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2024-08-16-deberta_base_zero_shot_classifier_mnli_anli_v3_en (#14369)
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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: French afrimt5_bbj_french_news T5Transformer from masakhane
author: John Snow Labs
name: afrimt5_bbj_french_news
date: 2024-08-16
tags: [fr, open_source, onnx, t5, question_answering, summarization, translation, text_generation]
task: [Question Answering, Summarization, Translation, Text Generation]
language: fr
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: T5Transformer
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained T5Transformer model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`afrimt5_bbj_french_news` is a French model originally trained by masakhane.

{:.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/afrimt5_bbj_french_news_fr_5.4.2_3.0_1723843978763.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/afrimt5_bbj_french_news_fr_5.4.2_3.0_1723843978763.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

documentAssembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

t5 = T5Transformer.pretrained("afrimt5_bbj_french_news","fr") \
.setInputCols(["document"]) \
.setOutputCol("output")

pipeline = Pipeline().setStages([documentAssembler, t5])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")

val t5 = T5Transformer.pretrained("afrimt5_bbj_french_news", "fr")
.setInputCols(Array("documents"))
.setOutputCol("output")

val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
val data = Seq("I love spark-nlp").toDS.toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|afrimt5_bbj_french_news|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document]|
|Output Labels:|[output]|
|Language:|fr|
|Size:|2.8 GB|

## References

https://huggingface.co/masakhane/afrimt5_bbj_fr_news
Original file line number Diff line number Diff line change
@@ -0,0 +1,69 @@
---
layout: model
title: French afrimt5_bbj_french_news_pipeline pipeline T5Transformer from masakhane
author: John Snow Labs
name: afrimt5_bbj_french_news_pipeline
date: 2024-08-16
tags: [fr, open_source, pipeline, onnx]
task: [Question Answering, Summarization, Translation, Text Generation]
language: fr
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained T5Transformer, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`afrimt5_bbj_french_news_pipeline` is a French model originally trained by masakhane.

{:.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/afrimt5_bbj_french_news_pipeline_fr_5.4.2_3.0_1723844140147.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/afrimt5_bbj_french_news_pipeline_fr_5.4.2_3.0_1723844140147.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

pipeline = PretrainedPipeline("afrimt5_bbj_french_news_pipeline", lang = "fr")
annotations = pipeline.transform(df)

```
```scala

val pipeline = new PretrainedPipeline("afrimt5_bbj_french_news_pipeline", lang = "fr")
val annotations = pipeline.transform(df)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|afrimt5_bbj_french_news_pipeline|
|Type:|pipeline|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Language:|fr|
|Size:|2.8 GB|

## References

https://huggingface.co/masakhane/afrimt5_bbj_fr_news

## Included Models

- DocumentAssembler
- T5Transformer
86 changes: 86 additions & 0 deletions docs/_posts/ahmedlone127/2024-08-16-amhat5_small_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,86 @@
---
layout: model
title: English amhat5_small T5Transformer from Samuael
author: John Snow Labs
name: amhat5_small
date: 2024-08-16
tags: [en, open_source, onnx, t5, question_answering, summarization, translation, text_generation]
task: [Question Answering, Summarization, Translation, Text Generation]
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: T5Transformer
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained T5Transformer model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`amhat5_small` is a English model originally trained by Samuael.

{:.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/amhat5_small_en_5.4.2_3.0_1723832153687.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/amhat5_small_en_5.4.2_3.0_1723832153687.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

documentAssembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

t5 = T5Transformer.pretrained("amhat5_small","en") \
.setInputCols(["document"]) \
.setOutputCol("output")

pipeline = Pipeline().setStages([documentAssembler, t5])
data = spark.createDataFrame([["I love spark-nlp"]]).toDF("text")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new DocumentAssembler()
.setInputCols("text")
.setOutputCols("document")

val t5 = T5Transformer.pretrained("amhat5_small", "en")
.setInputCols(Array("documents"))
.setOutputCol("output")

val pipeline = new Pipeline().setStages(Array(documentAssembler, t5))
val data = Seq("I love spark-nlp").toDS.toDF("text")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|amhat5_small|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document]|
|Output Labels:|[output]|
|Language:|en|
|Size:|88.2 MB|

## References

https://huggingface.co/Samuael/amhat5-small
69 changes: 69 additions & 0 deletions docs/_posts/ahmedlone127/2024-08-16-amhat5_small_pipeline_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,69 @@
---
layout: model
title: English amhat5_small_pipeline pipeline T5Transformer from Samuael
author: John Snow Labs
name: amhat5_small_pipeline
date: 2024-08-16
tags: [en, open_source, pipeline, onnx]
task: [Question Answering, Summarization, Translation, Text Generation]
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
annotator: PipelineModel
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained T5Transformer, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`amhat5_small_pipeline` is a English model originally trained by Samuael.

{:.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/amhat5_small_pipeline_en_5.4.2_3.0_1723832184882.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/amhat5_small_pipeline_en_5.4.2_3.0_1723832184882.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

pipeline = PretrainedPipeline("amhat5_small_pipeline", lang = "en")
annotations = pipeline.transform(df)

```
```scala

val pipeline = new PretrainedPipeline("amhat5_small_pipeline", lang = "en")
val annotations = pipeline.transform(df)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|amhat5_small_pipeline|
|Type:|pipeline|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Language:|en|
|Size:|88.2 MB|

## References

https://huggingface.co/Samuael/amhat5-small

## Included Models

- DocumentAssembler
- T5Transformer
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