embedding_glove | R Documentation |
The GloVe pre-trained word vectors provide word embeddings created using varying numbers of tokens.
embedding_glove6b( dir = NULL, dimensions = c(50, 100, 200, 300), delete = FALSE, return_path = FALSE, clean = FALSE, manual_download = FALSE ) embedding_glove27b( dir = NULL, dimensions = c(25, 50, 100, 200), delete = FALSE, return_path = FALSE, clean = FALSE, manual_download = FALSE ) embedding_glove42b( dir = NULL, delete = FALSE, return_path = FALSE, clean = FALSE, manual_download = FALSE ) embedding_glove840b( dir = NULL, delete = FALSE, return_path = FALSE, clean = FALSE, manual_download = FALSE )
dir |
Character, path to directory where data will be stored. If
|
dimensions |
A number indicating the number of vectors to include. One of 50, 100, 200, or 300 for glove6b, or one of 25, 50, 100, or 200 for glove27b. |
delete |
Logical, set |
return_path |
Logical, set |
clean |
Logical, set |
manual_download |
Logical, set |
Citation info:
InProceedings{pennington2014glove,
author = {Jeffrey Pennington and Richard Socher and Christopher D.
Manning},
title = {GloVe: Global Vectors for Word Representation},
booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
year = 2014
pages = {1532-1543}
url = {http://www.aclweb.org/anthology/D14-1162}
}
A tibble with 400k, 1.9m, 2.2m, or 1.2m rows (one row for each unique token in the vocabulary) and the following variables:
An individual token (usually a word)
The embeddings for that token.
https://nlp.stanford.edu/projects/glove/
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.
## Not run: embedding_glove6b(dimensions = 50) # Custom directory embedding_glove42b(dir = "data/") # Deleting dataset embedding_glove6b(delete = TRUE, dimensions = 300) # Returning filepath of data embedding_glove840b(return_path = TRUE) ## End(Not run)
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