2023-12-24-roberta_base_wechsel_german_finetuned_germanquad_en

docs/_posts/ahmedlone127/2023-12-24-bert_finetuned_squad_cojocaruvicentiu_en.md

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+---
+layout: model
+title: English bert_finetuned_squad_cojocaruvicentiu RoBertaForQuestionAnswering from cojocaruvicentiu
+author: John Snow Labs
+name: bert_finetuned_squad_cojocaruvicentiu
+date: 2023-12-24
+tags: [roberta, en, open_source, question_answering, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: RoBertaForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bert_finetuned_squad_cojocaruvicentiu` is a English model originally trained by cojocaruvicentiu.
+
+{:.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_finetuned_squad_cojocaruvicentiu_en_5.2.1_3.0_1703413725479.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bert_finetuned_squad_cojocaruvicentiu_en_5.2.1_3.0_1703413725479.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 = MultiDocumentAssembler() \
+    .setInputCol(["question", "context"]) \
+    .setOutputCol(["document_question", "document_context"])
+
+
+spanClassifier = RoBertaForQuestionAnswering.pretrained("bert_finetuned_squad_cojocaruvicentiu","en") \
+            .setInputCols(["document_question","document_context"]) \
+            .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([document_assembler, spanClassifier])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new MultiDocumentAssembler()
+    .setInputCol(Array("question", "context")) 
+    .setOutputCol(Array("document_question", "document_context"))
+
+val spanClassifier = RoBertaForQuestionAnswering  
+    .pretrained("bert_finetuned_squad_cojocaruvicentiu", "en")
+    .setInputCols(Array("document_question","document_context")) 
+    .setOutputCol("answer") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bert_finetuned_squad_cojocaruvicentiu|
+|Compatibility:|Spark NLP 5.2.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|457.6 MB|
+
+## References
+
+https://huggingface.co/cojocaruvicentiu/bert-finetuned-squad

docs/_posts/ahmedlone127/2023-12-24-bertin_roberta_base_spanish_finetuned_qa_mlqa_en.md

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+---
+layout: model
+title: English bertin_roberta_base_spanish_finetuned_qa_mlqa RoBertaForQuestionAnswering from dccuchile
+author: John Snow Labs
+name: bertin_roberta_base_spanish_finetuned_qa_mlqa
+date: 2023-12-24
+tags: [roberta, en, open_source, question_answering, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: RoBertaForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`bertin_roberta_base_spanish_finetuned_qa_mlqa` is a English model originally trained by dccuchile.
+
+{:.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/bertin_roberta_base_spanish_finetuned_qa_mlqa_en_5.2.1_3.0_1703421656386.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/bertin_roberta_base_spanish_finetuned_qa_mlqa_en_5.2.1_3.0_1703421656386.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 = MultiDocumentAssembler() \
+    .setInputCol(["question", "context"]) \
+    .setOutputCol(["document_question", "document_context"])
+
+
+spanClassifier = RoBertaForQuestionAnswering.pretrained("bertin_roberta_base_spanish_finetuned_qa_mlqa","en") \
+            .setInputCols(["document_question","document_context"]) \
+            .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([document_assembler, spanClassifier])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new MultiDocumentAssembler()
+    .setInputCol(Array("question", "context")) 
+    .setOutputCol(Array("document_question", "document_context"))
+
+val spanClassifier = RoBertaForQuestionAnswering  
+    .pretrained("bertin_roberta_base_spanish_finetuned_qa_mlqa", "en")
+    .setInputCols(Array("document_question","document_context")) 
+    .setOutputCol("answer") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|bertin_roberta_base_spanish_finetuned_qa_mlqa|
+|Compatibility:|Spark NLP 5.2.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|462.2 MB|
+
+## References
+
+https://huggingface.co/dccuchile/bertin-roberta-base-spanish-finetuned-qa-mlqa

docs/_posts/ahmedlone127/2023-12-24-burmese_awesome_qa_model_ingenieria_en.md

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+---
+layout: model
+title: English burmese_awesome_qa_model_ingenieria RoBertaForQuestionAnswering from Ingenieria
+author: John Snow Labs
+name: burmese_awesome_qa_model_ingenieria
+date: 2023-12-24
+tags: [roberta, en, open_source, question_answering, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: RoBertaForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_qa_model_ingenieria` is a English model originally trained by Ingenieria.
+
+{:.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/burmese_awesome_qa_model_ingenieria_en_5.2.1_3.0_1703415208346.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_qa_model_ingenieria_en_5.2.1_3.0_1703415208346.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 = MultiDocumentAssembler() \
+    .setInputCol(["question", "context"]) \
+    .setOutputCol(["document_question", "document_context"])
+
+
+spanClassifier = RoBertaForQuestionAnswering.pretrained("burmese_awesome_qa_model_ingenieria","en") \
+            .setInputCols(["document_question","document_context"]) \
+            .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([document_assembler, spanClassifier])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new MultiDocumentAssembler()
+    .setInputCol(Array("question", "context")) 
+    .setOutputCol(Array("document_question", "document_context"))
+
+val spanClassifier = RoBertaForQuestionAnswering  
+    .pretrained("burmese_awesome_qa_model_ingenieria", "en")
+    .setInputCols(Array("document_question","document_context")) 
+    .setOutputCol("answer") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|burmese_awesome_qa_model_ingenieria|
+|Compatibility:|Spark NLP 5.2.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|459.1 MB|
+
+## References
+
+https://huggingface.co/Ingenieria/my_awesome_qa_model

docs/_posts/ahmedlone127/2023-12-24-burmese_awesome_qa_model_itsamitkumar_en.md

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+---
+layout: model
+title: English burmese_awesome_qa_model_itsamitkumar RoBertaForQuestionAnswering from itsamitkumar
+author: John Snow Labs
+name: burmese_awesome_qa_model_itsamitkumar
+date: 2023-12-24
+tags: [roberta, en, open_source, question_answering, onnx]
+task: Question Answering
+language: en
+edition: Spark NLP 5.2.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: RoBertaForQuestionAnswering
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained RoBertaForQuestionAnswering model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`burmese_awesome_qa_model_itsamitkumar` is a English model originally trained by itsamitkumar.
+
+{:.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/burmese_awesome_qa_model_itsamitkumar_en_5.2.1_3.0_1703415208193.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/burmese_awesome_qa_model_itsamitkumar_en_5.2.1_3.0_1703415208193.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 = MultiDocumentAssembler() \
+    .setInputCol(["question", "context"]) \
+    .setOutputCol(["document_question", "document_context"])
+
+
+spanClassifier = RoBertaForQuestionAnswering.pretrained("burmese_awesome_qa_model_itsamitkumar","en") \
+            .setInputCols(["document_question","document_context"]) \
+            .setOutputCol("answer")
+
+pipeline = Pipeline().setStages([document_assembler, spanClassifier])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new MultiDocumentAssembler()
+    .setInputCol(Array("question", "context")) 
+    .setOutputCol(Array("document_question", "document_context"))
+
+val spanClassifier = RoBertaForQuestionAnswering  
+    .pretrained("burmese_awesome_qa_model_itsamitkumar", "en")
+    .setInputCols(Array("document_question","document_context")) 
+    .setOutputCol("answer") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, spanClassifier))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|burmese_awesome_qa_model_itsamitkumar|
+|Compatibility:|Spark NLP 5.2.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[document_question, document_context]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|463.9 MB|
+
+## References
+
+https://huggingface.co/itsamitkumar/my_awesome_qa_model