2023-02-07-asr_hubert_large_ls960_en

docs/_posts/maziyarpanahi/2023-02-07-asr_hubert_large_ls960_en.md

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+---
+layout: model
+title: ASR HubertForCTC - asr_hubert_large_ls960
+author: John Snow Labs
+name: asr_hubert_large_ls960
+date: 2023-02-07
+tags: [open_source, hubert, audio, en, english, asr, speech, librispeech_asr, tensorflow]
+task: Sentiment Analysis
+language: en
+edition: Spark NLP 4.3.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: HubertForCTC
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Hubert Model with a language modeling head on top for Connectionist Temporal Classification (CTC). 
+Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
+
+The large model fine-tuned on 960h of Librispeech on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
+
+{:.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/asr_hubert_large_ls960_en_4.3.0_3.0_1675767067233.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/asr_hubert_large_ls960_en_4.3.0_3.0_1675767067233.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
+
+audio_assembler = AudioAssembler()\
+  .setInputCol("audio_content")\
+  .setOutputCol("audio_assembler")
+
+speech_to_text = HubertForCTC.pretrained("asr_hubert_large_ls960", "en")\
+  .setInputCols("audio_assembler")\
+  .setOutputCol("text")
+
+pipeline = Pipeline(stages=[
+  audio_assembler,
+  speech_to_text,
+])
+
+pipelineModel = pipeline.fit(audioDf)
+
+pipelineDF = pipelineModel.transform(audioDf)
+```
+```scala
+
+val audioAssembler = new AudioAssembler()
+    .setInputCol("audio_content") 
+    .setOutputCol("audio_assembler")
+
+val speechToText = HubertForCTC
+    .pretrained("asr_hubert_large_ls960", "en")
+    .setInputCols("audio_assembler") 
+    .setOutputCol("text") 
+
+val pipeline = new Pipeline().setStages(Array(audioAssembler, speechToText))
+
+val pipelineModel = pipeline.fit(audioDf)
+
+val pipelineDF = pipelineModel.transform(audioDf)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|asr_hubert_large_ls960|
+|Compatibility:|Spark NLP 4.3.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[audio_assembler]|
+|Output Labels:|[text]|
+|Language:|en|
+|Size:|1.5 GB|
+
+## References
+
+[https://huggingface.co/facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft)

docs/_posts/maziyarpanahi/2023-02-07-image_classifier_swin_base_patch4_window12_384_in22k_en.md

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+---
+layout: model
+title: SwinForImageClassification - image_classifier_swin_base_patch4_window12_384_in22k
+author: John Snow Labs
+name: image_classifier_swin_base_patch4_window12_384_in22k
+date: 2023-02-07
+tags: [open_source, swin, image, en, english, image_classification, imagenet, tensorflow]
+task: Sentiment Analysis
+language: en
+edition: Spark NLP 4.3.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: SwinForImageClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Swin model for Image Classification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
+
+Swin Transformer was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al.
+
+{:.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/image_classifier_swin_base_patch4_window12_384_in22k_en_4.3.0_3.0_1675783085913.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/image_classifier_swin_base_patch4_window12_384_in22k_en_4.3.0_3.0_1675783085913.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
+
+image_assembler = ImageAssembler()\
+  .setInputCol("image")
+  .setOutputCol("image_assembler")
+
+imageClassifier = SwinForImageClassification.pretrained("image_classifier_swin_base_patch4_window12_384_in22k", "en")\
+  .setInputCols("image_assembler")\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+  image_assembler,
+  imageClassifier,
+])
+
+pipelineModel = pipeline.fit(imageDF)
+
+pipelineDF = pipelineModel.transform(imageDF)
+```
+```scala
+
+val imageAssembler = new ImageAssembler()
+    .setInputCol("image")
+    .setOutputCol("image_assembler")
+
+val imageClassifier = SwinForImageClassification
+    .pretrained("image_classifier_swin_base_patch4_window12_384_in22k", "en")
+    .setInputCols("image_assembler") 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
+
+val pipelineModel = pipeline.fit(imageDF)
+
+val pipelineDF = pipelineModel.transform(imageDF)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|image_classifier_swin_base_patch4_window12_384_in22k|
+|Compatibility:|Spark NLP 4.3.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|826.8 MB|
+
+## References
+
+[https://huggingface.co/microsoft/swin_base_patch4_window12_384_in22k](https://huggingface.co/microsoft/swin_base_patch4_window12_384_in22k)

docs/_posts/maziyarpanahi/2023-02-07-image_classifier_swin_base_patch4_window7_224_en.md

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+---
+layout: model
+title: SwinForImageClassification - image_classifier_swin_base_patch4_window7_224
+author: John Snow Labs
+name: image_classifier_swin_base_patch4_window7_224
+date: 2023-02-07
+tags: [open_source, swin, image, en, english, image_classification, imagenet, tensorflow]
+task: Sentiment Analysis
+language: en
+edition: Spark NLP 4.3.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: SwinForImageClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Swin model for Image Classification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
+
+Swin Transformer was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al.
+
+{:.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/image_classifier_swin_base_patch4_window7_224_en_4.3.0_3.0_1675783112124.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/image_classifier_swin_base_patch4_window7_224_en_4.3.0_3.0_1675783112124.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
+
+image_assembler = ImageAssembler()\
+  .setInputCol("image")\
+  .setOutputCol("image_assembler")
+
+imageClassifier = SwinForImageClassification.pretrained("image_classifier_swin_base_patch4_window7_224", "en")\
+  .setInputCols("image_assembler")\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+  image_assembler,
+  imageClassifier,
+])
+
+pipelineModel = pipeline.fit(imageDF)
+
+pipelineDF = pipelineModel.transform(imageDF)
+```
+```scala
+
+val imageAssembler = new ImageAssembler()
+    .setInputCol("image")
+    .setOutputCol("image_assembler")
+
+val imageClassifier = SwinForImageClassification
+    .pretrained("image_classifier_swin_base_patch4_window7_224", "en")
+    .setInputCols("image_assembler") 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
+
+val pipelineModel = pipeline.fit(imageDF)
+
+val pipelineDF = pipelineModel.transform(imageDF)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|image_classifier_swin_base_patch4_window7_224|
+|Compatibility:|Spark NLP 4.3.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|665.2 MB|
+
+## References
+
+[https://huggingface.co/microsoft/swin_base_patch4_window7_224](https://huggingface.co/microsoft/swin_base_patch4_window7_224)

docs/_posts/maziyarpanahi/2023-02-07-image_classifier_swin_base_patch4_window7_224_in22k_en.md

@@ -0,0 +1,92 @@
+---
+layout: model
+title: SwinForImageClassification - image_classifier_swin_base_patch4_window7_224_in22k
+author: John Snow Labs
+name: image_classifier_swin_base_patch4_window7_224_in22k
+date: 2023-02-07
+tags: [open_source, swin, image, en, english, image_classification, imagenet, tensorflow]
+task: Sentiment Analysis
+language: en
+edition: Spark NLP 4.3.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: SwinForImageClassification
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained Swin model for Image Classification, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.
+
+Swin Transformer was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al.
+
+{:.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/image_classifier_swin_base_patch4_window7_224_in22k_en_4.3.0_3.0_1675783139673.zip){:.button.button-orange}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/image_classifier_swin_base_patch4_window7_224_in22k_en_4.3.0_3.0_1675783139673.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
+
+image_assembler = ImageAssembler()\
+  .setInputCol("image")\
+  .setOutputCol("image_assembler")
+
+imageClassifier = SwinForImageClassification.pretrained("image_classifier_swin_base_patch4_window7_224_in22k", "en")\
+  .setInputCols("image_assembler")\
+  .setOutputCol("class")
+
+pipeline = Pipeline(stages=[
+  image_assembler,
+  imageClassifier,
+])
+
+pipelineModel = pipeline.fit(imageDF)
+
+pipelineDF = pipelineModel.transform(imageDF)
+```
+```scala
+
+val imageAssembler = new ImageAssembler()
+    .setInputCol("image")
+    .setOutputCol("image_assembler")
+
+val imageClassifier = SwinForImageClassification
+    .pretrained("image_classifier_swin_base_patch4_window7_224_in22k", "en")
+    .setInputCols("image_assembler") 
+    .setOutputCol("class") 
+
+val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
+
+val pipelineModel = pipeline.fit(imageDF)
+
+val pipelineDF = pipelineModel.transform(imageDF)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|image_classifier_swin_base_patch4_window7_224_in22k|
+|Compatibility:|Spark NLP 4.3.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[class]|
+|Language:|en|
+|Size:|825.5 MB|
+
+## References
+
+[https://huggingface.co/microsoft/swin_base_patch4_window7_224_in22k](https://huggingface.co/microsoft/swin_base_patch4_window7_224_in22k)