Italian CamemBert Embeddings (from Musixmatch)

Description

Pretrained CamemBert Embeddings model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. umberto-commoncrawl-cased-v1 is a Italian model orginally trained by Musixmatch.

Live Demo Open in Colab Download Copy S3 URI

How to use

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

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")
  
embeddings = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings")
    
pipeline = Pipeline(stages=[documentAssembler, tokenizer, embeddings])

data = spark.createDataFrame([["Adoro Spark NLP"]]).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 = CamemBertEmbeddings.pretrained("camembert_embeddings_umberto_commoncrawl_cased_v1","it") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("embeddings")

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

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

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

import nlu
nlu.load("it.embed.camembert.cased").predict("""Adoro Spark NLP""")

Model Information

References

• https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1
• https://github.com/musixmatchresearch/umberto
• https://traces1.inria.fr/oscar/
• http://bit.ly/35zO7GH
• https://github.com/google/sentencepiece
• https://github.com/musixmatchresearch/umberto
• https://github.com/UniversalDependencies/UD_Italian-ISDT
• https://github.com/UniversalDependencies/UD_Italian-ParTUT
• http://www.evalita.it/
• https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500
• https://www.sciencedirect.com/science/article/pii/S0004370212000276?via%3Dihub
• https://github.com/loretoparisi
• https://github.com/simonefrancia
• https://github.com/paulthemagno
• https://twitter.com/Musixmatch
• https://twitter.com/musixmatchai
• https://github.com/musixmatchresearch