NLP/NLPDay_4.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EB1eqf3cQWb",
        "outputId": "a957c5e2-60a4-4dec-e2a4-eef95f1ef1a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (3.6.0)\n",
            "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.15.0)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.21.6)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (5.2.1)\n",
            "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (1.4.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install gensim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5sUEjvcPcSjR"
      },
      "outputs": [],
      "source": [
        "import gensim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UoddHebCccEA"
      },
      "outputs": [],
      "source": [
        "from gensim.models import Word2Vec, KeyedVectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AQIC5kRFchcP"
      },
      "outputs": [],
      "source": [
        "## References: https://stackoverflow.com/questions/46433778/import-googlenews-vectors-negative300-bin\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e-x3FqZGdsyG"
      },
      "outputs": [],
      "source": [
        "!gzip -d GoogleNews-vectors-negative300.bin.gz"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "Ewfy4OVwd2gO",
        "outputId": "3bc352c7-1e2c-43db-aaa5-d6e5bdf44b32"
      },
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-1363b8f5cbcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgensim\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mKeyedVectors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_word2vec_format\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GoogleNews-vectors-negative300.bin'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mload_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)\u001b[0m\n\u001b[1;32m   1436\u001b[0m         return _load_word2vec_format(\n\u001b[1;32m   1437\u001b[0m             \u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfvocab\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfvocab\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbinary\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0municode_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0municode_errors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m             limit=limit, datatype=datatype)\n\u001b[0m\u001b[1;32m   1439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_keras_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_embeddings\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/gensim/models/utils_any2vec.py\u001b[0m in \u001b[0;36m_load_word2vec_format\u001b[0;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype)\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msmart_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_unicode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m         \u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# throws for invalid file format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m             \u001b[0mvocab_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
          ]
        }
      ],
      "source": [
        "from gensim import models\n",
        "\n",
        "w = models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RbLkvsGd7Fb",
        "outputId": "22612c5c-addb-4758-cc8e-42ebb0e9611f"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
          ]
        }
      ],
      "source": [
        "import gensim.downloader as api\n",
        "\n",
        "wv = api.load('word2vec-google-news-300')\n",
        "\n",
        "vec_king = wv['king']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jmw-5y9ie_WZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63057a60-9a5e-4c0f-a233-b70f4b3c6179"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "vec_king"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.most_similar('cricket')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e54uZ09viYfb",
        "outputId": "ee73ee69-56ee-463b-a2e0-9443282a21fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('cricketing', 0.8372225165367126),\n",
              " ('cricketers', 0.8165745735168457),\n",
              " ('Test_cricket', 0.8094818592071533),\n",
              " ('Twenty##_cricket', 0.8068488240242004),\n",
              " ('Twenty##', 0.7624266147613525),\n",
              " ('Cricket', 0.7541396617889404),\n",
              " ('cricketer', 0.7372579574584961),\n",
              " ('twenty##', 0.7316356897354126),\n",
              " ('T##_cricket', 0.7304614782333374),\n",
              " ('West_Indies_cricket', 0.698798656463623)]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.most_similar('happy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vw5kiHNOik2z",
        "outputId": "62431e2e-5244-410a-8169-db3d747a9c10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('glad', 0.7408890128135681),\n",
              " ('pleased', 0.6632171273231506),\n",
              " ('ecstatic', 0.6626912355422974),\n",
              " ('overjoyed', 0.6599286794662476),\n",
              " ('thrilled', 0.6514049768447876),\n",
              " ('satisfied', 0.6437950134277344),\n",
              " ('proud', 0.636042058467865),\n",
              " ('delighted', 0.627237856388092),\n",
              " ('disappointed', 0.6269949674606323),\n",
              " ('excited', 0.6247666478157043)]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.similarity(\"hockey\",\"sports\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5H4XANCFiz7L",
        "outputId": "faabadc5-d532-4426-e7dd-0c10d8cf15d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.53541523"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vec=wv['king']-wv['man']+wv['woman']"
      ],
      "metadata": {
        "id": "w7VVwVI8i90p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vec"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi--OSxMjH_R",
        "outputId": "cb2713d4-6334-4227-a31f-edacf5b13292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "       -1.87500000e-01,  3.05175781e-04,  5.52734375e-01, -9.71679688e-02,\n",
              "       -1.81640625e-01, -1.51855469e-01,  7.76367188e-02, -2.38281250e-01,\n",
              "       -2.63977051e-02,  2.25555420e-01, -3.02734375e-01,  1.34765625e-01,\n",
              "        3.23242188e-01,  1.25976562e-01,  3.51562500e-02, -2.04345703e-01,\n",
              "        2.96142578e-01,  1.03149414e-01, -4.76074219e-03,  1.69189453e-01,\n",
              "       -3.50585938e-01,  2.46887207e-02, -3.90502930e-01, -2.70507812e-01,\n",
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              "       -8.98437500e-02,  5.45043945e-02,  5.65185547e-02,  1.56860352e-01,\n",
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              "       -5.61523438e-01,  1.38549805e-02,  1.09863281e-01,  7.22656250e-02,\n",
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              "        2.53906250e-01, -2.46826172e-01,  1.03759766e-01,  1.14013672e-01,\n",
              "        1.71875000e-01, -5.61523438e-03,  2.05078125e-01,  6.34765625e-02,\n",
              "       -4.51293945e-01, -2.26562500e-01, -1.03027344e-01, -1.31469727e-01,\n",
              "        3.75976562e-02,  2.70996094e-01, -2.39257812e-01,  3.80859375e-02,\n",
              "       -3.90625000e-02, -9.42382812e-02,  8.30078125e-03,  7.03125000e-02,\n",
              "        2.75390625e-01,  3.31542969e-01, -1.07421875e-02,  3.72192383e-01,\n",
              "       -1.24511719e-01,  1.94335938e-01, -1.35620117e-01, -3.09570312e-01,\n",
              "       -2.36328125e-01, -1.26953125e-02, -2.76855469e-01,  1.57714844e-01,\n",
              "        3.07617188e-01, -2.32910156e-01,  3.25439453e-01,  1.36718750e-02,\n",
              "        1.99462891e-01, -2.61840820e-02, -8.08105469e-02, -7.50732422e-02,\n",
              "       -4.11109924e-02,  1.95556641e-01, -5.64270020e-02, -2.79296875e-01,\n",
              "       -2.75390625e-01, -4.04296875e-01, -1.75781250e-02, -5.85937500e-03,\n",
              "       -7.71484375e-02,  1.33789062e-01,  2.36816406e-01,  2.01538086e-01],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wv.most_similar([vec])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3PkeelKkjI8h",
        "outputId": "069e1e42-e4bb-4eae-cdf1-1ee4b005d59b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('king', 0.8449392318725586),\n",
              " ('queen', 0.7300517559051514),\n",
              " ('monarch', 0.6454660892486572),\n",
              " ('princess', 0.6156251430511475),\n",
              " ('crown_prince', 0.5818676948547363),\n",
              " ('prince', 0.5777117609977722),\n",
              " ('kings', 0.5613663792610168),\n",
              " ('sultan', 0.5376776456832886),\n",
              " ('Queen_Consort', 0.5344247817993164),\n",
              " ('queens', 0.5289887189865112)]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nAN01EbsjNQM"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}