min_alpha=0.00025,
min_count=1,
dm=1,
epoch=max_epochs,
workers=multiprocessing.cpu_count())
model.build_vocab([x for x in tqdm_notebook(tagged_data)])
for epoch in tqdm_notebook(range(max_epochs)):
model.train(tagged_data,
total_examples=model.corpus_count,
epochs=model.iter)
# decrease the learning rate
model.alpha -= 0.0002
# fix the learning rate, no decay
model.min_alpha = model.alpha
model.save("d2v.model")
print("Model Saved")
# In[376]:
tagged_data = pd.DataFrame(
TaggedDocument(words=word_tokenize(i.lower()), tags=[str(_d)])
for i, _d in zip(classic_raw.values.ravel(), encoded_label_classic))
# In[377]:
y_train, X_train = vec_for_learning(model, tagged_data)
# In[378]:
