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2024-04-22-mpnet_embeddings_biolord_2023_en #14240

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85 changes: 85 additions & 0 deletions docs/_posts/akrztrk/2024-04-22-mpnet_embeddings_biolord_2023_en.md
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---
layout: model
title: English BioLORD-2023 MPNetEmbeddings from FremyCompany
author: John Snow Labs
name: mpnet_embeddings_biolord_2023
date: 2024-04-22
tags: [en, mpnet, embeddings, biolord, open_source, onnx]
task: Embeddings
language: en
edition: Spark NLP 5.3.4
spark_version: 3.0
supported: true
engine: onnx
annotator: MPNetEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained MPNetEmbeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP `mpnet_embeddings_biolord_2023` is a English model originally trained by `FremyCompany`.

## 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/mpnet_embeddings_biolord_2023_en_5.3.4_3.0_1713812521034.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/mpnet_embeddings_biolord_2023_en_5.3.4_3.0_1713812521034.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")

embeddings =MPNetEmbeddings.pretrained("mpnet_embeddings_biolord_2023","en")\
.setInputCols(["documents"])\
.setOutputCol("mpnet_embeddings")

pipeline = Pipeline().setStages([document_assembler, embeddings])

result = pipeline.fit(data).transform(data)
```
```scala
val document_assembler = new DocumentAssembler()
.setInputCol("text")
.setOutputCol("documents")

val embeddings = MPNetEmbeddings
.pretrained("mpnet_embeddings_biolord_2023", "en")
.setInputCols(Array("documents"))
.setOutputCol("mpnet_embeddings")

val pipeline = new Pipeline().setStages(Array(document_assembler, embeddings))

val result = pipeline.fit(data).transform(data)
```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|mpnet_embeddings_biolord_2023|
|Compatibility:|Spark NLP 5.3.4+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[document]|
|Output Labels:|[MPNet]|
|Language:|en|
|Size:|407.0 MB|

## References

https://huggingface.co/FremyCompany/BioLORD-2023