Is there a way I can access just the vocabulary list of pre-trained vectors for word2vec and GloVe? I do not need the entire n-dimensional embeddings.

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For the word2vec models, you probably can load them with the gensim package and access the vocabulary using wv.vocab property. Like this:

from gensim.models.keyedvectors import KeyedVectors

model = KeyedVectors.load_word2vec_format(filename, binary=True)
words = model.wv.vocab

where filename is the path to the pretrained model. binary should be False if the pretrained model is in a text representation.

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