2023-07-30-albert_embeddings_ALR_BERT_ro

docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_ALR_BERT_ro.md

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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|

docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_arabic_ar.md

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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|

docs/_posts/ahmedlone127/2023-07-30-albert_embeddings_albert_base_bahasa_cased_ms.md

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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|