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I thought the Field function build_vocab() just builds its vocabulary from the training data. How are the GloVe embeddings involved here during this step? Dictionary mapping tokens to indices. insert_token ( token : str, index : int ) → None [source] ¶ Parameters :

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GLOVE TORCH Flashlight LED torch Light Flashlight Tools Fishing Cycling Plumbing Hiking Camping THE TORCH YOU CANT DROP Gloves... Description 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)But you set freeze=True. So, if you don't plan to retrain the embedding layer, then you'd probably do best with: Vectors -> Indices def emb2indices(vec_seq, vecs): # vec_seq is size: [sequence, emb_length], vecs is size: [num_indices, emb_length]

PyTorch documentation — PyTorch 2.1 documentation PyTorch documentation — PyTorch 2.1 documentation

max_tokens – If provided, creates the vocab from the max_tokens - len(specials) most frequent tokens.RuntimeError – If an index within indices is not int range [0, itos.size()). set_default_index ( index : Optional [ int ] ) → None [source] ¶ Parameters :

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Silicone Button - LED light set in the head of thumb and index finger that covered by silicone, effective prevent water ingress when fishing or rain. These fishing gloves use 2 x CR2016 button batteries that can be replaced easily by loosen the screw with a screwdriver. Beyond the first result, none of the other words are even related to programming! In contrast, if we flip the gender terms, we get very different results: print_closest_words(glove['programmer'] - glove['woman'] + glove['man']) Perfect gift for man] Birthdays, Christmas, Father's Day gift for any DIY, handyman, father, boyfriend, men, or women. This is a practical and creative gift, which will definitely surprise themOr, try a different but related analogies along the gender axis: print_closest_words(glove['king'] - glove['prince'] + glove['princess']) generating vocab from text file >>> import io >>> from torchtext.vocab import build_vocab_from_iterator >>> def yield_tokens ( file_path ): >>> with io . open ( file_path , encoding = 'utf-8' ) as f : >>> for line in f : >>> yield line . strip () . split () >>> vocab = build_vocab_from_iterator ( yield_tokens ( file_path ), specials = [ "" ]) Vectors ¶ class torchtext.vocab. Vectors ( name, cache = None, url = None, unk_init = None, max_vectors = None ) [source] ¶ __init__ ( name, cache = None, url = None, unk_init = None, max_vectors = None ) → None [source] ¶ Parameters : RuntimeError – If token already exists in the vocab forward ( tokens : List [ str ] ) → List [ int ] [source] ¶