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 (e.g 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
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...
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.
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:
It looks like this improvement could allow updating the GoogleNews-vectors-negative300.bin model.
来源：https://stackoverflow.com/questions/35117491/is-it-possible-to-re-train-a-word2vec-model-e-g-googlenews-vectors-negative300