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import torch
from torchnlp.word_to_vector.pretrained_word_vectors import _PretrainedWordVectors
[docs]class CharNGram(_PretrainedWordVectors):
""" Character n-gram is a character-based compositional model to embed textual sequences.
Character n-gram embeddings are trained by the same Skip-gram objective. The final character
embedding is the average of the unique character n-gram embeddings of wt. For example, the
character n-grams (n = 1, 2, 3) of the word “Cat” are {C, a, t, #B#C, Ca, at, t#E#, #B#Ca, Cat,
at#E#}, where “#B#” and “#E#” represent the beginning and the end of each word, respectively.
Using the character embeddings efficiently provides morphological features. Each word is
subsequently represented as xt, the concatenation of its corresponding word and character
embeddings shared across the tasks.
**Reference:**
http://www.logos.t.u-tokyo.ac.jp/~hassy/publications/arxiv2016jmt/
Args:
cache (str, optional): directory for cached vectors
unk_init (callback, optional): by default, initialize out-of-vocabulary word vectors
to zero vectors; can be any function that takes in a Tensor and
returns a Tensor of the same size
is_include (callable, optional): callable returns True if to include a token in memory
vectors cache; some of these embedding files are gigantic so filtering it can cut
down on the memory usage. We do not cache on disk if ``is_include`` is defined.
Example:
>>> from torchnlp.word_to_vector import CharNGram # doctest: +SKIP
>>> vectors = CharNGram() # doctest: +SKIP
>>> vectors['hello'] # doctest: +SKIP
-1.7494
0.6242
...
-0.6202
2.0928
[torch.FloatTensor of size 100]
"""
name = 'charNgram.txt'
url = ('https://www.logos.t.u-tokyo.ac.jp/~hassy/publications/arxiv2016jmt/'
'jmt_pre-trained_embeddings.tar.gz')
def __init__(self, **kwargs):
super(CharNGram, self).__init__(self.name, url=self.url, **kwargs)
def __getitem__(self, token):
vector = torch.Tensor(self.dim).zero_()
# These literals need to be coerced to unicode for Python 2 compatibility
# when we try to join them with read ngrams from the files.
chars = ['#BEGIN#'] + list(token) + ['#END#']
num_vectors = 0
for n in [2, 3, 4]:
end = len(chars) - n + 1
grams = [chars[i:(i + n)] for i in range(end)]
for gram in grams:
gram_key = '{}gram-{}'.format(n, ''.join(gram))
if gram_key in self.token_to_index:
vector += self.vectors[self.token_to_index[gram_key]]
num_vectors += 1
if num_vectors > 0:
vector /= num_vectors
else:
vector = self.unk_init(vector)
return vector