nlp-neural-word-embeddings.ipynb

{
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    {
     "name": "stdout",
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     "text": [
      "--2024-03-14 17:52:46--  https://archive.org/download/google-news-vectors-negative-300.bin_202311/GoogleNews-vectors-negative300.bin.gz\r\n",
      "Resolving archive.org (archive.org)... 207.241.224.2\r\n",
      "Connecting to archive.org (archive.org)|207.241.224.2|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 302 Found\r\n",
      "Location: https://ia801203.us.archive.org/18/items/google-news-vectors-negative-300.bin_202311/GoogleNews-vectors-negative300.bin.gz [following]\r\n",
      "--2024-03-14 17:52:46--  https://ia801203.us.archive.org/18/items/google-news-vectors-negative-300.bin_202311/GoogleNews-vectors-negative300.bin.gz\r\n",
      "Resolving ia801203.us.archive.org (ia801203.us.archive.org)... 207.241.228.23\r\n",
      "Connecting to ia801203.us.archive.org (ia801203.us.archive.org)|207.241.228.23|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 1647046227 (1.5G) [application/octet-stream]\r\n",
      "Saving to: 'GoogleNews-vectors-negative300.bin.gz'\r\n",
      "\r\n",
      "GoogleNews-vectors- 100%[===================>]   1.53G   177KB/s    in 2h 0m   \r\n",
      "\r\n",
      "2024-03-14 19:53:43 (222 KB/s) - 'GoogleNews-vectors-negative300.bin.gz' saved [1647046227/1647046227]\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "# Slower but always guaranteed to work\n",
    "!wget -nc https://archive.org/download/google-news-vectors-negative-300.bin_202311/GoogleNews-vectors-negative300.bin.gz\n",
    "\n",
    "# You are better off just downloading this from the source\n",
    "# https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing\n",
    "# https://code.google.com/archive/p/word2vec/"
   ]
  },
  {
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   "id": "3948f16a",
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    "tags": []
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   "outputs": [],
   "source": [
    "# import gdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "247ae69d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-14T19:53:53.702171Z",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !gdown https://drive.google.com/uc?id=0B7XkCwpI5KDYNlNUTTlSS21pQmM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4683b862",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-14T19:53:58.600128Z",
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    "tags": []
   },
   "outputs": [],
   "source": [
    "!gunzip GoogleNews-vectors-negative300.bin.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76bbb9ad",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70da534c",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "word_vectors = KeyedVectors.load_word2vec_format(\n",
    "  'GoogleNews-vectors-negative300.bin',\n",
    "  binary=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfcf94d6",
   "metadata": {
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   "outputs": [],
   "source": [
    "def find_analogies(w1, w2, w3):\n",
    "  # w1 - w2 = ? - w3\n",
    "  # e.g. king - man = ? - woman\n",
    "  #      ? = +king +woman -man\n",
    "  r = word_vectors.most_similar(positive=[w1, w3], negative=[w2])\n",
    "  print(\"%s - %s = %s - %s\" % (w1, w2, r[0][0], w3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8ce8328",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "king - man = queen - woman\n"
     ]
    }
   ],
   "source": [
    "find_analogies('king', 'man', 'woman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "597a24c4",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "france - paris = england - london\n"
     ]
    }
   ],
   "source": [
    "find_analogies('france', 'paris', 'london')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5d786bab",
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    {
     "name": "stdout",
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     "text": [
      "france - paris = italy - rome\n"
     ]
    }
   ],
   "source": [
    "find_analogies('france', 'paris', 'rome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d6ebab55",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "paris - france = lohan - italy\n"
     ]
    }
   ],
   "source": [
    "find_analogies('paris', 'france', 'italy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc2c0ada",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "france - french = england - english\n"
     ]
    }
   ],
   "source": [
    "find_analogies('france', 'french', 'english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0589121d",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "japan - japanese = tibet - chinese\n"
     ]
    }
   ],
   "source": [
    "find_analogies('japan', 'japanese', 'chinese')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd754906",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def nearest_neighbors(w):\n",
    "  r = word_vectors.most_similar(positive=[w])\n",
    "  print(\"neighbors of: %s\" % w)\n",
    "  for word, score in r:\n",
    "    print(\"\\t%s\" % word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1b72544",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors of: king\n",
      "\tkings\n",
      "\tqueen\n",
      "\tmonarch\n",
      "\tcrown_prince\n",
      "\tprince\n",
      "\tsultan\n",
      "\truler\n",
      "\tprinces\n",
      "\tPrince_Paras\n",
      "\tthrone\n"
     ]
    }
   ],
   "source": [
    "nearest_neighbors('king')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26441c9a",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors of: france\n",
      "\tspain\n",
      "\tfrench\n",
      "\tgermany\n",
      "\teurope\n",
      "\titaly\n",
      "\tengland\n",
      "\teuropean\n",
      "\tbelgium\n",
      "\tusa\n",
      "\tserbia\n"
     ]
    }
   ],
   "source": [
    "nearest_neighbors('france')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56893057",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors of: japan\n",
      "\tjapanese\n",
      "\ttokyo\n",
      "\tamerica\n",
      "\teurope\n",
      "\tgermany\n",
      "\tchinese\n",
      "\tindia\n",
      "\thawaii\n",
      "\tusa\n",
      "\tkorea\n"
     ]
    }
   ],
   "source": [
    "nearest_neighbors('japan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72d5294c",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors of: einstein\n",
      "\tnikki\n",
      "\tlmfao\n",
      "\talbert\n",
      "\tarmstrong\n",
      "\tjoan\n",
      "\tbecky\n",
      "\tmcmahon\n",
      "\tconrad\n",
      "\tlori\n",
      "\thaley\n"
     ]
    }
   ],
   "source": [
    "nearest_neighbors('einstein')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "236a9585",
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "neighbors of: woman\n",
      "\tman\n",
      "\tgirl\n",
      "\tteenage_girl\n",
      "\tteenager\n",
      "\tlady\n",
      "\tteenaged_girl\n",
      "\tmother\n",
      "\tpolicewoman\n",
      "\tboy\n",
      "\tWoman\n"
     ]
    }
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   ]
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