application_vnd.jupyter.stderr_output_1.png
from gensim.models import KeyedVectors

from gensim.models import Word2Vec

from nltk.tokenize import RegexpTokenizer

import pandas as pd

import gensim
from gensim.models import Word2Vec
from gensim.models import KeyedVectors
import pandas as pd
from nltk.tokenize import RegexpTokenizer
forum_posts = pd.read_csv('../input/meta-kaggle/ForumMessages.csv')

sentences = forum_posts.Message.astype('str').tolist()
tokenizer = RegexpTokenizer('\\w+')
sentences_tokenized = [w.lower() for w in sentences]
sentences_tokenized = [tokenizer.tokenize(i) for i in sentences_tokenized]

model = KeyedVectors.load_word2vec_format('../input/word2vec-google/GoogleNews-vectors-negative300.bin', binary=True)

model_2 = Word2Vec(size=300, min_count=1)
model_2.build_vocab(sentences_tokenized)
total_examples = model_2.corpus_count
model_2.build_vocab([list(model.vocab.keys())], update=True)
model_2.intersect_word2vec_format('../input/word2vec-google/GoogleNews-vectors-negative300.bin', binary=True)
model_2.train(sentences_tokenized, total_examples=total_examples, epochs=model_2.iter)