a minimal example of word vector in gensim

wordvector in gensim.ipynb

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      "id": "important-tractor",
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      "source": "from nltk.tokenize import sent_tokenize, word_tokenize \nimport gensim \nfrom gensim.models import Word2Vec\nimport warnings \nwarnings.filterwarnings(action = 'ignore') \nimport urllib.request",
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      "source": "url = \"http://www.gutenberg.org/files/11/11-0.txt\"\ntext = urllib.request.urlopen(url).read().decode()\ntext.replace(\"\\n\",\" \")",
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      "source": "text_seq = []\nfor sent in sent_tokenize(text):\n    temp = [w.lower() for w in word_tokenize(sent)]\n    text_seq.append(temp)",
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      "source": "# Create CBOW model \nmodel1 = gensim.models.Word2Vec(text_seq, min_count = 1,  \n                              size = 100, window = 5) \n  \n# Print results \nprint(\"Cosine similarity between 'alice' \" + \n               \"and 'wonderland' - CBOW : \", \n    model1.similarity('alice', 'wonderland')) \n      \nprint(\"Cosine similarity between 'alice' \" +\n                 \"and 'machines' - CBOW : \", \n      model1.similarity('alice', 'machines'))",
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      "source": "# Create Skip Gram model \nmodel2 = gensim.models.Word2Vec(text_seq, min_count = 1, size = 100, \n                                             window = 5, sg = 1) \n  \n# Print results \nprint(\"Cosine similarity between 'alice' \" +\n          \"and 'wonderland' - Skip Gram : \", \n    model2.similarity('alice', 'wonderland')) \n      \nprint(\"Cosine similarity between 'alice' \" +\n            \"and 'machines' - Skip Gram : \", \n      model2.similarity('alice', 'machines')) ",
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