Embedding Layers

Embedding layers map discrete tokens (words, categories, IDs) to dense vector representations. They are the foundation of NLP models and recommendation systems.

• Embedding: Standard lookup table that maps indices to dense vectors
• EmbeddingBag: Computes sums or means of embeddings (efficient for sparse features)

Key parameters:

• num_embeddings: Size of the vocabulary (number of unique tokens)
• embedding_dim: Dimension of each embedding vector
• padding_idx: Index that outputs zeros (useful for padding tokens)

Embedding

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Example:

auto embedding = torch::nn::Embedding(
    torch::nn::EmbeddingOptions(10000, 256)  // num_embeddings, embedding_dim
        .padding_idx(0));

auto indices = torch::tensor({1, 2, 3, 4});
auto embedded = embedding->forward(indices);  // [4, 256]

EmbeddingBag

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