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  • John Snow Labs
  • Apr 11, 2022

Spanish Bert Embeddings (from flax-community)

  • bert
  • embeddings
  • es
  • open_source

Description

Pretrained Bert Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. alberti-bert-base-multilingual-cased is a Spanish model orginally trained by flax-community.

Download Copy S3 URI

How to use

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

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

embeddings = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") \
.setInputCols(["document", "token"]) \
.setOutputCol("embeddings")

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

data = spark.createDataFrame([["Me encanta chispa 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 = BertEmbeddings.pretrained("bert_embeddings_alberti_bert_base_multilingual_cased","es") 
.setInputCols(Array("document", "token")) 
.setOutputCol("embeddings")

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

val data = Seq("Me encanta chispa nlp").toDF("text")

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

import nlu
nlu.load("es.embed.alberti_bert_base_multilingual_cased").predict("""Me encanta chispa nlp""")

Model Information

Model Name: bert_embeddings_alberti_bert_base_multilingual_cased
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [bert]
Language: es
Size: 667.2 MB
Case sensitive: true

References

  • https://huggingface.co/flax-community/alberti-bert-base-multilingual-cased
  • https://github.com/google/flax
  • https://github.com/linhd-postdata/averell/
  • https://postdata.linhd.uned.es/
  • https://github.com/pruizf/disco
  • https://github.com/bncolorado/CorpusSonetosSigloDeOro
  • https://github.com/bncolorado/CorpusGeneralPoesiaLiricaCastellanaDelSigloDeOro
  • https://github.com/linhd-postdata/gongocorpus
  • http://obvil.sorbonne-universite.site/corpus/gongora/gongora_obra-poetica
  • https://github.com/alhuber1502/ECPA
  • https://github.com/waynegraham/for_better_for_verse
  • https://crisco2.unicaen.fr/verlaine/index.php?navigation=accueil
  • https://github.com/linhd-postdata/metrique-en-ligne
  • https://github.com/linhd-postdata/biblioteca_italiana
  • http://www.bibliotecaitaliana.it/
  • https://github.com/versotym/corpusCzechVerse
  • https://gitlab.com/stichotheque/stichotheque-pt
  • https://github.com/linhd-postdata/poesi.as
  • http://www.poesi.as/
  • https://github.com/aparrish/gutenberg-poetry-corpus
  • https://www.kaggle.com/ahmedabelal/arabic-poetry
  • https://github.com/THUNLP-AIPoet/Datasets/tree/master/CCPC
  • https://github.com/sks190/SKVR
  • https://github.com/linhd-postdata/textgrid-poetry
  • https://textgrid.de/en/digitale-bibliothek
  • https://github.com/tnhaider/german-rhyme-corpus
  • https://github.com/ELTE-DH/verskorpusz
  • https://www.kaggle.com/oliveirasp6/poems-in-portuguese
  • https://www.kaggle.com/grafstor/19-000-russian-poems
  • https://discord.com/channels/858019234139602994/859113060068229190