Embedding — nnf_embedding • torch

A simple lookup table that looks up embeddings in a fixed dictionary and size.

Usage

nnf_embedding(
  input,
  weight,
  padding_idx = NULL,
  max_norm = NULL,
  norm_type = 2,
  scale_grad_by_freq = FALSE,
  sparse = FALSE
)

Arguments

input: (LongTensor) Tensor containing indices into the embedding matrix
weight: (Tensor) The embedding matrix with number of rows equal to the maximum possible index + 1, and number of columns equal to the embedding size
padding_idx: (int, optional) If given, pads the output with the embedding vector at padding_idx (initialized to zeros) whenever it encounters the index.
max_norm: (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.
norm_type: (float, optional) The p of the p-norm to compute for the max_norm option. Default 2.
scale_grad_by_freq: (boolean, optional) If given, this will scale gradients by the inverse of frequency of the words in the mini-batch. Default FALSE.
sparse: (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.

Details

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.