Amharic RoBERTa Embeddings (from uhhlt)

Description

Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. am-roberta is a Amharic model orginally trained by uhhlt.

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How to use

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

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")
  
embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_am_roberta","am") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings")
    
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["ስካርቻ nlp እወዳለሁ"]]).toDF("text")

result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler() 
      .setInputCol("text") 
      .setOutputCol("document")
 
val tokenizer = new Tokenizer() 
    .setInputCols(Array("document"))
    .setOutputCol("token")

val embeddings = RoBertaEmbeddings.pretrained("roberta_embeddings_am_roberta","am") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("embeddings")

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

val data = Seq("ስካርቻ nlp እወዳለሁ").toDF("text")

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

Model Information

Model Name: roberta_embeddings_am_roberta
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [bert]
Language: am
Size: 1.6 GB
Case sensitive: true

References

  • https://huggingface.co/uhhlt/am-roberta
  • https://github.com/uhh-lt/amharicmodels
  • https://github.com/uhh-lt/amharicmodels
  • https://www.mdpi.com/1999-5903/13/11/275