text_plain_output_1.png
from gensim.models import Word2Vec

max_seq_len = 458

gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
    news_desc_list = []
    for tok in chunk_data[record].split():
        news_desc_list.append(str(tok))
    gensim_news_desc.append(news_desc_list)

vsize = max_seq_len
vmin_count = 4
gensim_model = Word2Vec(gensim_news_desc, min_count=vmin_count, size=vsize, sg=1)
print(gensim_model)