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# -*- coding: utf-8 -*-
import torch
class Embedding(object):
def __init__(self, tokens, vectors, unk=None):
super(Embedding, self).__init__()
self.tokens = tokens
self.vectors = torch.tensor([v[0] for v in vectors])
print(self.vectors.size(0))
self.pretrained = {w: v for w, v in zip(tokens, vectors)}
self.unk = '[UNK]'
def __len__(self):
return len(self.tokens)
def __contains__(self, token):
return token in self.pretrained
@property
def dim(self):
return self.vectors.size(0)
@property
def unk_index(self):
if self.unk is not None:
return self.tokens.index(self.unk)
else:
raise AttributeError
@classmethod
def load(cls, path, unk=None):
with open(path, 'r') as f:
lines = [line for line in f]
splits = [line.split() for line in lines]
tokens, vectors = zip(*[(s[0], list(map(float, s[1:])))
for s in splits])
return cls(tokens, vectors, unk=unk)
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