How to load a word embedding dictionary using torchtext

[nawshad]

Hi,

I have tried to write that to a gensim word2vec format then load, but it throws error about string to float conversion. Is there a standard way to use custom pre-trained embedding (not created through gensim) which is a python dictionary to load using torchtext?

Thanks,

[zhangguanheng66]

@bentrevett @mttk Any ideas for this issue. I think we support the pretrained word vector in torchtext.

[bentrevett]

There is a way to load custom embeddings from a file, so you can write your dictionary to a file and then read it with TorchText.

import torchtext.vocab as vocab

custom_embeddings = vocab.Vectors(name = 'custom_embeddings.txt')

The format of your custom_embeddings.txt file needs to be the token followed by the values of each of the dimensions for the embedding, all separated by a single space, e.g. here's three tokens with 20 dimensional embeddings (all just ones as an example):

good 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
great 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
awesome 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

You then align these with your vocabulary when you build_vocab for the desired Field:

TEXT.build_vocab(train_data, vectors = custom_embeddings)

Then you actually load these pre-trained embeddings into your model with:

model.embedding.weight.data.copy_(TEXT.vocab.vectors)