我有一个庞大的语料库，需要花费超过3天的时间来构建词汇表。因此，我真的需要重复使用这些词汇。然而，随着新版本的出现，如下面的代码片段所示，这变得不可能了：

vocab = gensim.models.word2vec.Word2VecVocab(min_count=1, sorted_vocab=True)
vocab.scan_vocab(sentences, progress_per=PROGRESS_PER, trim_rule=None)
vocab.save(VOCAB_FILE_NAME)

#LATER
vocab  = gensim.models.word2vec.Word2VecVocab.load(VOCAB_FILE_NAME) 
#Word2VecVocab object doesn't keep total_words and corpus_count info
#it is now impossible for me to retrieve these values. 
#These values are returned from scan_vocab instead of being saved as fields of the object

#Create an empty model. 
model = gensim.models.word2vec.Word2Vec(min_count=MIN_COUNT, max_vocab_size=MAX_VOCAB_SIZE, seed=SEED, sg=SG, workers=WORKERS, size=SIZE)

#prepare the vocabulary. This step is also very confusing. 
#I think prepare_vocab should belong to the model object since it actually modifies model's parameters
vocab.prepare_vocab(model.hs, model.negative, model.wv, min_count=MIN_COUNT,  keep_raw_vocab=False)

#This part is even messier. There is no way I could have known what to do next without having to go thru the source code. 
#model.build_vocab rescans the whole vocabulary again, rendering my first phase useless. 
#instead, I have to use model.trainables.prepare_weights to have a workaround. 
#Again, what does vocabulary=model.vocabulary do? 
model.trainables.prepare_weights(model.hs, model.negative, model.wv, update=False, vocabulary=model.vocabulary)

#Now, most importantly, how do I set total_examples parameter? and why is it named differently? 
#model.corpus_count returns zero, because the only way of setting it up is to use build_vocab, which will rescan the vocabulary
#since model.corpus_count is zero now, I get this error:
#File "/home/ahmed/anaconda3/lib/python3.5/site-packages/gensim/models/base_any2vec.py", line 135, in _job_producer
#    epoch_progress = 1.0 * pushed_words / total_words
#ZeroDivisionError: float division by zero
#---
#there is no way I can get the total_words info from the vocabulary without scanning it again
model.train(sentences=sentences, total_examples=model.corpus_count, total_words=model.corpus_count, epochs=model.iter)

model.save(MODEL_FILE_NAME)