2023-08-18-mpnet_embedding_mpnet_snli_en

docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_579_STmodel_product_rem_v3a_en.md

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+---
+layout: model
+title: English mpnet_embedding_579_STmodel_product_rem_v3a TFMPNetModel from jamiehudson
+author: John Snow Labs
+name: mpnet_embedding_579_STmodel_product_rem_v3a
+date: 2023-08-18
+tags: [mpnet, en, open_source, tensorflow]
+task: Embeddings
+language: en
+edition: Spark NLP 5.1.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MPNetEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained mpnet  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_579_STmodel_product_rem_v3a` is a English model originally trained by jamiehudson.
+
+{:.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/mpnet_embedding_579_STmodel_product_rem_v3a_en_5.1.0_3.0_1692379340262.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_579_STmodel_product_rem_v3a_en_5.1.0_3.0_1692379340262.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 = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+instruction = MPNetEmbeddings \
+    .pretrained("mpnet_embedding_579_STmodel_product_rem_v3a", "en")\
+    .setInputCols(["documents"]) \
+    .setOutputCol("mpnet_embeddings")
+
+pipeline = Pipeline(stages=[
+  document_assembler,
+  instruction,
+])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val instruction = MPNetEmbeddings
+    .pretrained("mpnet_embedding_579_STmodel_product_rem_v3a", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("mpnet_embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, instruction))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|mpnet_embedding_579_STmodel_product_rem_v3a|
+|Compatibility:|Spark NLP 5.1.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[mpnet_embeddings]|
+|Language:|en|
+|Size:|410.2 MB|

docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_ATTACK_BERT_en.md

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+---
+layout: model
+title: English mpnet_embedding_ATTACK_BERT TFMPNetModel from basel
+author: John Snow Labs
+name: mpnet_embedding_ATTACK_BERT
+date: 2023-08-18
+tags: [mpnet, en, open_source, tensorflow]
+task: Embeddings
+language: en
+edition: Spark NLP 5.1.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MPNetEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained mpnet  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_ATTACK_BERT` is a English model originally trained by basel.
+
+{:.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/mpnet_embedding_ATTACK_BERT_en_5.1.0_3.0_1692376584683.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_ATTACK_BERT_en_5.1.0_3.0_1692376584683.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 = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+instruction = MPNetEmbeddings \
+    .pretrained("mpnet_embedding_ATTACK_BERT", "en")\
+    .setInputCols(["documents"]) \
+    .setOutputCol("mpnet_embeddings")
+
+pipeline = Pipeline(stages=[
+  document_assembler,
+  instruction,
+])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val instruction = MPNetEmbeddings
+    .pretrained("mpnet_embedding_ATTACK_BERT", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("mpnet_embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, instruction))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|mpnet_embedding_ATTACK_BERT|
+|Compatibility:|Spark NLP 5.1.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[mpnet_embeddings]|
+|Language:|en|
+|Size:|409.8 MB|

docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_BioLORD_STAMB2_v1_en.md

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+---
+layout: model
+title: English mpnet_embedding_BioLORD_STAMB2_v1 TFMPNetModel from FremyCompany
+author: John Snow Labs
+name: mpnet_embedding_BioLORD_STAMB2_v1
+date: 2023-08-18
+tags: [mpnet, en, open_source, tensorflow]
+task: Embeddings
+language: en
+edition: Spark NLP 5.1.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MPNetEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained mpnet  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_BioLORD_STAMB2_v1` is a English model originally trained by FremyCompany.
+
+{:.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/mpnet_embedding_BioLORD_STAMB2_v1_en_5.1.0_3.0_1692377544444.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_BioLORD_STAMB2_v1_en_5.1.0_3.0_1692377544444.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 = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+instruction = MPNetEmbeddings \
+    .pretrained("mpnet_embedding_BioLORD_STAMB2_v1", "en")\
+    .setInputCols(["documents"]) \
+    .setOutputCol("mpnet_embeddings")
+
+pipeline = Pipeline(stages=[
+  document_assembler,
+  instruction,
+])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val instruction = MPNetEmbeddings
+    .pretrained("mpnet_embedding_BioLORD_STAMB2_v1", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("mpnet_embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, instruction))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|mpnet_embedding_BioLORD_STAMB2_v1|
+|Compatibility:|Spark NLP 5.1.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[mpnet_embeddings]|
+|Language:|en|
+|Size:|409.9 MB|

docs/_posts/ahmedlone127/2023-08-18-mpnet_embedding_CPU_Conditional_Classifier_en.md

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+---
+layout: model
+title: English mpnet_embedding_CPU_Conditional_Classifier TFMPNetModel from mtyrrell
+author: John Snow Labs
+name: mpnet_embedding_CPU_Conditional_Classifier
+date: 2023-08-18
+tags: [mpnet, en, open_source, tensorflow]
+task: Embeddings
+language: en
+edition: Spark NLP 5.1.0
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: MPNetEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained mpnet  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`mpnet_embedding_CPU_Conditional_Classifier` is a English model originally trained by mtyrrell.
+
+{:.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/mpnet_embedding_CPU_Conditional_Classifier_en_5.1.0_3.0_1692380267965.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embedding_CPU_Conditional_Classifier_en_5.1.0_3.0_1692380267965.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 = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+instruction = MPNetEmbeddings \
+    .pretrained("mpnet_embedding_CPU_Conditional_Classifier", "en")\
+    .setInputCols(["documents"]) \
+    .setOutputCol("mpnet_embeddings")
+
+pipeline = Pipeline(stages=[
+  document_assembler,
+  instruction,
+])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+```
+```scala
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val instruction = MPNetEmbeddings
+    .pretrained("mpnet_embedding_CPU_Conditional_Classifier", "en")
+    .setInputCols(Array("documents")) 
+    .setOutputCol("mpnet_embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, instruction))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|mpnet_embedding_CPU_Conditional_Classifier|
+|Compatibility:|Spark NLP 5.1.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[mpnet_embeddings]|
+|Language:|en|
+|Size:|410.2 MB|