word2vec

论文来自Mikolov等人的《Efficient Estimation of Word Representations in Vector Space》 论文地址： 66666 论文介绍了2个方法，原理不解释... skim code and comment : # -*- coding: utf-8 -*- # @time : 2019/11/9 12:53 import numpy as np import torch import torch.nn as nn import torch.optim as optim from torch.autograd import Variable import matplotlib.pyplot as plt dtype = torch.FloatTensor # 3 Words Sentence sentences = [ "i like dog", "i like cat", "i like animal", "dog cat animal", "apple cat dog like", "dog fish milk like", "dog cat eyes like", "i like apple", "apple i hate", "apple i movie book music like", "cat dog hate",

I have an generator (a function that yields stuff), but when trying to pass it to gensim.Word2Vec I get the following error: TypeError: You can't pass a generator as the sentences argument. Try an iterator. Isn't a generator a kind of iterator? If not, how do I make an iterator from it? Looking at the library code, it seems to simply iterate over sentences like for x in enumerate(sentences) , which works just fine with my generator. What is causing the error then? Generator is exhausted after one loop over it. Word2vec simply needs to traverse sentences multiple times (and probably get item

I'm using gensim implementation of Word2Vec. I have the following code snippet: print('training model') model = Word2Vec(Sentences(start, end)) print('trained model:', model) print('vocab:', model.vocab.keys()) When I run this in python2, it runs as expected. The final print is all the words in the vocabulary. However, if I run it in python3, I get an error: trained model: Word2Vec(vocab=102, size=100, alpha=0.025) Traceback (most recent call last): File "learn.py", line 58, in <module> train(to_datetime('-4h'), to_datetime('now'), 'model.out') File "learn.py", line 23, in train print('vocab:'

I am solving a text classification problem. I defined my classifier using the Estimator class with my own model_fn . I would like to use Google's pre-trained word2vec embedding as initial values and then further optimise it for the task at hand. I saw this post: Using a pre-trained word embedding (word2vec or Glove) in TensorFlow which explains how to go about it in 'raw' TensorFlow code. However, I would really like to use the Estimator class. As an extension, I would like to then train this code on Cloud ML Engine, is there a good way of passing in the fairly large file with initial values?

i am going thorugh this paper http://cs.stanford.edu/~quocle/paragraph_vector.pdf and it states that " Theparagraph vector and word vectors are averaged or concatenated to predict the next word in a context. In the experiments, we use concatenation as the method to combine the vectors." How does concatenation or averaging work? example (if paragraph 1 contain word1 and word2): word1 vector =[0.1,0.2,0.3] word2 vector =[0.4,0.5,0.6] concat method does paragraph vector = [0.1+0.4,0.2+0.5,0.3+0.6] ? Average method does paragraph vector = [(0.1+0.4)/2,(0.2+0.5)/2,(0.3+0.6)/2] ? Also from this