If it helps, you can have a look at my code for that. You only need the create_embedding_matrix method – load_glove and generate_embedding_matrix were my initial solution, but there’s not need to load and store all word embeddings, since you need only those that match your vocabulary. The word_to_index and max_index reflect the information from your vocabulary, with word_to_index mapping each word to a unique index from 0..max_index (not that I’ve written it, you probably don’t need max_index as an extra parameter). I use my own implementation of a vectorizer, but torchtext should give you similar information.

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torchtext.vocab ¶ Vocab ¶ class torchtext.vocab. Vocab ( vocab ) [source] ¶ __contains__ ( token : str ) → bool [source] ¶ Parameters :word_indices = torch.argmin(torch.abs(vec_seq.unsqueeze(1).expand(vs_new_size)- vecs.unsqueeze(0).expand(vec_new_size)).sum(dim=2),dim=1)

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RuntimeError – If index not in range [0, itos.size()). lookup_tokens ( indices : List [ int ] ) → List [ str ] [source] ¶ Parameters : Vocab ¶ class torchtext.vocab. Vocab ( counter, max_size=None, min_freq=1, specials=[''], vectors=None, unk_init=None, vectors_cache=None, specials_first=True ) ¶ Keep collections to yourself or inspire other shoppers! Keep in mind that anyone can view public collections - they may also appear in recommendations and other places.In Keras, you can load the GloVe vectors by having the Embedding layer constructor take a weights argument: # Keras code. Bright and Convenient LED Gloves Hands-free Torch Gloves: When you put on LED light gloves, even if you are working in a small dark space or doing outdoor activities at night, as long as you stretch out your hand with LED torch gloves, it can provide you with the necessary lighting. You no longer need to ask others to help you hold the torch, and you no longer need to use the torch in your mouth to illuminate your work, better free your hands, which can greatly improve work efficiency