2023-09-21-multilingual_e5_base_xx

docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_base_xx.md

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+---
+layout: model
+title: Multilingual multilingual_e5_base XlmRoBertaSentenceEmbeddings from intfloat
+author: John Snow Labs
+name: multilingual_e5_base
+date: 2023-09-21
+tags: [xlm_roberta, xx, open_source, tensorflow]
+task: Embeddings
+language: xx
+edition: Spark NLP 5.1.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: XlmRoBertaSentenceEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaSentenceEmbeddings  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_base` is a Multilingual model originally trained by intfloat.
+
+{:.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/multilingual_e5_base_xx_5.1.2_3.0_1695314944172.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_base_xx_5.1.2_3.0_1695314944172.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")
+
+sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ 
+    .setInputCols(["document"])\ 
+    .setOutputCol("sentence")
+
+embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_base","xx") \
+            .setInputCols(["sentence"]) \
+            .setOutputCol("embeddings")
+
+pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
+    .setInputCols(["document"])
+    .setOutputCol("sentence")
+
+val embeddings = XlmRoBertaSentenceEmbeddings 
+    .pretrained("multilingual_e5_base", "xx")
+    .setInputCols(Array("sentence")) 
+    .setOutputCol("embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|multilingual_e5_base|
+|Compatibility:|Spark NLP 5.1.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence]|
+|Output Labels:|[embeddings]|
+|Language:|xx|
+|Size:|667.1 MB|
+
+## References
+
+https://huggingface.co/intfloat/multilingual-e5-base

docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_large_xx.md

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+---
+layout: model
+title: Multilingual multilingual_e5_large XlmRoBertaSentenceEmbeddings from intfloat
+author: John Snow Labs
+name: multilingual_e5_large
+date: 2023-09-21
+tags: [xlm_roberta, xx, open_source, tensorflow]
+task: Embeddings
+language: xx
+edition: Spark NLP 5.1.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: XlmRoBertaSentenceEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaSentenceEmbeddings  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_large` is a Multilingual model originally trained by intfloat.
+
+{:.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/multilingual_e5_large_xx_5.1.2_3.0_1695315223158.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_large_xx_5.1.2_3.0_1695315223158.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")
+
+sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ 
+    .setInputCols(["document"])\ 
+    .setOutputCol("sentence")
+
+embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_large","xx") \
+            .setInputCols(["sentence"]) \
+            .setOutputCol("embeddings")
+
+pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
+    .setInputCols(["document"])
+    .setOutputCol("sentence")
+
+val embeddings = XlmRoBertaSentenceEmbeddings 
+    .pretrained("multilingual_e5_large", "xx")
+    .setInputCols(Array("sentence")) 
+    .setOutputCol("embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|multilingual_e5_large|
+|Compatibility:|Spark NLP 5.1.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence]|
+|Output Labels:|[embeddings]|
+|Language:|xx|
+|Size:|1.3 GB|
+
+## References
+
+https://huggingface.co/intfloat/multilingual-e5-large

docs/_posts/ahmedlone127/2023-09-21-multilingual_e5_small_xx.md

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+---
+layout: model
+title: Multilingual multilingual_e5_small XlmRoBertaSentenceEmbeddings from intfloat
+author: John Snow Labs
+name: multilingual_e5_small
+date: 2023-09-21
+tags: [xlm_roberta, xx, open_source, tensorflow]
+task: Embeddings
+language: xx
+edition: Spark NLP 5.1.2
+spark_version: 3.0
+supported: true
+engine: tensorflow
+annotator: XlmRoBertaSentenceEmbeddings
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+Pretrained XlmRoBertaSentenceEmbeddings  model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP.`multilingual_e5_small` is a Multilingual model originally trained by intfloat.
+
+{:.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/multilingual_e5_small_xx_5.1.2_3.0_1695316525385.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/multilingual_e5_small_xx_5.1.2_3.0_1695316525385.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")
+
+sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\ 
+    .setInputCols(["document"])\ 
+    .setOutputCol("sentence")
+
+embeddings =XlmRoBertaSentenceEmbeddings.pretrained("multilingual_e5_small","xx") \
+            .setInputCols(["sentence"]) \
+            .setOutputCol("embeddings")
+
+pipeline = Pipeline().setStages([document_assembler, sentencerDL, embeddings])
+
+pipelineModel = pipeline.fit(data)
+
+pipelineDF = pipelineModel.transform(data)
+
+```
+```scala
+
+
+val document_assembler = new DocumentAssembler()
+    .setInputCol("text") 
+    .setOutputCol("documents")
+
+val sentencerDL = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
+    .setInputCols(["document"])
+    .setOutputCol("sentence")
+
+val embeddings = XlmRoBertaSentenceEmbeddings 
+    .pretrained("multilingual_e5_small", "xx")
+    .setInputCols(Array("sentence")) 
+    .setOutputCol("embeddings") 
+
+val pipeline = new Pipeline().setStages(Array(document_assembler, sentencerDL, embeddings))
+
+val pipelineModel = pipeline.fit(data)
+
+val pipelineDF = pipelineModel.transform(data)
+
+
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|multilingual_e5_small|
+|Compatibility:|Spark NLP 5.1.2+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[sentence]|
+|Output Labels:|[embeddings]|
+|Language:|xx|
+|Size:|284.4 MB|
+
+## References
+
+https://huggingface.co/intfloat/multilingual-e5-small