111_en.md
Original file line number Diff line number Diff line change
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---
layout: model
title: English ad_distilbert111 DistilBertForSequenceClassification from tombellens
author: John Snow Labs
name: ad_distilbert111
date: 2024-01-01
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ad_distilbert111` is a English model originally trained by tombellens.

{:.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/ad_distilbert111_en_5.2.2_3.0_1704149830825.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ad_distilbert111_en_5.2.2_3.0_1704149830825.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")

sequenceClassifier = DistilBertForSequenceClassification.pretrained("ad_distilbert111","en")\
.setInputCols(["document","token"])\
.setOutputCol("class")

pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

```
```scala

val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val sequenceClassifier = DistilBertForSequenceClassification.pretrained("ad_distilbert111","en")
.setInputCols(Array("document","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)


```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|ad_distilbert111|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.4 MB|

## References

https://huggingface.co/tombellens/AD-distilBert111
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2024-01-01-ad_distilbert113_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English ad_distilbert113 DistilBertForSequenceClassification from tombellens
author: John Snow Labs
name: ad_distilbert113
date: 2024-01-01
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DistilBertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DistilBertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`ad_distilbert113` is a English model originally trained by tombellens.

{:.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/ad_distilbert113_en_5.2.2_3.0_1704151135997.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ad_distilbert113_en_5.2.2_3.0_1704151135997.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")

sequenceClassifier = DistilBertForSequenceClassification.pretrained("ad_distilbert113","en")\
.setInputCols(["document","token"])\
.setOutputCol("class")

pipeline = Pipeline().setStages([document_assembler, tokenizer, sequenceClassifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

```
```scala

val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("document")

val tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val sequenceClassifier = DistilBertForSequenceClassification.pretrained("ad_distilbert113","en")
.setInputCols(Array("document","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)


```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|ad_distilbert113|
|Compatibility:|Spark NLP 5.2.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.4 MB|

## References

https://huggingface.co/tombellens/AD-distilBert113
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