nlp_roberta_sentence_embeddings_pretrained: Load a pretrained Spark NLP RoBertaSentenceEmbeddings model

In r-spark/sparknlp: R Interface to John Snow Labs Spark NLP

Load a pretrained Spark NLP RoBertaSentenceEmbeddings model

Description

Create a pretrained Spark NLP RoBertaSentenceEmbeddings model. Sentence-level embeddings using RoBERTa. The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Google's BERT model released in 2018.

Usage

nlp_roberta_sentence_embeddings_pretrained(
  sc,
  input_cols,
  output_col,
  case_sensitive = NULL,
  batch_size = NULL,
  dimension = NULL,
  max_sentence_length = NULL,
  name = NULL,
  lang = NULL,
  remote_loc = NULL
)

Arguments

sc	A Spark connection
input_cols	Input columns. String array.
output_col	Output column. String.
case_sensitive	whether to lowercase tokens or not
batch_size	batch size
dimension	defines the output layer of BERT when calculating embeddings
max_sentence_length	max sentence length to process
name	the name of the model to load. If NULL will use the default value
lang	the language of the model to be loaded. If NULL will use the default value
remote_loc	the remote location of the model. If NULL will use the default value

Details

It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates. See https://nlp.johnsnowlabs.com/docs/en/annotators#robertabertsentenceembeddings

Value

The Spark NLP model with the pretrained model loaded

r-spark/sparknlp documentation built on Oct. 15, 2022, 10:50 a.m.