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* Add model 2023-11-19-distilbert_base_uncased_finetuned_amazon_fine_food_lite_en

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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: English 03_model_sales DistilBertForSequenceClassification from hannoh
author: John Snow Labs
name: 03_model_sales
date: 2023-11-18
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.`03_model_sales` is a English model originally trained by hannoh.

{:.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/03_model_sales_en_5.2.0_3.0_1700339455179.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/03_model_sales_en_5.2.0_3.0_1700339455179.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("03_model_sales","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("03_model_sales","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:|03_model_sales|
|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/hannoh/03_model_sales
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-11-18-1023_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English 1023 DistilBertForSequenceClassification from tingchih
author: John Snow Labs
name: 1023
date: 2023-11-18
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.`1023` 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/1023_en_5.2.0_3.0_1700345642807.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/1023_en_5.2.0_3.0_1700345642807.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("1023","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("1023","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:|1023|
|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/tingchih/1023
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-11-18-action_decisions_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English action_decisions DistilBertForSequenceClassification from knkarthick
author: John Snow Labs
name: action_decisions
date: 2023-11-18
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.`action_decisions` is a English model originally trained by knkarthick.

{:.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/action_decisions_en_5.2.0_3.0_1700347033471.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/action_decisions_en_5.2.0_3.0_1700347033471.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("action_decisions","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("action_decisions","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:|action_decisions|
|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/knkarthick/Action_Decisions
97 changes: 97 additions & 0 deletions docs/_posts/ahmedlone127/2023-11-18-action_items_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,97 @@
---
layout: model
title: English action_items DistilBertForSequenceClassification from knkarthick
author: John Snow Labs
name: action_items
date: 2023-11-18
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.`action_items` is a English model originally trained by knkarthick.

{:.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/action_items_en_5.2.0_3.0_1700341844870.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/action_items_en_5.2.0_3.0_1700341844870.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("action_items","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("action_items","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:|action_items|
|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/knkarthick/Action_Items
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