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Produce new word2vec model from existing one


Produce new word2vec model from existing one

By : Ace Jackson
Date : October 25 2020, 07:10 PM
Any of those help When you're using a set of pre-trained vectors, like GoogleNews-vectors-negative300.bin.gz, the creator of those vectors determined what words, with what case-handling, are included.
Once loaded, lookup in such a model is by exact, case-sensitive string matching.
code :


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Is it possible to use gensim word2vec model in deeplearning4j.word2vec?

Is it possible to use gensim word2vec model in deeplearning4j.word2vec?


By : Shay Kennedy
Date : March 29 2020, 07:55 AM
I wish did fix the issue. Yes, it's possible since Word2Vec implementation defines a standard to structure its model.
To do this:
code :
w2v_model.wv.save_word2vec_format("path/to/w2v_model.bin", binary=True)
Word2Vec w2vModel = WordVectorSerializer.readWord2VecModel("path/to/w2v_model.bin");
print(w2v_model.most_similar("love"))
print(w2v_model.n_similarity(["man"], ["king"]))
System.out.println(w2vModel.wordsNearest("love", 10));
System.out.println(w2vModel.similarity("man", "king"));
Word2vec saved model is not UTF-8 encoded but the sentence input to the Word2vec model is UTF-8 encoded

Word2vec saved model is not UTF-8 encoded but the sentence input to the Word2vec model is UTF-8 encoded


By : sandi
Date : March 29 2020, 07:55 AM
This might help you Are you using the latest gensim? If not, be sure to try it – there have sometimes been save()/load() bugs in older versions.
The INFO "not storing" log lines are normal – they're not indicative of any problem (and thus could be deleted from your question.)
After loading a pretrained Word2Vec model, how do I get word2vec representations of new sentences?

After loading a pretrained Word2Vec model, how do I get word2vec representations of new sentences?


By : jkgz
Date : March 29 2020, 07:55 AM
Does that help Word2Vec only offers vector representations for words, not sentences.
One crude but somewhat effective (for some purposes) way to go from word-vectors to vectors for longer texts (like sentences) is to average all the word-vectors together. This isn't a function of the gensim Word2Vec class; you have to code this yourself.
code :
import numpy as np

sentence_tokens = "I do not like green eggs and ham".split()
sum_vector = np.zeros(word_model.vector_size)
for token in sentence_tokens:
    sum_vector += word_model[token]
sentence_vector = sum_vector / len(sentence_tokens)
In Gensim Word2vec, how to reduce the vocab size of an existing model?

In Gensim Word2vec, how to reduce the vocab size of an existing model?


By : user2965212
Date : March 29 2020, 07:55 AM
To fix the issue you can do In Gensims word2vec api, I trained a model where I initialized the model with max_final_vocab = 100000 and saved the model using model.save() (This gives me one .model file, one .model.trainables.syn1neg.npy and one .model.wv.vectors.npy file).
gensim - Word2vec continue training on existing model - AttributeError: 'Word2Vec' object has no attribute 'compute_loss

gensim - Word2vec continue training on existing model - AttributeError: 'Word2Vec' object has no attribute 'compute_loss


By : Sham Shahar
Date : March 29 2020, 07:55 AM
I wish did fix the issue. I am trying to continue training on an existing model, , Here is how I continues training my model
code :
# training_data: initial training data. contain list of tokenized sentences
model = Word2Vec(training_data, size=50, window=5, min_count=10, workers=4)

# datasmall: more sentences
# total_examples: number of additional sentence
# epochs: provide your current epochs. model.epochs is ok 
model.train(datasmall, total_examples=len(datasmall), epochs=model.epochs)
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