3
$\begingroup$

I've been using Tensorflow and just started learning PyTorch. I was following the tutorial: https://pytorch.org/tutorials/beginner/nlp/word_embeddings_tutorial.html#sphx-glr-beginner-nlp-word-embeddings-tutorial-py

Where we try to create an n-gram language model. However, there's something I don't understand.

class NGramLanguageModeler(nn.Module):

    def __init__(self, vocab_size, embedding_dim, context_size):
        super(NGramLanguageModeler, self).__init__()
        self.embeddings = nn.Embedding(vocab_size, embedding_dim)
        self.linear1 = nn.Linear(context_size * embedding_dim, 128)
        self.linear2 = nn.Linear(128, vocab_size)

at self.linear1 = nn.Linear(context_size * embedding_dim, 128) why did we multiply embedding_dim with context_size? Isn't the embedding_dim input size? So why do we multiply it by the context size?

$\endgroup$
3
$\begingroup$

An n-gram language model is a language model trained with n context words. This means you're not feeding the model a single word but n. This is why the dimension of the input layer is "context_size * embedding_dim" or "n * embedding_dims"

$\endgroup$
0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.