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2024-09-05-sent_arbertv2_ar (#14394)
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Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: English deberta_v3_base_company_names DeBertaForTokenClassification from nbroad
author: John Snow Labs
name: deberta_v3_base_company_names
date: 2024-09-01
tags: [en, open_source, onnx, token_classification, deberta, ner]
task: Named Entity Recognition
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DeBertaForTokenClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DeBertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deberta_v3_base_company_names` is a English model originally trained by nbroad.

{:.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/deberta_v3_base_company_names_en_5.4.2_3.0_1725197551202.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_base_company_names_en_5.4.2_3.0_1725197551202.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')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

tokenClassifier = DeBertaForTokenClassification.pretrained("deberta_v3_base_company_names","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("ner")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, tokenClassifier])
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 tokenizer = new Tokenizer()
.setInputCols("document")
.setOutputCol("token")

val tokenClassifier = DeBertaForTokenClassification.pretrained("deberta_v3_base_company_names", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("ner")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, tokenClassifier))
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:|deberta_v3_base_company_names|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[ner]|
|Language:|en|
|Size:|661.8 MB|

## References

https://huggingface.co/nbroad/deberta-v3-base-company-names
Original file line number Diff line number Diff line change
@@ -0,0 +1,94 @@
---
layout: model
title: English deberta_v3_large__sst2__train_8_2 DeBertaForSequenceClassification from SetFit
author: John Snow Labs
name: deberta_v3_large__sst2__train_8_2
date: 2024-09-01
tags: [en, open_source, onnx, sequence_classification, deberta]
task: Text Classification
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: DeBertaForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DeBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`deberta_v3_large__sst2__train_8_2` is a English model originally trained by SetFit.

{:.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/deberta_v3_large__sst2__train_8_2_en_5.4.2_3.0_1725182599185.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/deberta_v3_large__sst2__train_8_2_en_5.4.2_3.0_1725182599185.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')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = DeBertaForSequenceClassification.pretrained("deberta_v3_large__sst2__train_8_2","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("class")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
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 tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val sequenceClassifier = DeBertaForSequenceClassification.pretrained("deberta_v3_large__sst2__train_8_2", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
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:|deberta_v3_large__sst2__train_8_2|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|1.5 GB|

## References

https://huggingface.co/SetFit/deberta-v3-large__sst2__train-8-2
94 changes: 94 additions & 0 deletions docs/_posts/ahmedlone127/2024-09-01-expe_1_en.md
Original file line number Diff line number Diff line change
@@ -0,0 +1,94 @@
---
layout: model
title: English expe_1 RoBertaForSequenceClassification from BaronSch
author: John Snow Labs
name: expe_1
date: 2024-09-01
tags: [en, open_source, onnx, sequence_classification, roberta]
task: Text Classification
language: en
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: RoBertaForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`expe_1` is a English model originally trained by BaronSch.

{:.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/expe_1_en_5.5.0_3.0_1725212357824.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/expe_1_en_5.5.0_3.0_1725212357824.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')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = RoBertaForSequenceClassification.pretrained("expe_1","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("class")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
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 tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val sequenceClassifier = RoBertaForSequenceClassification.pretrained("expe_1", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
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:|expe_1|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|468.5 MB|

## References

https://huggingface.co/BaronSch/Expe_1
Original file line number Diff line number Diff line change
@@ -0,0 +1,94 @@
---
layout: model
title: English imdb_microsoft_deberta_v3_large_seed_1 DeBertaForSequenceClassification from utahnlp
author: John Snow Labs
name: imdb_microsoft_deberta_v3_large_seed_1
date: 2024-09-01
tags: [en, open_source, onnx, sequence_classification, deberta]
task: Text Classification
language: en
edition: Spark NLP 5.5.0
spark_version: 3.0
supported: true
engine: onnx
annotator: DeBertaForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained DeBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`imdb_microsoft_deberta_v3_large_seed_1` is a English model originally trained by utahnlp.

{:.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/imdb_microsoft_deberta_v3_large_seed_1_en_5.5.0_3.0_1725209314777.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/imdb_microsoft_deberta_v3_large_seed_1_en_5.5.0_3.0_1725209314777.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')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = DeBertaForSequenceClassification.pretrained("imdb_microsoft_deberta_v3_large_seed_1","en") \
.setInputCols(["documents","token"]) \
.setOutputCol("class")

pipeline = Pipeline().setStages([documentAssembler, tokenizer, sequenceClassifier])
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 tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val sequenceClassifier = DeBertaForSequenceClassification.pretrained("imdb_microsoft_deberta_v3_large_seed_1", "en")
.setInputCols(Array("documents","token"))
.setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, sequenceClassifier))
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:|imdb_microsoft_deberta_v3_large_seed_1|
|Compatibility:|Spark NLP 5.5.0+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|1.6 GB|

## References

https://huggingface.co/utahnlp/imdb_microsoft_deberta-v3-large_seed-1
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