Here is my oversimplified and rather naive understanding of the difference: As we know, CBOW is learning to predict the word by the context. Or maximize the probability of the target word by looking at the context. And this happens to be a problem for rare words. For example, given the context yesterday was a … Read more
In recent versions, the model.wv property holds the words-and-vectors, and can itself can report a length – the number of words it contains. So if w2v_model is your Word2Vec (or Doc2Vec or FastText) model, it’s enough to just do: vocab_len = len(w2v_model.wv) If your model is just a raw set of word-vectors, like a KeyedVectors … Read more
If you want to train Doc2Vec model, your data set needs to contain lists of words (similar to Word2Vec format) and tags (id of documents). It can also contain some additional info (see https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/doc2vec-IMDB.ipynb for more information). # Import libraries from gensim.models import doc2vec from collections import namedtuple # Load data doc1 = [“This is … Read more
I find the previous answers here a bit overcomplicated – a projection layer is just a simple matrix multiplication, or in the context of NN, a regular/dense/linear layer, without the non-linear activation in the end (sigmoid/tanh/relu/etc.) The idea is to project the (e.g.) 100K-dimensions discrete vector into a 600-dimensions continuous vector (I chose the numbers … Read more
I use this code to load binary model, then save the model to text file, from gensim.models.keyedvectors import KeyedVectors model = KeyedVectors.load_word2vec_format(‘path/to/GoogleNews-vectors-negative300.bin’, binary=True) model.save_word2vec_format(‘path/to/GoogleNews-vectors-negative300.txt’, binary=False) References: API and nullege. Note: Above code is for new version of gensim. For previous version, I used this code: from gensim.models import word2vec model = word2vec.Word2Vec.load_word2vec_format(‘path/to/GoogleNews-vectors-negative300.bin’, binary=True) model.save_word2vec_format(‘path/to/GoogleNews-vectors-negative300.txt’, binary=False)
There are differet methods to get the sentence vectors : Doc2Vec : you can train your dataset using Doc2Vec and then use the sentence vectors. Average of Word2Vec vectors : You can just take the average of all the word vectors in a sentence. This average vector will represent your sentence vector. Average of Word2Vec … Read more
The idea of word2vec is to maximise the similarity (dot product) between the vectors for words which appear close together (in the context of each other) in text, and minimise the similarity of words that do not. In equation (3) of the paper you link to, ignore the exponentiation for a moment. You have v_c … Read more
This is actually a pretty challenging problem that you are asking. Computing sentence similarity requires building a grammatical model of the sentence, understanding equivalent structures (e.g. “he walked to the store yesterday” and “yesterday, he walked to the store”), finding similarity not just in the pronouns and verbs but also in the proper nouns, finding … Read more