2024-11-01-distilbart_xsum_12_6_en

docs/_posts/ahmedlone127/2024-11-01-distilbart_xsum_12_6_en.md

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+---
+layout: model
+title: Abstractive Summarization by BART - DistilBART XSUM
+author: John Snow Labs
+name: distilbart_xsum_12_6
+date: 2024-11-01
+tags: [en, summarization, text_to_text, distil, open_source, openvino]
+task: Summarization
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: BartTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+“BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension Transformer” The Facebook BART (Bidirectional and Auto-Regressive Transformer) model is a state-of-the-art language generation model that was introduced by Facebook AI in 2019. It is based on the transformer architecture and is designed to handle a wide range of natural language processing tasks such as text generation, summarization, and machine translation.
+
+This pre-trained model is DistilBART fine-tuned on the Extreme Summarization (XSum) Dataset.
+
+## Predicted Entities
+
+
+
+{:.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/distilbart_xsum_12_6_en_5.5.0_3.0_1730492024334.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/distilbart_xsum_12_6_en_5.5.0_3.0_1730492024334.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
+bart = BartTransformer.pretrained("distilbart_xsum_12_6") \
+            .setTask("summarize:") \
+            .setMaxOutputLength(200) \
+            .setInputCols(["documents"]) \
+            .setOutputCol("summaries")
+```
+```scala
+val bart = BartTransformer.pretrained("distilbart_xsum_12_6")
+            .setTask("summarize:")
+            .setMaxOutputLength(200)
+            .setInputCols("documents")
+            .setOutputCol("summaries")
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|distilbart_xsum_12_6|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|853.7 MB|
+
+## References
+
+https://huggingface.co/sshleifer/distilbart-xsum-12-6

docs/_posts/ahmedlone127/2024-11-03-gpt2_en.md

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+---
+layout: model
+title: GPT2 text-to-text model (Base)
+author: John Snow Labs
+name: gpt2
+date: 2024-11-03
+tags: [gpt2, en, open_source, onnx, openvino]
+task: Text Generation
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: GPT2Transformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+“GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where the model is primed with an input and it generates a lengthy continuation.
+
+## Predicted Entities
+
+
+
+{:.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/gpt2_en_5.5.0_3.0_1730653115205.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/gpt2_en_5.5.0_3.0_1730653115205.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("documents")
+
+gpt2 = GPT2Transformer.pretrained("gpt2") \
+.setInputCols(["documents"]) \
+.setMaxOutputLength(50) \
+.setOutputCol("generation")
+
+pipeline = Pipeline().setStages([documentAssembler, gpt2])
+data = spark.createDataFrame([["My name is Leonardo."]]).toDF("text")
+result = pipeline.fit(data).transform(data)
+result.select("summaries.generation").show(truncate=False)
+```
+```scala
+val documentAssembler = new DocumentAssembler()
+.setInputCol("text")
+.setOutputCol("documents")
+
+val gpt2 = GPT2Transformer.pretrained("gpt2")
+.setInputCols(Array("documents"))
+.setMinOutputLength(10)
+.setMaxOutputLength(50)
+.setDoSample(false)
+.setTopK(50)
+.setNoRepeatNgramSize(3)
+.setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, gpt2))
+
+val data = Seq("My name is Leonardo.").toDF("text")
+val result = pipeline.fit(data).transform(data)
+results.select("generation.result").show(truncate = false)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|gpt2|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|467.4 MB|
+
+## References
+
+https://huggingface.co/openai-community/gpt2

docs/_posts/ahmedlone127/2024-11-08-hubert_large_japanese_asr_ja.md

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+---
+layout: model
+title: Japanese hubert_large_japanese_asr HubertForCTC from TKU410410103
+author: John Snow Labs
+name: hubert_large_japanese_asr
+date: 2024-11-08
+tags: [ja, open_source, onnx, asr, hubert]
+task: Automatic Speech Recognition
+language: ja
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: HubertForCTC
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained HubertForCTC model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubert_large_japanese_asr` is a Japanese model originally trained by TKU410410103.
+
+{:.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/hubert_large_japanese_asr_ja_5.5.1_3.0_1731106819898.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubert_large_japanese_asr_ja_5.5.1_3.0_1731106819898.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
+
+audioAssembler = AudioAssembler() \
+	.setInputCol("audio_content") \
+	.setOutputCol("audio_assembler")
+
+speechToText  = HubertForCTC.pretrained("hubert_large_japanese_asr","ja") \
+     .setInputCols(["audio_assembler"]) \
+     .setOutputCol("text")
+
+pipeline = Pipeline().setStages([audioAssembler, speechToText])
+pipelineModel = pipeline.fit(data)
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+val audioAssembler = new DocumentAssembler()
+    .setInputCols("audio_content")
+    .setOutputCols("audio_assembler")
+
+val speechToText = HubertForCTC.pretrained("hubert_large_japanese_asr", "ja")
+    .setInputCols(Array("audio_assembler")) 
+    .setOutputCol("text") 
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, speechToText))
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|hubert_large_japanese_asr|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[audio_assembler]|
+|Output Labels:|[text]|
+|Language:|ja|
+|Size:|2.4 GB|
+
+## References
+
+https://huggingface.co/TKU410410103/hubert-large-japanese-asr

docs/_posts/ahmedlone127/2024-11-08-hubert_large_japanese_asr_pipeline_ja.md

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+---
+layout: model
+title: Japanese hubert_large_japanese_asr_pipeline pipeline HubertForCTC from TKU410410103
+author: John Snow Labs
+name: hubert_large_japanese_asr_pipeline
+date: 2024-11-08
+tags: [ja, open_source, pipeline, onnx]
+task: Automatic Speech Recognition
+language: ja
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained HubertForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubert_large_japanese_asr_pipeline` is a Japanese model originally trained by TKU410410103.
+
+{:.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/hubert_large_japanese_asr_pipeline_ja_5.5.1_3.0_1731106937966.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubert_large_japanese_asr_pipeline_ja_5.5.1_3.0_1731106937966.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("hubert_large_japanese_asr_pipeline", lang = "ja")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("hubert_large_japanese_asr_pipeline", lang = "ja")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|hubert_large_japanese_asr_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|ja|
+|Size:|2.4 GB|
+
+## References
+
+https://huggingface.co/TKU410410103/hubert-large-japanese-asr
+
+## Included Models
+
+- AudioAssembler
+- HubertForCTC

docs/_posts/ahmedlone127/2024-11-08-hubert_ukrainian_pipeline_uk.md

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+---
+layout: model
+title: Ukrainian hubert_ukrainian_pipeline pipeline HubertForCTC from Yehor
+author: John Snow Labs
+name: hubert_ukrainian_pipeline
+date: 2024-11-08
+tags: [uk, open_source, pipeline, onnx]
+task: Automatic Speech Recognition
+language: uk
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+annotator: PipelineModel
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained HubertForCTC, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`hubert_ukrainian_pipeline` is a Ukrainian model originally trained by Yehor.
+
+{:.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/hubert_ukrainian_pipeline_uk_5.5.1_3.0_1731106461400.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/hubert_ukrainian_pipeline_uk_5.5.1_3.0_1731106461400.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("hubert_ukrainian_pipeline", lang = "uk")
+annotations =  pipeline.transform(df)   
+
+```
+```scala
+
+val pipeline = new PretrainedPipeline("hubert_ukrainian_pipeline", lang = "uk")
+val annotations = pipeline.transform(df)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|hubert_ukrainian_pipeline|
+|Type:|pipeline|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Language:|uk|
+|Size:|708.6 MB|
+
+## References
+
+https://huggingface.co/Yehor/hubert-uk
+
+## Included Models
+
+- AudioAssembler
+- HubertForCTC