<jupyter_start><jupyter_text>Train Model<jupyter_code>import gensim, time
from gensim.models.word2vec import Word2Vec

start = time.time()

print("Training Model. This could take a while (10-60 mins for moderate collections). Get a coffee")
model = Word2Vec(tokenized, iter=TRAINING_ITERATIONS, size=VECTOR_SIZE, window=WINDOW_SIZE, min_count=MIN_WD_COUNT, workers=WORKERS, sample=1e-5, hs=0, negative=20)
model.save(MODEL_FILE)
end = time.time()
print "Took %s seconds" % (end - start)
#find the top n similar terms as below:
#model.most_similar(positive="hadoop developer",topn=10)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11582381729200653, 'fraction_numerical': 0.022838499184339316, 'mean_word_length': 7.527777777777778, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '337412', 'license': 'Apache-2.0', 'n_tokens_mistral': 227, 'n_tokens_neox': 215, 'n_words': 54, 'path': 'jupyter notebooks/Conceptual Search - 3. Create Word2Vec Model.ipynb', 'repo_name': 'DiceTechJobs/ConceptualSearch', 'size': '613'}