NLP/word2vec.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "44214df4",
      "metadata": {
        "id": "44214df4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1527f449-fade-4692-c8ac-b7abd3445940"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 29.8M  100 29.8M    0     0  11.5M      0  0:00:02  0:00:02 --:--:-- 11.5M\n"
          ]
        }
      ],
      "source": [
        "!curl -O http://mattmahoney.net/dc/text8.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "337594b8",
      "metadata": {
        "id": "337594b8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68566545-f227-427b-c422-28efc56a23ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  text8.zip\n",
            "  inflating: text8                   \n"
          ]
        }
      ],
      "source": [
        "!unzip text8.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "11ce21d5",
      "metadata": {
        "id": "11ce21d5"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "from collections import Counter\n",
        "from tqdm.notebook import tqdm\n",
        "import numpy as np\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.optim as optim"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = []"
      ],
      "metadata": {
        "id": "0NZahTYgaIR1"
      },
      "id": "0NZahTYgaIR1",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenize = lambda item: item.lower().split()"
      ],
      "metadata": {
        "id": "_ADiyYEpaIPu"
      },
      "id": "_ADiyYEpaIPu",
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "with open('text8', 'r') as file:\n",
        "    for line in file:\n",
        "        tokens += tokenize(line)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbYpNtcZaINZ",
        "outputId": "a262ae4f-ab57-4da9-afe1-2e33e833cc88"
      },
      "id": "TbYpNtcZaINZ",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.43 s, sys: 877 ms, total: 2.3 s\n",
            "Wall time: 3.28 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words_freq = Counter(tokens)\n",
        "words_freq = Counter({k: c for k, c in words_freq.items() if c >= 20}).most_common(200_000)\n",
        "\n",
        "vocabulary = [key for key, _ in words_freq]\n",
        "unk_token = '<UNK>'\n",
        "vocabulary.append(unk_token)"
      ],
      "metadata": {
        "id": "QTTOX-M_aL3g"
      },
      "id": "QTTOX-M_aL3g",
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ind_to_word = dict(enumerate(vocabulary))\n",
        "word_to_ind = {value: key for key, value in ind_to_word.items()}"
      ],
      "metadata": {
        "id": "Nfo_JRS4aPGE"
      },
      "id": "Nfo_JRS4aPGE",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "d3a823bc",
      "metadata": {
        "id": "d3a823bc"
      },
      "outputs": [],
      "source": [
        "class W2VDataset(Dataset):\n",
        "    def __init__(self, tokens: list, word_to_ind: dict, unk_token: str, window_size: int = 4):\n",
        "        self.tokens = tokens\n",
        "        self.vocab = word_to_ind\n",
        "        self.unk_token = unk_token\n",
        "        assert not window_size % 2\n",
        "        self.window_size = window_size\n",
        "\n",
        "    def __getitem__(self, item):\n",
        "        center_index = item + self.window_size // 2\n",
        "        center_word = self.vocab.get(self.tokens[center_index], self.vocab[self.unk_token])\n",
        "\n",
        "        context_words = [\n",
        "            self.vocab.get(self.tokens[i], self.vocab[self.unk_token])\n",
        "            for i in range(item, item + self.window_size + 1)\n",
        "            if i != center_index\n",
        "        ]\n",
        "\n",
        "        return {\n",
        "            'word': torch.tensor(center_word, dtype=torch.long),\n",
        "            'context': torch.tensor(context_words, dtype=torch.long)\n",
        "            }\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.tokens) - self.window_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "6712004e",
      "metadata": {
        "id": "6712004e"
      },
      "outputs": [],
      "source": [
        "dataset = W2VDataset(tokens, word_to_ind, unk_token)\n",
        "loader = DataLoader(dataset, batch_size=224, num_workers=2, shuffle=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "2yzvz5bravA1",
        "outputId": "e5ac94d8-4ed7-4045-aef3-739b66df95ea"
      },
      "id": "2yzvz5bravA1",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'cuda'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "09535d86",
      "metadata": {
        "id": "09535d86",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93765325-4aca-4d34-a907-9f84802cad78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([224]) torch.Size([224, 4])\n"
          ]
        }
      ],
      "source": [
        "for batch in loader:\n",
        "    print(batch['word'].shape, batch['context'].shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(loader), len(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOVz_iA3Zn6J",
        "outputId": "08d531d3-c44c-4ff6-8ed1-7a64883c9538"
      },
      "id": "MOVz_iA3Zn6J",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(75917, 17005203)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "612e5848",
      "metadata": {
        "id": "612e5848"
      },
      "outputs": [],
      "source": [
        "class W2VModel(nn.Module):\n",
        "    def __init__(self, vocab_size, emb_dim):\n",
        "        super(W2VModel, self).__init__()\n",
        "        self.vocab_size = vocab_size\n",
        "        self.emb_dim = emb_dim\n",
        "\n",
        "        self.emb = nn.Embedding(self.vocab_size, self.emb_dim)\n",
        "        self.decoder = nn.Linear(self.emb_dim, self.vocab_size)\n",
        "\n",
        "        self.init_emb()\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.emb(x)\n",
        "        return self.decoder(x)\n",
        "\n",
        "    def init_emb(self):\n",
        "        \"\"\"\n",
        "        init the weight as original word2vec do.\n",
        "        \"\"\"\n",
        "        initrange = 0.5 / self.emb_dim\n",
        "        self.emb.weight.data.uniform_(-initrange, initrange)\n",
        "        self.decoder.weight.data.uniform_(0, 0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_labels(context_index: torch.Tensor, vocab_size: int):\n",
        "    batch_size = context_index.size(0)\n",
        "    labels = torch.zeros(batch_size, vocab_size)\n",
        "    for i in range(batch_size):\n",
        "        labels[i, context_index[i]] = 1\n",
        "    return labels"
      ],
      "metadata": {
        "id": "5LJ9LuXvaXXs"
      },
      "id": "5LJ9LuXvaXXs",
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "50c6ce60",
      "metadata": {
        "id": "50c6ce60"
      },
      "outputs": [],
      "source": [
        "def train(\n",
        "    epoch: int,\n",
        "    model: nn.Module,\n",
        "    train_loader: DataLoader,\n",
        "    criterion: nn.Module,\n",
        "    optimizer: torch.optim.Optimizer,\n",
        "    device: str\n",
        "):\n",
        "    epoch_loss = 0\n",
        "    for batch in tqdm(train_loader, total=len(train_loader)):\n",
        "        center, context = batch['word'].to(device), batch['context'].to(device)\n",
        "        labels = create_labels(context, model.vocab_size).to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "        loss = criterion(model(center), labels)\n",
        "        epoch_loss += loss.item()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    print(f'Epoch: {epoch}, Loss: {epoch_loss / len(train_loader)}')\n",
        "    return epoch_loss / len(train_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "605fe4d9",
      "metadata": {
        "id": "605fe4d9"
      },
      "outputs": [],
      "source": [
        "model = W2VModel(len(word_to_ind), 200).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "da3bf570",
      "metadata": {
        "id": "da3bf570",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
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        "outputId": "6c2ecba4-d530-45fa-f35c-29ebcd198e54"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/75917 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "254eea1653654a6ba5332e798059424d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 0, Loss: 0.0030066878291172086\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/75917 [00:00<?, ?it/s]"
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1, Loss: 0.0007608064727032514\n"
          ]
        }
      ],
      "source": [
        "criterion = nn.BCEWithLogitsLoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
        "\n",
        "train_losses = []\n",
        "\n",
        "\n",
        "for epoch in range(2):\n",
        "    train_loss = train(epoch, model, loader, criterion, optimizer, device)\n",
        "\n",
        "    train_losses.append(train_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "fbb4e451",
      "metadata": {
        "id": "fbb4e451"
      },
      "outputs": [],
      "source": [
        "torch.save(model, 'w2v_model.torch')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(train_losses, marker='s')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "A_iWw09Dfg9v",
        "outputId": "8b087411-a9fe-4c38-ce93-68950930f037"
      },
      "id": "A_iWw09Dfg9v",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f96ce232e00>]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity, euclidean_distances\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "uSU0qVlYfvfh"
      },
      "id": "uSU0qVlYfvfh",
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = dict()\n",
        "for key, value in tqdm(word_to_ind.items()):\n",
        "    preds = model.emb.weight[value]\n",
        "    result[key] = preds.cpu().detach().numpy()"
      ],
      "metadata": {
        "id": "KJjpYjtHfyCx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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