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2024-09-03-xlmroberta_ner_base_indonesian_pipeline_id (#14391)
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---------

Co-authored-by: ahmedlone127 <ahmedlone127@gmail.com>
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---
layout: model
title: English albert_base_finetuned_recipe_modified AlbertForQuestionAnswering from saumyasinha0510
author: John Snow Labs
name: albert_base_finetuned_recipe_modified
date: 2024-09-01
tags: [en, open_source, onnx, question_answering, albert]
task: Question Answering
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: AlbertForQuestionAnswering
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_finetuned_recipe_modified` is a English model originally trained by saumyasinha0510.

{:.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/albert_base_finetuned_recipe_modified_en_5.4.2_3.0_1725193374690.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_finetuned_recipe_modified_en_5.4.2_3.0_1725193374690.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 = MultiDocumentAssembler() \
.setInputCol(["question", "context"]) \
.setOutputCol(["document_question", "document_context"])

spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_finetuned_recipe_modified","en") \
.setInputCols(["document_question","document_context"]) \
.setOutputCol("answer")

pipeline = Pipeline().setStages([documentAssembler, spanClassifier])
data = spark.createDataFrame([["What framework do I use?","I use spark-nlp."]]).toDF("document_question", "document_context")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new MultiDocumentAssembler()
.setInputCol(Array("question", "context"))
.setOutputCol(Array("document_question", "document_context"))

val spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_finetuned_recipe_modified", "en")
.setInputCols(Array("document_question","document_context"))
.setOutputCol("answer")

val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
val data = Seq("What framework do I use?","I use spark-nlp.").toDS.toDF("document_question", "document_context")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|albert_base_finetuned_recipe_modified|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document_question, document_context]|
|Output Labels:|[answer]|
|Language:|en|
|Size:|42.0 MB|

## References

https://huggingface.co/saumyasinha0510/Albert-base-finetuned-recipe-modified
86 changes: 86 additions & 0 deletions docs/_posts/ahmedlone127/2024-09-01-albert_base_qa_2_k_fold_1_en.md
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---
layout: model
title: English albert_base_qa_2_k_fold_1 AlbertForQuestionAnswering from mateiaass
author: John Snow Labs
name: albert_base_qa_2_k_fold_1
date: 2024-09-01
tags: [en, open_source, onnx, question_answering, albert]
task: Question Answering
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: AlbertForQuestionAnswering
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained AlbertForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`albert_base_qa_2_k_fold_1` is a English model originally trained by mateiaass.

{:.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/albert_base_qa_2_k_fold_1_en_5.4.2_3.0_1725193498781.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_base_qa_2_k_fold_1_en_5.4.2_3.0_1725193498781.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 = MultiDocumentAssembler() \
.setInputCol(["question", "context"]) \
.setOutputCol(["document_question", "document_context"])

spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_qa_2_k_fold_1","en") \
.setInputCols(["document_question","document_context"]) \
.setOutputCol("answer")

pipeline = Pipeline().setStages([documentAssembler, spanClassifier])
data = spark.createDataFrame([["What framework do I use?","I use spark-nlp."]]).toDF("document_question", "document_context")
pipelineModel = pipeline.fit(data)
pipelineDF = pipelineModel.transform(data)

```
```scala

val documentAssembler = new MultiDocumentAssembler()
.setInputCol(Array("question", "context"))
.setOutputCol(Array("document_question", "document_context"))

val spanClassifier = AlbertForQuestionAnswering.pretrained("albert_base_qa_2_k_fold_1", "en")
.setInputCols(Array("document_question","document_context"))
.setOutputCol("answer")

val pipeline = new Pipeline().setStages(Array(documentAssembler, spanClassifier))
val data = Seq("What framework do I use?","I use spark-nlp.").toDS.toDF("document_question", "document_context")
val pipelineModel = pipeline.fit(data)
val pipelineDF = pipelineModel.transform(data)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|albert_base_qa_2_k_fold_1|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document_question, document_context]|
|Output Labels:|[answer]|
|Language:|en|
|Size:|42.0 MB|

## References

https://huggingface.co/mateiaass/albert-base-qa-2-k-fold-1
104 changes: 104 additions & 0 deletions .../ahmedlone127/2024-09-01-bert_classifier_autonlp_entity_selection_5771228_en.md
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---
layout: model
title: English BertForSequenceClassification Cased model (from kamivao)
author: John Snow Labs
name: bert_classifier_autonlp_entity_selection_5771228
date: 2024-09-01
tags: [en, open_source, bert, sequence_classification, classification, onnx]
task: Text Classification
language: en
edition: Spark NLP 5.4.2
spark_version: 3.0
supported: true
engine: onnx
annotator: BertForSequenceClassification
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. `autonlp-entity_selection-5771228` is a English model originally trained by `kamivao`.

## Predicted Entities

`1`, `0`

{:.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/bert_classifier_autonlp_entity_selection_5771228_en_5.4.2_3.0_1725204813533.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_classifier_autonlp_entity_selection_5771228_en_5.4.2_3.0_1725204813533.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")

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_autonlp_entity_selection_5771228","en") \
.setInputCols(["document", "token"]) \
.setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])

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

result = pipeline.fit(data).transform(data)
```
```scala
val documentAssembler = new DocumentAssembler()
.setInputCols(Array("text"))
.setOutputCols(Array("document"))

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

val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_autonlp_entity_selection_5771228","en")
.setInputCols(Array("document", "token"))
.setOutputCol("class")

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

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

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

{:.nlu-block}
```python
import nlu
nlu.load("en.classify.bert.by_kamivao").predict("""PUT YOUR STRING HERE""")
```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|bert_classifier_autonlp_entity_selection_5771228|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document, token]|
|Output Labels:|[class]|
|Language:|en|
|Size:|409.4 MB|

## References

References

- https://huggingface.co/kamivao/autonlp-entity_selection-5771228
69 changes: 69 additions & 0 deletions ...ert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline_en.md
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---
layout: model
title: English bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline pipeline BertForQuestionAnswering from anas-awadalla
author: John Snow Labs
name: bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline
date: 2024-09-01
tags: [en, open_source, pipeline, onnx]
task: Question Answering
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 BertForQuestionAnswering, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline` is a English model originally trained by anas-awadalla.

{:.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/bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline_en_5.4.2_3.0_1725185371351.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline_en_5.4.2_3.0_1725185371351.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("bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline", lang = "en")
annotations = pipeline.transform(df)

```
```scala

val pipeline = new PretrainedPipeline("bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline", lang = "en")
val annotations = pipeline.transform(df)

```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|bert_qa_spanbert_base_cased_few_shot_k_512_finetuned_squad_seed_6_pipeline|
|Type:|pipeline|
|Compatibility:|Spark NLP 5.4.2+|
|License:|Open Source|
|Edition:|Official|
|Language:|en|
|Size:|386.6 MB|

## References

https://huggingface.co/anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-6

## Included Models

- MultiDocumentAssembler
- BertForQuestionAnswering
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