English RoBERTa Embeddings (from jackaduma)

Description

Pretrained RoBERTa Embeddings model, uploaded to Hugging Face, adapted and imported into Spark NLP. SecRoBERTa is a English model orginally trained by jackaduma.

Download

How to use

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

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

data = spark.createDataFrame([["I love 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 = RoBertaEmbeddings.pretrained("roberta_embeddings_SecRoBERTa","en") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("embeddings")

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

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

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.embed.SecRoBERTa").predict("""I love Spark NLP""")

Model Information

Model Name: roberta_embeddings_SecRoBERTa
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [sentence, token]
Output Labels: [bert]
Language: en
Size: 314.2 MB
Case sensitive: true

References

  • https://huggingface.co/jackaduma/SecRoBERTa
  • https://github.com/jackaduma/SecBERT/
  • https://github.com/kbandla/APTnotes
  • https://stucco.github.io/data/
  • https://ebiquity.umbc.edu/file_directory/papers/943.pdf
  • https://competitions.codalab.org/competitions/17262
  • https://github.com/allenai/scibert
  • https://github.com/jackaduma/SecBERT
  • https://github.com/jackaduma/SecBERT