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---------

Co-authored-by: ahmedlone127 <[email protected]>
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---
layout: model
title: Romanian ALBERT Embeddings (from dragosnicolae555)
author: John Snow Labs
name: albert_embeddings_ALR_BERT
date: 2023-07-30
tags: [albert, embeddings, ro, open_source, onnx]
task: Embeddings
language: ro
edition: Spark NLP 5.0.2
spark_version: 3.0
supported: true
engine: onnx
annotator: AlbertEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `ALR_BERT` is a Romanian model orginally trained by `dragosnicolae555`.

## 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/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_ALR_BERT_ro_5.0.2_3.0_1690752767725.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("document")

tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["Îmi place Spark NLP"]]).toDF("text")

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

val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_ALR_BERT","ro")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")

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

val data = Seq("Îmi place Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)
```

{:.nlu-block}
```python
import nlu
nlu.load("ro.embed.ALR_BERT").predict("""Îmi place Spark NLP""")
```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|albert_embeddings_ALR_BERT|
|Compatibility:|Spark NLP 5.0.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[sentence, token]|
|Output Labels:|[bert]|
|Language:|ro|
|Size:|51.7 MB|
|Case sensitive:|false|
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---
layout: model
title: Arabic ALBERT Embeddings (Base)
author: John Snow Labs
name: albert_embeddings_albert_base_arabic
date: 2023-07-30
tags: [albert, embeddings, ar, open_source, onnx]
task: Embeddings
language: ar
edition: Spark NLP 5.0.2
spark_version: 3.0
supported: true
engine: onnx
annotator: AlbertEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-arabic` is a Arabic model orginally trained by `asafaya`.

## 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/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_arabic_ar_5.0.2_3.0_1690753212237.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("document")

tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["أنا أحب شرارة NLP"]]).toDF("text")

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

val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_arabic","ar")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")

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

val data = Seq("أنا أحب شرارة NLP").toDF("text")

val result = pipeline.fit(data).transform(data)
```

{:.nlu-block}
```python
import nlu
nlu.load("ar.embed.albert").predict("""أنا أحب شرارة NLP""")
```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|albert_embeddings_albert_base_arabic|
|Compatibility:|Spark NLP 5.0.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[sentence, token]|
|Output Labels:|[bert]|
|Language:|ar|
|Size:|42.0 MB|
|Case sensitive:|false|
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---
layout: model
title: Malay ALBERT Embeddings (Base)
author: John Snow Labs
name: albert_embeddings_albert_base_bahasa_cased
date: 2023-07-30
tags: [albert, embeddings, ms, open_source, onnx]
task: Embeddings
language: ms
edition: Spark NLP 5.0.2
spark_version: 3.0
supported: true
engine: onnx
annotator: AlbertEmbeddings
article_header:
type: cover
use_language_switcher: "Python-Scala-Java"
---

## Description

Pretrained ALBERT Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. `albert-base-bahasa-cased` is a Malay model orginally trained by `malay-huggingface`.

## 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/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/albert_embeddings_albert_base_bahasa_cased_ms_5.0.2_3.0_1690753174981.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("document")

tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")

embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["Saya suka Spark NLP"]]).toDF("text")

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

val tokenizer = new Tokenizer()
.setInputCols(Array("document"))
.setOutputCol("token")

val embeddings = AlbertEmbeddings.pretrained("albert_embeddings_albert_base_bahasa_cased","ms")
.setInputCols(Array("document", "token"))
.setOutputCol("embeddings")

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

val data = Seq("Saya suka Spark NLP").toDF("text")

val result = pipeline.fit(data).transform(data)
```

{:.nlu-block}
```python
import nlu
nlu.load("ms.embed.albert_base_bahasa_cased").predict("""Saya suka Spark NLP""")
```
</div>

{:.model-param}
## Model Information

{:.table-model}
|---|---|
|Model Name:|albert_embeddings_albert_base_bahasa_cased|
|Compatibility:|Spark NLP 5.0.2+|
|License:|Open Source|
|Edition:|Official|
|Input Labels:|[sentence, token]|
|Output Labels:|[bert]|
|Language:|ms|
|Size:|42.9 MB|
|Case sensitive:|false|
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