Malay ALBERT Embeddings (Base)


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.

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How to use

documentAssembler = DocumentAssembler() \
.setInputCol("text") \

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

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

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

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

result =
val documentAssembler = new DocumentAssembler() 

val tokenizer = new Tokenizer() 

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

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

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

val result =
import nlu
nlu.load("ms.embed.albert_base_bahasa_cased").predict("""Saya suka Spark NLP""")

Model Information

Model Name: albert_embeddings_albert_base_bahasa_cased
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [bert]
Language: ms
Size: 45.7 MB
Case sensitive: false