pdfcoffee

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Chapter 7
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We are now ready to create our word embedding model. The gensim package
offers a simple API that allows us to declaratively create and train a Word2Vec
model, using the following code. The trained model will be serialized to the file
given by W2V_MODEL_FILE. The Documents class allows us to stream large input files
to train the Word2Vec model without running into memory issues. We will train
the Word2Vec model in skip-gram mode with a window size of 10, which means we
train it to predict up to five neighboring vertices given a central vertex. The resulting
embedding for each vertex is a dense vector of size 128:
W2V_MODEL_FILE = os.path.join(DATA_DIR, "w2v-neurips-papers.model")
class Documents(object):
def __init__(self, input_file):
self.input_file = input_file
def __iter__(self):
with open(self.input_file, "r") as f:
for i, line in enumerate(f):
if i % 1000 == 0:
if i % 1000 == 0:
logging.info(
"{:d} random walks extracted".format(i))
yield line.strip().split()
def train_word2vec_model(random_walks_file, model_file):
if os.path.exists(model_file):
print("Model file {:s} already present, skipping training"
.format(model_file))
return
docs = Documents(random_walks_file)
model = gensim.models.Word2Vec(
docs,
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