Yotutube_WordVectors.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PradipNichite/Youtube-Tutorials/blob/main/Yotutube_WordVectors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# https://radimrehurek.com/gensim/auto_examples/tutorials/run_word2vec.html\n",
        "# https://radimrehurek.com/gensim/models/word2vec.html\n",
        "# https://analyticsindiamag.com/word2vec-vs-glove-a-comparative-guide-to-word-embedding-techniques/"
      ],
      "metadata": {
        "id": "4DhgUmJAPdbc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://medium.com/intelligentmachines/word-embedding-and-one-hot-encoding-ad17b4bbe111"
      ],
      "metadata": {
        "id": "mpkwvwH5g66R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Agenda </h3>\n",
        "\n",
        "*   Use Pretrained Word Vectors\n",
        "*   Use word vecotrs as feature for classification\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Ain7XVepPiUf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I6T_5ubcd3t3"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade gensim -q\n",
        "!pip install matplotlib -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "9XaQFwH6d3uF"
      },
      "outputs": [],
      "source": [
        "import gensim\n",
        "from gensim.models import word2vec\n",
        "from gensim.models.word2vec import Word2Vec\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import spacy\n",
        "import string"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gensim.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Ar_X4gP6IhSk",
        "outputId": "62852788-88cc-47ec-9c2d-9f0df27dc3cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4.2.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(42)"
      ],
      "metadata": {
        "id": "a98GdugAAH35"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "Iul5f_doNzHC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Use Pretrained Word Vectors"
      ],
      "metadata": {
        "id": "gW_0s2ZQQtDB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gensim.downloader as api\n",
        "print(list(gensim.downloader.info()['models'].keys()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Yt8fZ6pQp4j",
        "outputId": "542ca7c5-bf3a-448d-df4f-bcbf3a2773dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['fasttext-wiki-news-subwords-300', 'conceptnet-numberbatch-17-06-300', 'word2vec-ruscorpora-300', 'word2vec-google-news-300', 'glove-wiki-gigaword-50', 'glove-wiki-gigaword-100', 'glove-wiki-gigaword-200', 'glove-wiki-gigaword-300', 'glove-twitter-25', 'glove-twitter-50', 'glove-twitter-100', 'glove-twitter-200', '__testing_word2vec-matrix-synopsis']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv = api.load('glove-twitter-50')"
      ],
      "metadata": {
        "id": "u8_e1FlsOxUA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(wv)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UT0rA32iHFd",
        "outputId": "780e0b4d-723c-41e6-dd06-6c7fc6857ee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gensim.models.keyedvectors.KeyedVectors"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# wv = api.load('word2vec-google-news-300')\n",
        "# wv.save('/content/drive/MyDrive/Content Creation/Youtube Tutorials/vectors.kv')"
      ],
      "metadata": {
        "id": "-_yq3wTaQzSO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wv['apple']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZObUSlhH1Mt",
        "outputId": "9754a1f8-c1c7-4d6b-da8b-aab93b5bd090"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        0.16210938,  0.3046875 , -0.1796875 , -0.12109375,  0.25390625,\n",
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              "       -0.34765625,  0.35742188,  0.265625  ,  0.00188446, -0.01586914,\n",
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              "        0.0378418 ,  0.17382812, -0.41796875,  0.2890625 ,  0.32617188,\n",
              "        0.02429199, -0.01647949, -0.06494141, -0.08886719,  0.07666016,\n",
              "       -0.15136719,  0.05249023, -0.04199219, -0.05419922,  0.00108337,\n",
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              "       -0.00482178, -0.00089264, -0.30664062, -0.0625    ,  0.07958984,\n",
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              "       -0.14941406,  0.0378418 ,  0.38671875,  0.14160156, -0.2890625 ,\n",
              "       -0.16894531, -0.140625  , -0.04174805,  0.22753906,  0.24023438,\n",
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              "       -0.20996094, -0.1796875 , -0.19628906, -0.22167969,  0.08886719,\n",
              "       -0.27734375, -0.13964844,  0.15917969,  0.03637695,  0.03320312,\n",
              "       -0.08105469,  0.25390625, -0.08691406, -0.21289062, -0.18945312,\n",
              "       -0.22363281,  0.06542969, -0.16601562,  0.08837891, -0.359375  ,\n",
              "       -0.09863281,  0.35546875, -0.00741577,  0.19042969,  0.16992188,\n",
              "       -0.06005859, -0.20605469,  0.08105469,  0.12988281, -0.01135254,\n",
              "        0.33203125, -0.08691406,  0.27539062, -0.03271484,  0.12011719,\n",
              "       -0.0625    ,  0.1953125 , -0.10986328, -0.11767578,  0.20996094,\n",
              "        0.19921875,  0.02954102, -0.16015625,  0.00276184, -0.01367188,\n",
              "        0.03442383, -0.19335938,  0.00352478, -0.06542969, -0.05566406,\n",
              "        0.09423828,  0.29296875,  0.04052734, -0.09326172, -0.10107422,\n",
              "       -0.27539062,  0.04394531, -0.07275391,  0.13867188,  0.02380371,\n",
              "        0.13085938,  0.00236511, -0.2265625 ,  0.34765625,  0.13574219,\n",
              "        0.05224609,  0.18164062,  0.0402832 ,  0.23730469, -0.16992188,\n",
              "        0.10058594,  0.03833008,  0.10839844, -0.05615234, -0.00946045,\n",
              "        0.14550781, -0.30078125, -0.32226562,  0.18847656, -0.40234375,\n",
              "       -0.3125    , -0.08007812, -0.26757812,  0.16699219,  0.07324219,\n",
              "        0.06347656,  0.06591797,  0.17285156, -0.17773438,  0.00276184,\n",
              "       -0.05761719, -0.2265625 , -0.19628906,  0.09667969,  0.13769531,\n",
              "       -0.49414062, -0.27929688,  0.12304688, -0.30078125,  0.01293945,\n",
              "       -0.1875    , -0.20898438, -0.1796875 , -0.16015625, -0.03295898,\n",
              "        0.00976562,  0.25390625, -0.25195312,  0.00210571,  0.04296875,\n",
              "        0.01184082, -0.20605469,  0.24804688, -0.203125  , -0.17773438,\n",
              "        0.07275391,  0.04541016,  0.21679688, -0.2109375 ,  0.14550781,\n",
              "       -0.16210938,  0.20410156, -0.19628906, -0.35742188,  0.35742188,\n",
              "       -0.11962891,  0.35742188,  0.10351562,  0.07080078, -0.24707031,\n",
              "       -0.10449219, -0.19238281,  0.1484375 ,  0.00057983,  0.296875  ,\n",
              "       -0.12695312, -0.03979492,  0.13183594, -0.16601562,  0.125     ,\n",
              "        0.05126953, -0.14941406,  0.13671875, -0.02075195,  0.34375   ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(wv['apple'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mk6KkNvH644",
        "outputId": "bee36c8a-2ac4-4656-e3cd-20792e70943c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "300"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import KeyedVectors\n",
        "wv = KeyedVectors.load('/content/drive/MyDrive/Content Creation/Youtube Tutorials/vectors.kv')"
      ],
      "metadata": {
        "id": "yj3MKu9xOMEl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wv.similarity(\"apple\", \"mango\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERMiTXcrI2Du",
        "outputId": "b953cf24-4cec-4f3a-9a38-6d00c1158a57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.57518554"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.similarity(\"apple\", \"car\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56TLg3ygJKwB",
        "outputId": "617c654f-feaf-46df-f68a-2d1653ada6bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12830707"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pairs = [\n",
        "    ('car', 'minivan'),   # a minivan is a kind of car\n",
        "    ('car', 'bicycle'),   # still a wheeled vehicle\n",
        "    ('car', 'airplane'),  # ok, no wheels, but still a vehicle\n",
        "    ('car', 'cereal'),    # ... and so on\n",
        "    ('car', 'communism'),\n",
        "]\n",
        "for w1, w2 in pairs:\n",
        "    print('%r\\t%r\\t%.2f' % (w1, w2, wv.similarity(w1, w2)))"
      ],
      "metadata": {
        "id": "b1MxV_x3fEPm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "816f1e83-b76e-4d85-9301-1393a15b3552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'car'\t'minivan'\t0.69\n",
            "'car'\t'bicycle'\t0.54\n",
            "'car'\t'airplane'\t0.42\n",
            "'car'\t'cereal'\t0.14\n",
            "'car'\t'communism'\t0.06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(wv.most_similar(positive=['car', 'minivan'], topn=5))"
      ],
      "metadata": {
        "id": "af8Fjp0TfOFl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "680b9b7e-455a-41aa-cf70-d967824849d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('SUV', 0.8532192707061768), ('vehicle', 0.8175783753395081), ('pickup_truck', 0.7763688564300537), ('Jeep', 0.7567334175109863), ('Ford_Explorer', 0.7565720081329346)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(wv.doesnt_match(['fire', 'water', 'land', 'sea', 'air', 'car']))"
      ],
      "metadata": {
        "id": "QykE5BjrfVoG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0356b015-0610-4983-a3f1-5967d8a73931"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "car\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKbj-Rj0d3uw"
      },
      "source": [
        "#### Semantic regularities captured in word embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CioPg8xtd3uw",
        "outputId": "86439147-6ef1-442b-db04-6d8d5bfd042a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('queen', 0.7118193507194519),\n",
              " ('monarch', 0.6189674139022827),\n",
              " ('princess', 0.5902431011199951)]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "wv.most_similar(positive=['woman', 'king'], negative=['man'], topn=3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.most_similar(positive=['woman', 'king'], topn=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-mZtVMO1MgIQ",
        "outputId": "4956c8f5-610c-4180-d434-242ebdf45f45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('man', 0.6628609299659729),\n",
              " ('queen', 0.6438563466072083),\n",
              " ('girl', 0.6136074066162109)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "words = [\"one\",'two','man','woman','table']"
      ],
      "metadata": {
        "id": "77of9vb4Nngx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# https://deepai.org/dataset/text8"
      ],
      "metadata": {
        "id": "Xw50zX3EbDqh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sample_vectors = np.array([wv[word] for word in words])\n",
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "result = pca.fit_transform(sample_vectors)\n",
        "result"
      ],
      "metadata": {
        "id": "DYpApeTjBYZ1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad274bbf-1fca-4751-d755-dbb1f0f715df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.41775945,  0.906729  ],\n",
              "       [ 0.52036536,  1.3947775 ],\n",
              "       [-1.2547548 , -0.3259122 ],\n",
              "       [-1.6043063 , -0.64717937],\n",
              "       [ 1.920937  , -1.328415  ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TnJ-I-8sd3uy"
      },
      "source": [
        "### Visualizing these word vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vLRFzuXLd3u3",
        "outputId": "843871df-5c15-4619-b825-85dca05f94b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "plt.scatter(result[:,0], result[:,1])\n",
        "for i, word in enumerate(words):\n",
        "    plt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/drive/MyDrive/Content Creation/Youtube Tutorials/datasets/toxic_commnets_500.csv\",error_bad_lines=False, engine=\"python\")\n",
        "data.head()"
      ],
      "metadata": {
        "id": "r8q8h4K3dXcI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bb3393f7-e224-434f-a172-c1445bab0ede"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:3326: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n",
            "\n",
            "\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        comment_text  toxic\n",
              "0  Your knowledge of Hebrew? Thank god that Jossi...      0\n",
              "1  ==Tablighi Jamaat and allegations of terrorism...      0\n",
              "2  \"\\nWell it's understandable not wanting the in...      0\n",
              "3  \"\\n\\n Please stop adding nonsense to Wikipedia...      0\n",
              "4  Saint Petersburg\\nPlease, leave the sister cit...      0"
            ],
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              "          const element = document.querySelector('#df-77d298d6-b752-4a71-9497-79ac09f1933e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def sent_vec(sent):\n",
        "    vector_size = wv.vector_size\n",
        "    wv_res = np.zeros(vector_size)\n",
        "    # print(wv_res)\n",
        "    ctr = 1\n",
        "    for w in sent:\n",
        "        if w in wv:\n",
        "            ctr += 1\n",
        "            wv_res += wv[w]\n",
        "    wv_res = wv_res/ctr\n",
        "    return wv_res"
      ],
      "metadata": {
        "id": "Lkq3vjTRcGXz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating our tokenizer function\n",
        "def spacy_tokenizer(sentence):\n",
        "    # Creating our token object, which is used to create documents with linguistic annotations.\n",
        "    doc = nlp(sentence)\n",
        "\n",
        "\n",
        "\n",
        "    # print(doc)\n",
        "    # print(type(doc))\n",
        "\n",
        "    # Lemmatizing each token and converting each token into lowercase\n",
        "    mytokens = [ word.lemma_.lower().strip() for word in doc ]\n",
        "\n",
        "    # print(mytokens)\n",
        "\n",
        "    # Removing stop words\n",
        "    mytokens = [ word for word in mytokens if word not in stop_words and word not in punctuations ]\n",
        "\n",
        "    # return preprocessed list of tokens\n",
        "    return mytokens"
      ],
      "metadata": {
        "id": "1X-v4Q9adjfC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sent_vec(\"I am happy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7PkUr8fcIny",
        "outputId": "6bbca910-b689-43c3-a00b-69198b0cdff7"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
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              "       -0.15813337, -0.11539132, -0.00368827, -0.00578962,  0.05154855])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "stop_words = nlp.Defaults.stop_words\n",
        "print(stop_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MrfAYBjQdoGe",
        "outputId": "9c58b564-b93e-421d-f972-bd2d2b5db381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'empty', 'more', 'she', 'upon', 'really', 'less', 'so', 'still', 'no', 'behind', 'nothing', 'become', 'every', 'using', 'yourself', 'who', 'others', 'such', 'before', 'but', 'toward', 'everyone', '’ve', 'because', 'never', 'see', 'whereupon', 'seeming', 'throughout', 'up', 'latterly', 'while', 'within', 'amongst', 'once', 'around', 'our', 'former', 'eight', 'full', 'these', 'be', 'front', 'whom', 'became', 'either', 'least', 'other', 'neither', 'has', 'where', 'only', 'do', 'part', 'something', 'those', 'was', 'give', 'down', 'is', 'might', 'wherever', 'amount', 'ca', 'n‘t', 'doing', 'each', 'your', 'beside', '‘ve', 'of', 'also', 'been', 'whole', 'n’t', 'about', 'two', 'formerly', '‘ll', 'often', 'put', 'someone', 'besides', 'ever', 'quite', '‘d', 'becoming', 'thence', 'even', 'last', 'twenty', '‘s', 'just', 'am', 'should', 'off', 'among', 'into', 'together', 'almost', 'ours', 'were', 'below', 'none', 'rather', 'fifteen', 'regarding', 'twelve', 'four', 'if', 'enough', 'moreover', 'six', 'against', 'everywhere', 'whatever', 'ourselves', 'very', 'per', \"'ve\", 'whence', 'between', 'namely', 'forty', 'may', 'nine', 'side', 'hereby', 'get', 'show', 'anything', 'by', '’d', 'yourselves', 'hers', 'have', 'whenever', 'most', 'seems', 'own', 'towards', 'hereupon', 'yours', 'until', 'hundred', 'beforehand', 'nor', 'as', 'us', 'could', 'hereafter', 'through', 'make', 'their', 'along', '’m', 'much', 'can', 'thus', 'else', 'go', 'top', 'first', 'again', 'under', 'whereafter', 'i', 'wherein', 'here', \"'m\", 're', 'sometimes', 'he', 'thereafter', 'cannot', 'whither', '’ll', 'nevertheless', 'must', 'anyway', 'her', 'otherwise', 'nobody', 'various', \"n't\", 'used', 'himself', 'ten', 'always', 'via', 'except', 'for', 'three', 'after', 'thru', 'a', 'him', 'serious', 'thereby', 'across', 'though', 'what', 'somehow', 'well', 'and', 'many', 'its', '‘m', 'some', 'it', 'made', 'meanwhile', 'we', 'his', 'the', 'anywhere', 'with', 'noone', 'therein', 'too', 'during', 'therefore', 'seemed', 'now', 'eleven', 'say', \"'s\", 'latter', 'from', 'indeed', 'them', 'they', 'me', 'afterwards', 'perhaps', 'seem', 'whose', 'whoever', 'in', \"'d\", 'anyhow', 'will', 'themselves', 'which', 'thereupon', 'whereas', 'same', 'above', 'beyond', 'however', \"'ll\", 'few', 'an', 'does', 'already', 'itself', 'becomes', 'herein', 'back', 'whereby', 'although', 'sixty', 'how', 'did', 'any', 'at', 'not', 'onto', 'that', 'to', 'take', 'bottom', 'fifty', 'done', 'everything', 'anyone', 'another', 'herself', \"'re\", 'due', 'since', 'this', 'call', 'hence', 'sometime', 'move', 'myself', 'there', 'had', 'third', 'nowhere', 'all', 'alone', 'keep', 'over', 'or', 'why', 'elsewhere', '’s', 'name', 'mostly', '’re', 'you', 'out', 'somewhere', 'unless', '‘re', 'five', 'please', 'both', 'are', 'next', 'mine', 'than', 'then', 'one', 'on', 'yet', 'my', 'further', 'would', 'whether', 'several', 'when', 'without', 'being'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "punctuations = string.punctuation\n",
        "print(punctuations)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDO7z2qxdu9c",
        "outputId": "5f386646-d565-4467-f71c-aea2628c2118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['tokens'] = data['comment_text'].apply(spacy_tokenizer)"
      ],
      "metadata": {
        "id": "vnv2KJUld3AP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "efY14RdKeDIB",
        "outputId": "74aeb8b2-732f-417e-e5ea-68a2ae06ec6f"
      },
      "execution_count": null,
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              "3  \"\\n\\n Please stop adding nonsense to Wikipedia...      0   \n",
              "4  Saint Petersburg\\nPlease, leave the sister cit...      0   \n",
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              "                                              tokens  \n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4eac7ad9-1686-4a2c-8cc3-c3e08f706b3b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4eac7ad9-1686-4a2c-8cc3-c3e08f706b3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['vec'] = data['tokens'].apply(sent_vec)"
      ],
      "metadata": {
        "id": "J6fqfDhTeGA4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IBWAxmw2elxw",
        "outputId": "5ee6f732-8536-41cd-888f-7ea3c0e8bffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        comment_text  toxic  \\\n",
              "0  Your knowledge of Hebrew? Thank god that Jossi...      0   \n",
              "1  ==Tablighi Jamaat and allegations of terrorism...      0   \n",
              "2  \"\\nWell it's understandable not wanting the in...      0   \n",
              "3  \"\\n\\n Please stop adding nonsense to Wikipedia...      0   \n",
              "4  Saint Petersburg\\nPlease, leave the sister cit...      0   \n",
              "\n",
              "                                              tokens  \\\n",
              "0  [knowledge, hebrew, thank, god, jossi, speak, ...   \n",
              "1  [tablighi, jamaat, allegation, terrorism==, ad...   \n",
              "2  [understandable, want, infobox, overfill, info...   \n",
              "3  [stop, add, nonsense, wikipedia, consider, van...   \n",
              "4  [saint, petersburg, leave, sister, city, ill, ...   \n",
              "\n",
              "                                                 vec  \n",
              "0  [0.0632793719951923, -0.007477980393629808, 0....  \n",
              "1  [-0.049352010091145836, -0.007761637369791667,...  \n",
              "2  [0.02009953090122768, -0.027743094308035714, -...  \n",
              "3  [0.08058844672309028, 0.048272026909722224, 0....  \n",
              "4  [0.05062408447265625, 0.007540130615234375, 0....  "
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              "      <td>0</td>\n",
              "      <td>[understandable, want, infobox, overfill, info...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>\"\\n\\n Please stop adding nonsense to Wikipedia...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Saint Petersburg\\nPlease, leave the sister cit...</td>\n",
              "      <td>0</td>\n",
              "      <td>[saint, petersburg, leave, sister, city, ill, ...</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-fc76ffad-11de-432b-833d-4843cc616ceb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fc76ffad-11de-432b-833d-4843cc616ceb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data['vec'].to_list()\n",
        "y = data['toxic'].to_list()"
      ],
      "metadata": {
        "id": "6ZLvbyAUe2RQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[0]"
      ],
      "metadata": {
        "id": "M2MvztRqfCGg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=y)"
      ],
      "metadata": {
        "id": "hJisSzFafD1I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression()"
      ],
      "metadata": {
        "id": "yYFiyvbIfMmn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PlvKwoWGfX5A",
        "outputId": "a0518549-e934-424a-99fa-656328f58782"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "predicted = classifier.predict(X_test)\n",
        "print(\"Logistic Regression Accuracy:\",metrics.accuracy_score(y_test, predicted))\n",
        "print(\"Logistic Regression Precision:\",metrics.precision_score(y_test, predicted))\n",
        "print(\"Logistic Regression Recall:\",metrics.recall_score(y_test, predicted))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jel4lRCkfaz_",
        "outputId": "b358933c-7207-4d11-dfe8-49d3534c3cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Accuracy: 0.88\n",
            "Logistic Regression Precision: 0.8958333333333334\n",
            "Logistic Regression Recall: 0.86\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VygTwri1amL0"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3.9.10 ('venv': venv)",
      "language": "python",
      "name": "python3"
    },
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    },
    "vscode": {
      "interpreter": {
        "hash": "3aee63c9909931f8570ab0af8ed7fb48514b1bc692251c446ac030d3ef29d031"
      }
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}