gensim doc2vec tutorial

from gensim import models

sentence = models.doc2vec.LabeledSentence(
    words=[u'so`bme', u'words', u'here'], tags=["SENT_0"])
sentence1 = models.doc2vec.LabeledSentence(
    words=[u'here', u'we', u'go'], tags=["SENT_1"])

sentences = [sentence, sentence1]

class LabeledLineSentence(object):
    def __init__(self, filename):
        self.filename = filename
    def __iter__(self):
        for uid, line in enumerate(open(filename)):
            yield LabeledSentence(words=line.split(), labels=['SENT_%s' % uid])
            
model = models.Doc2Vec(alpha=.025, min_alpha=.025, min_count=1)
model.build_vocab(sentences)

for epoch in range(10):
    model.train(sentences)
    model.alpha -= 0.002  # decrease the learning rate`
    model.min_alpha = model.alpha  # fix the learning rate, no decay

model.save("my_model.doc2vec")
model_loaded = models.Doc2Vec.load('my_model.doc2vec')

print model.docvecs.most_similar(["SENT_0"])
print model_loaded.docvecs.most_similar(["SENT_1"])

@hswick You can use this class if you wish to read the sentences from a file. It is not used in the example since the example hard codes 2 sentences and uses them.

I have an error on line 21: model.train

"You must specify either total_examples or total_words, for proper alpha and progress calculations. "
ValueError: You must specify either total_examples or total_words, for proper alpha and progress calculations. The usual value is total_examples=model.corpus_count."

Please help me

How can I change "print model.docvecs.most_similar(["SENT_0"]) " if I am using the LabeledLineSentence class?