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2023-12-18-sentiment_analysis_distillbert_base_uncased_model_en (#14102)
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---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: English ag_news_classifier DistilBertForSequenceClassification from rajkumarrrk
author: John Snow Labs
name: ag_news_classifier
date: 2023-12-18
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.0
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.`ag_news_classifier` is a English model originally trained by rajkumarrrk.

{:.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/ag_news_classifier_en_5.2.0_3.0_1702941363076.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ag_news_classifier_en_5.2.0_3.0_1702941363076.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("ag_news_classifier","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("ag_news_classifier","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:|ag_news_classifier|
|Compatibility:|Spark NLP 5.2.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/rajkumarrrk/ag-news-classifier
97 changes: 97 additions & 0 deletions ...sts/ahmedlone127/2023-12-18-ai_day_distilbert_base_uncased_finetuned_cola_en.md
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---
layout: model
title: English ai_day_distilbert_base_uncased_finetuned_cola DistilBertForSequenceClassification from younes9
author: John Snow Labs
name: ai_day_distilbert_base_uncased_finetuned_cola
date: 2023-12-18
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.0
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.`ai_day_distilbert_base_uncased_finetuned_cola` is a English model originally trained by younes9.

{:.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/ai_day_distilbert_base_uncased_finetuned_cola_en_5.2.0_3.0_1702933763158.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/ai_day_distilbert_base_uncased_finetuned_cola_en_5.2.0_3.0_1702933763158.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("ai_day_distilbert_base_uncased_finetuned_cola","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("ai_day_distilbert_base_uncased_finetuned_cola","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:|ai_day_distilbert_base_uncased_finetuned_cola|
|Compatibility:|Spark NLP 5.2.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.4 MB|

## References

https://huggingface.co/younes9/AI-DAY-distilbert-base-uncased-finetuned-cola
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-12-18-airlinesentiment_en.md
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---
layout: model
title: English airlinesentiment DistilBertForSequenceClassification from PDatt
author: John Snow Labs
name: airlinesentiment
date: 2023-12-18
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.0
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.`airlinesentiment` is a English model originally trained by PDatt.

{:.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/airlinesentiment_en_5.2.0_3.0_1702928222574.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/airlinesentiment_en_5.2.0_3.0_1702928222574.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("airlinesentiment","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("airlinesentiment","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:|airlinesentiment|
|Compatibility:|Spark NLP 5.2.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/PDatt/airlinesentiment
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-12-18-amazon_review_sentiment_analysis2_en.md
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---
layout: model
title: English amazon_review_sentiment_analysis2 DistilBertForSequenceClassification from Christian2903
author: John Snow Labs
name: amazon_review_sentiment_analysis2
date: 2023-12-18
tags: [bert, en, open_source, sequence_classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.2.0
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.`amazon_review_sentiment_analysis2` is a English model originally trained by Christian2903.

{:.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/amazon_review_sentiment_analysis2_en_5.2.0_3.0_1702938441585.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/amazon_review_sentiment_analysis2_en_5.2.0_3.0_1702938441585.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("amazon_review_sentiment_analysis2","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("amazon_review_sentiment_analysis2","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:|amazon_review_sentiment_analysis2|
|Compatibility:|Spark NLP 5.2.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[documents, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|249.5 MB|

## References

https://huggingface.co/Christian2903/amazon-review-sentiment-analysis2
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