examples/Vector_Example.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOzty3I1fH6n17SvF4//s7c",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vishalmysore/AI/blob/main/examples/Vector_Example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets word2vec\n",
        "!pip install --upgrade gensim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1UKS5oCDLkT2",
        "outputId": "1e6433e7-63b0-4ea4-bcf7-8c516c098246"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
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            "\u001b[?25hCollecting word2vec\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Building wheels for collected packages: word2vec\n",
            "  Building wheel for word2vec (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for word2vec: filename=word2vec-0.11.1-py2.py3-none-any.whl size=141242 sha256=003bcdcedf81af4c79e48a1d7eecf8fcfaa123e20a0b81e6ef25dbc9112e08df\n",
            "  Stored in directory: /root/.cache/pip/wheels/6a/fa/d1/e03e8c10e0e2aa5c7b6e2b46b4a1c715d140283853937bb4b1\n",
            "Successfully built word2vec\n",
            "Installing collected packages: word2vec, pyarrow-hotfix, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.16.0 dill-0.3.7 multiprocess-0.70.15 pyarrow-hotfix-0.6 word2vec-0.11.1\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.2)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.23.5)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.11.4)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (6.4.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "aeqHZ6yLLdDX"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "from gensim.models import Word2Vec\n",
        "from gensim.utils import simple_preprocess"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset('stas/openwebtext-10k', split='train[:2000]')"
      ],
      "metadata": {
        "id": "ZrOhSL7SO4wu"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnWMKPajOiUo",
        "outputId": "10626141-51d6-43eb-adc7-a4d50e41f4b3"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 2000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texts = [simple_preprocess(art['text']) for art in dataset]"
      ],
      "metadata": {
        "id": "DfVVNiS1OvZ_"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"VishalMysore/cookGPT\", split=\"train\")"
      ],
      "metadata": {
        "id": "YhAuTQTOfhkp"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlz-1gtc-K4V",
        "outputId": "7d7e981b-df03-4b3b-8121-0e3919e8168b"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 5938\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "texts = [simple_preprocess(art['text']) for art in dataset]"
      ],
      "metadata": {
        "id": "xi30VMPOf1g5"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Word2Vec(texts, vector_size=200, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "Q1rvZ-HDRJYj"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.wv[\"country\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imMcsaNYTtNS",
        "outputId": "309b2bed-9037-40b9-9a14-6734371e6f7e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 7.71433860e-02 -4.05825943e-01  1.08757712e-01  1.28278825e-02\n",
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            "  5.49316183e-02  7.10305870e-01 -2.85880506e-01  7.94317186e-01\n",
            "  1.37418598e-01 -1.97094828e-01  3.94449145e-01  2.49700233e-01\n",
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            "  8.68782923e-02 -5.75494468e-01 -6.09761924e-02  1.06842875e+00\n",
            "  1.68907389e-01 -1.92657173e-01  2.47115158e-02 -4.70862359e-01\n",
            "  1.04710257e+00 -8.79172027e-01 -5.30653417e-01 -8.55412185e-02\n",
            "  1.55109510e-01 -4.76188570e-01  1.24122642e-01  5.21540921e-03\n",
            "  3.98150861e-01  1.11548580e-01  4.38938349e-01 -1.26047403e-01\n",
            "  7.55677164e-01 -4.41483594e-03 -7.71266162e-01  5.62669039e-01\n",
            " -1.96736917e-01  3.54839444e-01 -1.01450027e-03  2.43677780e-01\n",
            "  3.74988347e-01  1.56079575e-01 -6.36942238e-02 -4.47543055e-01\n",
            "  3.70133668e-01  4.69567418e-01  3.94832551e-01 -5.51820040e-01\n",
            " -6.68957949e-01 -3.73245418e-01  1.60816714e-01 -1.36025585e-02\n",
            "  3.22595865e-01  1.68174714e-01 -4.11082596e-01  6.99793458e-01\n",
            " -4.54942375e-01  4.18562412e-01  5.18572964e-02  9.26839113e-02\n",
            " -3.59989877e-04  3.10062855e-01 -4.92775291e-02 -3.03920954e-01\n",
            " -1.74539477e-01  2.40847990e-01  6.83377147e-01  1.79856569e-01\n",
            "  6.19486868e-01  8.65111277e-02  3.89854282e-01  3.53236616e-01\n",
            " -6.46595597e-01 -7.79917955e-01 -8.11764479e-01  4.46787447e-01\n",
            " -3.57396036e-01 -3.90716672e-01  6.07533634e-01 -5.85151017e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Word2Vec(texts, vector_size=200, window=5, min_count=1, workers=4)"
      ],
      "metadata": {
        "id": "ewrVf-veXsXQ"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model.wv[\"curry\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wO2A3tkUXw9a",
        "outputId": "bc5e8a5b-307f-4b58-fa5b-5967cc32d679"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-0.6449355  -0.16600572  0.65637714  0.6139747  -1.114005    0.59743875\n",
            " -0.75153863  0.02248816  0.09341598 -1.0115138   0.49911708 -0.9733342\n",
            "  1.1641619   2.4645488  -0.76755494 -0.9487304   0.4373897  -1.5327953\n",
            " -0.06228069  2.555225   -1.0830663   0.65158474 -1.8286058  -0.18413395\n",
            "  0.3214392  -0.05137096  0.7704669  -1.9463527  -1.5842938   0.73009074\n",
            " -1.1924165  -0.81805706 -0.10811521  1.2913295  -0.8991021  -1.5573798\n",
            "  0.41917402 -0.76455057  1.7474304   1.1556249   0.76353    -1.4861491\n",
            " -1.3281822  -0.45075983  0.064293   -0.8083985  -0.2136378  -0.77848953\n",
            " -0.01688977  1.5608721   0.03600923 -0.47788924 -0.3932481  -0.62904495\n",
            " -0.14549692 -1.0665908  -1.731863    0.6704712   0.8777108  -1.4174439\n",
            "  1.0212926  -0.3393248  -1.0152375  -0.8103006  -0.4828903  -2.9464033\n",
            " -0.3061676   0.0559967   1.9267671  -0.29828373  0.41823375 -2.6112015\n",
            " -1.0483311   1.1762062  -0.73183125  1.2328616  -1.888158    1.2096367\n",
            "  2.992204    0.76457596 -1.267939   -1.8102813   0.01309838  0.03281611\n",
            "  1.7477983   1.2119961  -0.33906266  1.5649142   1.2640743   0.1305197\n",
            " -0.24284585  1.3184304  -0.7227891  -2.375019   -0.04033624 -2.8846693\n",
            " -0.84250873  2.3204684   1.0369862   1.394099    2.305098   -1.285515\n",
            " -0.35283265 -0.08926902 -0.41706297  0.5294694  -0.5584759  -1.3717741\n",
            " -1.6599762   1.8179786  -2.1067548   0.32753626 -1.4004323  -2.3974316\n",
            "  0.21257488  0.58209765  0.34037182 -0.38184077 -1.2056427  -1.1054788\n",
            "  0.5465823   0.03953015 -1.5515373  -0.0551715   1.5264653  -1.2551513\n",
            "  1.2646658  -0.30461097  0.08191835  0.37992194 -0.40876165 -0.9997947\n",
            "  0.61076874  0.3446858   0.50002146 -1.6811216   0.4405515  -0.5930898\n",
            "  1.1547339   0.51555765 -0.11989768  0.87027407  0.16131513 -1.8098736\n",
            "  0.6350583  -1.1024541  -0.19763322  1.7545998  -0.5446565   1.603365\n",
            " -0.21702926 -0.97586095 -0.5356971  -1.3925892  -0.24472128  0.5186597\n",
            " -1.54899     1.2694889   0.39626157 -0.40413678 -0.01941651  1.5466707\n",
            " -0.85518247 -0.09277029 -0.65657085  0.1537488   0.03842515  1.2872819\n",
            "  1.0688125  -2.3017175  -0.38961935 -0.04701168  0.18079908 -0.10946354\n",
            " -2.3284228   0.9827038   0.24629629 -1.8255209   0.40201482  0.92539495\n",
            "  3.468096   -3.3829112  -1.3617147   0.72916657 -0.4134023   1.0276802\n",
            " -1.2910281  -1.6584177  -0.6936143   0.049298   -2.788968   -0.12333278\n",
            " -0.7139793   0.04060752 -1.6689203  -2.217699   -0.6298474   0.4806096\n",
            "  0.49934033  0.5541439 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar_words = model.wv.most_similar(positive=['paneer', 'masala', 'spice'], topn=10)\n",
        "print(similar_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wfN2b1OURU_C",
        "outputId": "f0b36ce0-d090-4a48-a21b-5c6914020b79"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('spices', 0.51334547996521), ('powders', 0.49869391322135925), ('masalas', 0.4770452678203583), ('amchoor', 0.4132078289985657), ('chicken', 0.41215088963508606), ('including', 0.4067543148994446), ('secs', 0.40314871072769165), ('freshly', 0.3988073170185089), ('asafoedita', 0.38405483961105347), ('chillie', 0.37987735867500305)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "similar_words = model.wv.most_similar(positive=['country', 'history', 'population'], topn=10)\n",
        "print(similar_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkK992yqCaZt",
        "outputId": "2131b32b-eb7a-4795-b691-4b95e1cc9d62"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('crisis', 0.9217091202735901), ('nation', 0.9191784858703613), ('economy', 0.9116148352622986), ('region', 0.9039095640182495), ('study', 0.8893676996231079), ('terms', 0.8882991671562195), ('conflict', 0.8863515257835388), ('cities', 0.8856520056724548), ('manner', 0.8838774561882019), ('europe', 0.8837443590164185)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Extract the words and their vectors\n",
        "words = [word for word, _ in similar_words]\n",
        "word_vectors = np.array([model.wv[word] for word in words])\n",
        "\n",
        "# Perform dimensionality reduction using t-SNE\n",
        "tsne = TSNE(n_components=2, random_state=42, perplexity=9)\n",
        "word_vectors_2d = tsne.fit_transform(word_vectors)\n",
        "\n",
        "# Plot the words and their 2D vectors\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.scatter(word_vectors_2d[:, 0], word_vectors_2d[:, 1], c='red', edgecolors='k')\n",
        "\n",
        "# Annotate the points with their respective words\n",
        "for i, word in enumerate(words):\n",
        "    plt.annotate(word, xy=(word_vectors_2d[i, 0], word_vectors_2d[i, 1]), xytext=(5, 2),\n",
        "                 textcoords='offset points', ha='right', va='bottom')\n",
        "\n",
        "plt.title('t-SNE visualization of word vectors')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('ingredients')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "f0_XPWBrBvCs",
        "outputId": "ff0d495c-8d92-4e2f-b140-f86428e11bae"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}