是否可以从python句子集中重新训练word2vec模型（例如GoogleNews-vectors-negative300.bin）？

Question

I am using pre-trained Google news dataset for getting word vectors by using Gensim library in python

model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)

After loading the model I am converting training reviews sentence words into vectors

#reading all sentences from training file
with open('restaurantSentences', 'r') as infile:
x_train = infile.readlines()
#cleaning sentences
x_train = [review_to_wordlist(review,remove_stopwords=True) for review in x_train]
train_vecs = np.concatenate([buildWordVector(z, n_dim) for z in x_train])

During word2Vec process i get a lot of errors for the words in my corpus, that are not in the model. Problem is how can i retrain already pre-trained model (eg GoogleNews-vectors-negative300.bin'), in order to get word vectors for those missing words.

Following is what I have tried: Trained a new model from training sentences that I had

# Set values for various parameters
num_features = 300    # Word vector dimensionality                      
min_word_count = 10   # Minimum word count                        
num_workers = 4       # Number of threads to run in parallel
context = 10          # Context window    size                                                                                    
downsampling = 1e-3   # Downsample setting for frequent words

sentences = gensim.models.word2vec.LineSentence("restaurantSentences")
# Initialize and train the model (this will take some time)
print "Training model..."
model = gensim.models.Word2Vec(sentences, workers=num_workers,size=num_features, min_count = min_word_count, 
                      window = context, sample = downsampling)


model.build_vocab(sentences)
model.train(sentences)
model.n_similarity(["food"], ["rice"])

It worked! but the problem is that I have a really small dataset and less resources to train a large model.

Second way that I am looking at is to extend the already trained model such as GoogleNews-vectors-negative300.bin.

model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)
sentences = gensim.models.word2vec.LineSentence("restaurantSentences")
model.train(sentences)

Is it possible and is that a good way to use, please help me out

Answer 1

This is how I technically solved the issue:

Preparing data input with sentence iterable from Radim Rehurek: https://rare-technologies.com/word2vec-tutorial/

sentences = MySentences('newcorpus')  

Setting up the model

model = gensim.models.Word2Vec(sentences)

Intersecting the vocabulary with the google word vectors

model.intersect_word2vec_format('GoogleNews-vectors-negative300.bin',
                                lockf=1.0,
                                binary=True)

Finally executing the model and updating

model.train(sentences)

A note of warning: From a substantive point of view, it is of course highly debatable whether a corpus likely to be very small can actually "improve" the Google wordvectors trained on a massive corpus...

Answer 2

it is possible if model builder didn't finalize the model training . in python it is:

model.sims(replace=True) #finalize the model

if the model didn't finalize it is a perfect way to have model with large dataset.

Answer 3

Some folks have been working on extending gensim to allow online training.

A couple GitHub pull requests you might want to watch for progress on that effort:

• https://github.com/piskvorky/gensim/pull/435
• https://github.com/piskvorky/gensim/pull/615

It looks like this improvement could allow updating the GoogleNews-vectors-negative300.bin model.