nlp_xlm_roberta_sentence_embeddings_pretrained: Load a pretrained Spark NLP XlmRoBertaSentenceEmbeddings...

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

Load a pretrained Spark NLP XlmRoBertaSentenceEmbeddings model

Description

Create a pretrained Spark NLP XlmRoBertaSentenceEmbeddings model. See https://nlp.johnsnowlabs.com/docs/en/annotators#xlmrobertasentenceembeddings

Usage

nlp_xlm_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

Sentence-level embeddings using XLM-RoBERTa. The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data.

Value

The Spark NLP model with the pretrained model loaded

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