def make_word_embeddings(opt, word_dict, fields):
word_padding_idx = word_dict.stoi[table.IO.PAD_WORD]
num_word = len(word_dict)
emb_word = nn.Embedding(num_word,
opt.word_vec_size,
padding_idx=word_padding_idx)
return emb_word
if len(opt.pre_word_vecs) > 0:
vectors = torchtext.vocab.GloVe(name="840B",
cache=opt.pre_word_vecs,
dim=str(opt.word_vec_size))
fields["src"].vocab.load_vectors(vectors)
emb_word.weight.data.copy_(fields["src"].vocab.vectors)
if opt.fix_word_vecs:
# <unk> is 0
num_special = len(table.IO.special_token_list)
# zero vectors in the fixed embedding (emb_word)
emb_word.weight.data[:num_special].zero_()
emb_special = nn.Embedding(num_special,
opt.word_vec_size,
padding_idx=word_padding_idx)
emb = PartUpdateEmbedding(num_special, emb_special, emb_word)
return emb
else:
return emb_word
def make_word_embeddings(opt, word_dict, fields):
word_padding_idx = word_dict.stoi[table.IO.PAD_WORD]
num_word = len(word_dict)
emb_word = nn.Embedding(num_word,
opt.word_vec_size,
padding_idx=word_padding_idx)
if len(opt.pre_word_vecs) > 0:
if opt.word_vec_size == 150:
dim_list = ['100', '50']
elif opt.word_vec_size == 250:
dim_list = ['200', '50']
else:
dim_list = [
str(opt.word_vec_size),
]
vectors = [
torchtext.vocab.GloVe(name="6B", cache=opt.pre_word_vecs, dim=it)
for it in dim_list
]
word_dict.load_vectors(vectors)
emb_word.weight.data.copy_(word_dict.vectors)
if opt.fix_word_vecs:
# <unk> is 0
num_special = len(table.IO.special_token_list)
# zero vectors in the fixed embedding (emb_word)
emb_word.weight.data[:num_special].zero_()
emb_special = nn.Embedding(num_special,
opt.word_vec_size,
padding_idx=word_padding_idx)
emb = PartUpdateEmbedding(num_special, emb_special, emb_word)
return emb
else:
return emb_word
def make_word_embeddings(args, vocab: torchtext.vocab.Vocab):
word_padding_idx = vocab.stoi[table.IO.PAD_WORD]
num_word = len(vocab)
emb_word = nn.Embedding(num_word,
args.word_emb_size,
padding_idx=word_padding_idx)
logger.info(" * using embeddings [%s]" % args.word_embeddings)
if args.word_embeddings != '': # TODO: might get rid of this check?
# load custom embeddings
if args.use_custom_embeddings:
logger.info(" * loading custom embeddings")
vocab.load_vectors(
vectors=[load_glove_fine_tuned(args, get_only_dict=False)])
emb_word.weight.data.copy_(vocab.vectors)
else:
logger.info(" * using default embeddings")
if args.word_emb_size == 150:
dim_list = ['100', '50']
elif args.word_emb_size == 250:
dim_list = ['200', '50']
else:
dim_list = [
str(args.word_emb_size),
]
vectors = [
torchtext.vocab.GloVe(name="6B",
cache=args.word_embeddings,
dim=it) for it in dim_list
]
vocab.load_vectors(vectors)
emb_word.weight.data.copy_(vocab.vectors)
# ---
if args.fix_word_vecs:
# <unk> is 0
num_special = len(table.IO.SPECIAL_TOKEN_LIST)
# zero vectors in the fixed embedding (emb_word)
emb_word.weight.data[:num_special].zero_()
emb_special = nn.Embedding(num_special,
args.word_emb_size,
padding_idx=word_padding_idx)
emb = PartUpdateEmbedding(num_special, emb_special, emb_word)
return emb
else:
return emb_word
def make_word_embeddings(opt, word_dict, fields):
word_padding_idx = word_dict.stoi[table.IO.PAD_WORD]
num_word = len(word_dict)
print(opt.word_vec_size)
# in pytorch, nn.Embedding is not a nn. nn.Embedding is a num_word*opt.word_vec_size matrix
emb_word = nn.Embedding(num_word,
opt.word_vec_size,
padding_idx=word_padding_idx)
if len(opt.pre_word_vecs) > 0:
# torchtext.vocab.GloVe(): if there is a file name:glove.{name}.{dim}d.txt.pt it will load it
# If there is not *.pt file, it will take glove.{name}.{dim}d.txt instead
vectors = torchtext.vocab.GloVe(name="840B",
cache=opt.pre_word_vecs,
dim=str(opt.word_vec_size))
# the emb_word hold the same dimension as the 840B
# Although there are a lot of word vectors in GloVe(), there are many words from dataset not in GloVe()
# such as: state/territory, text/background etc. These word vectors that not in GloVe() will be [0,0,...,0] as same as special token.
fields["src"].vocab.load_vectors(vectors)
emb_word.weight.data.copy_(
fields["src"].vocab.vectors
) # we define the emb_word.weight is the same as the fields["src"].vocab.vectors
if opt.fix_word_vecs: # fix_word_vecs will be true
# <unk> is 0
num_special = len(table.IO.special_token_list)
# zero vectors in the fixed embedding (emb_word). Actually it is zero in GloVe vector since the token is not a word
emb_word.weight.data[:num_special].zero_()
emb_special = nn.Embedding(num_special,
opt.word_vec_size,
padding_idx=word_padding_idx)
emb = PartUpdateEmbedding(num_special, emb_special, emb_word)
return emb
else:
return emb_word