A simple lookup table that looks up embeddings in a fixed dictionary and size.
nnf_embedding(
input,
weight,
padding_idx = NULL,
max_norm = NULL,
norm_type = 2,
scale_grad_by_freq = FALSE,
sparse = FALSE
)(LongTensor) Tensor containing indices into the embedding matrix
(Tensor) The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size
(int, optional) If given, pads the output with the embedding
vector at padding_idx (initialized to zeros) whenever it encounters the index.
(float, optional) If given, each embedding vector with norm larger
than max_norm is renormalized to have norm max_norm. Note: this will modify
weight in-place.
(float, optional) The p of the p-norm to compute for the max_norm
option. Default 2.
(boolean, optional) If given, this will scale gradients
by the inverse of frequency of the words in the mini-batch. Default FALSE.
(bool, optional) If TRUE, gradient w.r.t. weight will be a
sparse tensor. See Notes under nn_embedding for more details regarding
sparse gradients.
This module is often used to retrieve word embeddings using indices. The input to the module is a list of indices, and the embedding matrix, and the output is the corresponding word embeddings.