iter parameter in the
gensim Word2Vec implementation
class gensim.models.word2vec.Word2Vec(sentences=None, size=100, alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=0, seed=1, workers=1, min_alpha=0.0001, sg=1, hs=1, negative=0, cbow_mean=0, hashfxn=<built-in function hash>, **iter=1**, null_word=0, trim_rule=None, sorted_vocab=1)
that specifies the number of epochs, i.e.:
iter = number of iterations (epochs) over the corpus.
Does anyone know whether that helps in improving the model over the corpus?
Is there any reason why the
iter is set to 1 by default? Is there not much effect in increasing the no. of epochs?
Is there any scientific/empirical evaluation of how to set the no. of epochs?
Unlike classification/regression task, the grid search method wouldn't really work since the vectors are generated in an unsupervised manner and the objective function is simply by either hierarchical softmax or negative sampling.
Is there an early stopping mechanism to cut short the no. of epochs once vectors converges? And can the hierarchical softmax or negative sampling objective converge?