build_vocab with custom word embedding

Hi 😃 I want to use customized bio-word embedding to do some text classification.

And I can’t find how.

Some old tutorial says there is ‘wv_dir’ keyword argument, which I tried and failed :

TypeError                                 Traceback (most recent call last)
<ipython-input-48-ac09f554719e> in <module>()
      1 test_field = data.Field()
      2 lang_data = datasets.LanguageModelingDataset(path='pr_data/processed_neg.txt',text_field=test_field)
----> 3 voc = torchtext.vocab.Vocab(wv_dir='bio_wordemb/PubMed-and-PMC-w2v.txt')
      4 
      5 # test_field.build_vocab(lang_data,wv_dir='bio_wordemb/PubMed-and-PMC-w2v.txt')

TypeError: __init__() got an unexpected keyword argument 'wv_dir'

Just like we can load pretrained GloVe embedding using TEXTFIELD.build_vocab(data, vectors='glove.6B.100d'), is there similar way to load customized embedding?

Any help would be much appreciated. Thanks!

[bebound]

This works, xxx.vecshould be the standard word2vec format file.

from torchtext.vocab import Vectors

vectors = Vectors(name='xxx.vec', cache='./')
TEXT.build_vocab(train, val, test, vectors=vectors)

[pablogps]

Yes, you are right! There is a bit more info here.