nlp_embeddidngs.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "u5XfPnSrjYx0"
      },
      "outputs": [],
      "source": [
        "import gensim.downloader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HRtUA5jEjfUa",
        "outputId": "429eb873-8181-4b03-c98e-1c62d1923330"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['fasttext-wiki-news-subwords-300',\n",
              " 'conceptnet-numberbatch-17-06-300',\n",
              " 'word2vec-ruscorpora-300',\n",
              " 'word2vec-google-news-300',\n",
              " 'glove-wiki-gigaword-50',\n",
              " 'glove-wiki-gigaword-100',\n",
              " 'glove-wiki-gigaword-200',\n",
              " 'glove-wiki-gigaword-300',\n",
              " 'glove-twitter-25',\n",
              " 'glove-twitter-50',\n",
              " 'glove-twitter-100',\n",
              " 'glove-twitter-200',\n",
              " '__testing_word2vec-matrix-synopsis']"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "list(gensim.downloader.info()['models'].keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpGwksYYjwQp",
        "outputId": "7b972b03-38f1-477d-e31e-83cd2bc61bdd"
      },
      "outputs": [],
      "source": [
        "word2vec_rus = gensim.downloader.load('word2vec-ruscorpora-300')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "iFF9A9059Ita",
        "outputId": "676eb91b-7b44-4712-8555-92d669427bf6"
      },
      "outputs": [
        {
          "ename": "KeyError",
          "evalue": "\"Key 'тест' not present\"",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[1;32mc:\\dev\\lecture_exercises\\nlp_embeddidngs.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/dev/lecture_exercises/nlp_embeddidngs.ipynb#W3sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m word2vec_rus[\u001b[39m'\u001b[39;49m\u001b[39mтест\u001b[39;49m\u001b[39m'\u001b[39;49m]\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\gensim\\models\\keyedvectors.py:403\u001b[0m, in \u001b[0;36mKeyedVectors.__getitem__\u001b[1;34m(self, key_or_keys)\u001b[0m\n\u001b[0;32m    389\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Get vector representation of `key_or_keys`.\u001b[39;00m\n\u001b[0;32m    390\u001b[0m \n\u001b[0;32m    391\u001b[0m \u001b[39mParameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    400\u001b[0m \n\u001b[0;32m    401\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    402\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(key_or_keys, _KEY_TYPES):\n\u001b[1;32m--> 403\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_vector(key_or_keys)\n\u001b[0;32m    405\u001b[0m \u001b[39mreturn\u001b[39;00m vstack([\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_vector(key) \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m key_or_keys])\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\gensim\\models\\keyedvectors.py:446\u001b[0m, in \u001b[0;36mKeyedVectors.get_vector\u001b[1;34m(self, key, norm)\u001b[0m\n\u001b[0;32m    422\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mget_vector\u001b[39m(\u001b[39mself\u001b[39m, key, norm\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m    423\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Get the key's vector, as a 1D numpy array.\u001b[39;00m\n\u001b[0;32m    424\u001b[0m \n\u001b[0;32m    425\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    444\u001b[0m \n\u001b[0;32m    445\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 446\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mget_index(key)\n\u001b[0;32m    447\u001b[0m     \u001b[39mif\u001b[39;00m norm:\n\u001b[0;32m    448\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfill_norms()\n",
            "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\gensim\\models\\keyedvectors.py:420\u001b[0m, in \u001b[0;36mKeyedVectors.get_index\u001b[1;34m(self, key, default)\u001b[0m\n\u001b[0;32m    418\u001b[0m     \u001b[39mreturn\u001b[39;00m default\n\u001b[0;32m    419\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 420\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mKey \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not present\u001b[39m\u001b[39m\"\u001b[39m)\n",
            "\u001b[1;31mKeyError\u001b[0m: \"Key 'тест' not present\""
          ]
        }
      ],
      "source": [
        "word2vec_rus['тест']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwWK8iuPk0i2",
        "outputId": "d75f34c2-b48f-49d1-ab88-681745b03757"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "word #0/184973 is весь_DET\n",
            "word #1/184973 is человек_NOUN\n",
            "word #2/184973 is мочь_VERB\n",
            "word #3/184973 is год_NOUN\n",
            "word #4/184973 is сказать_VERB\n",
            "word #5/184973 is время_NOUN\n",
            "word #6/184973 is говорить_VERB\n",
            "word #7/184973 is становиться_VERB\n",
            "word #8/184973 is знать_VERB\n",
            "word #9/184973 is самый_DET\n",
            "word #10/184973 is дело_NOUN\n",
            "word #11/184973 is день_NOUN\n",
            "word #12/184973 is жизнь_NOUN\n",
            "word #13/184973 is рука_NOUN\n",
            "word #14/184973 is очень_ADV\n",
            "word #15/184973 is первый_ADJ\n",
            "word #16/184973 is давать_VERB\n",
            "word #17/184973 is новый_ADJ\n",
            "word #18/184973 is слово_NOUN\n",
            "word #19/184973 is иметь_VERB\n"
          ]
        }
      ],
      "source": [
        "for index, word in enumerate(word2vec_rus.index_to_key ):\n",
        "    if index == 20:\n",
        "        break\n",
        "    print(f\"word #{index}/{len(word2vec_rus.index_to_key )} is {word}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_A3YgBs-nl2I",
        "outputId": "9083a628-827d-4724-d404-5d89d3e63b5a"
      },
      "outputs": [
        {
          "data": {
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              "      dtype=float32)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word2vec_rus['печь_VERB']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DixDIpbU9B24",
        "outputId": "d07bc19f-6ea5-4cdc-c979-ca5b83cd7aca"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "        1.94925219e-02, -1.01120956e-01, -2.13958360e-02,  3.57672907e-02,\n",
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              "       -4.46029417e-02,  3.90188433e-02,  9.91340727e-02,  1.53278485e-01,\n",
              "        8.53673667e-02, -5.91412149e-02, -6.21093251e-02,  1.17255161e-02,\n",
              "        5.28062582e-02,  5.82912751e-02,  7.34764934e-02,  4.62218150e-02,\n",
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              "       -3.69432606e-02, -8.39514807e-02,  6.30876794e-02, -1.28500862e-02,\n",
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              "       -5.95361553e-02, -1.63800403e-01, -1.22875012e-02,  1.06482416e-01,\n",
              "        6.07103156e-03, -5.12871891e-03,  1.98991708e-02, -7.69749982e-03,\n",
              "        4.29828130e-02,  1.55742029e-02,  2.85588466e-02,  5.05802222e-02,\n",
              "       -6.19536173e-03,  7.10553676e-02,  3.58315334e-02,  1.27903167e-02,\n",
              "       -7.35795796e-02, -1.15166157e-02,  1.90748628e-02, -2.27816384e-02,\n",
              "        7.04118144e-03, -1.25415623e-01,  4.86680306e-02,  1.20324492e-01,\n",
              "       -1.90788805e-02,  1.72033869e-02,  5.23368232e-02, -5.04792817e-02,\n",
              "        5.86587824e-02, -4.88322116e-02, -1.10781007e-02, -3.36438674e-03,\n",
              "        2.62496769e-02,  5.52049913e-02, -1.75047126e-02, -2.67160442e-02,\n",
              "        6.62052855e-02,  2.21071336e-02, -5.72599284e-02,  5.97622655e-02,\n",
              "        7.50152767e-02, -9.20058321e-03,  8.34535062e-02,  1.37469135e-02,\n",
              "        7.07960427e-02,  1.64752994e-02, -1.16385892e-01, -7.28768157e-03,\n",
              "       -9.54352096e-02,  8.52974951e-02, -1.07891314e-01,  5.46240024e-02,\n",
              "        1.18708938e-01,  9.56188329e-03,  1.27080768e-01, -4.77623157e-02,\n",
              "       -3.35567519e-02, -3.73203382e-02, -4.01373394e-02, -1.25895152e-02,\n",
              "       -3.66865620e-02,  2.44657304e-02,  2.79945973e-02,  1.26880845e-02,\n",
              "       -1.08274594e-01, -8.33899602e-02,  4.39459682e-02, -3.44437957e-02,\n",
              "       -8.40049889e-03,  1.08315177e-01, -2.27933247e-02, -2.37453077e-02,\n",
              "       -4.11164798e-02,  7.11826533e-02,  8.41675103e-02,  1.14128226e-02,\n",
              "       -3.97583768e-02, -1.64128430e-02,  8.25809781e-03, -9.04217246e-04,\n",
              "        3.18587013e-02,  1.96749121e-02, -4.55804206e-02, -3.58253345e-02,\n",
              "       -4.70046699e-02, -1.14834104e-02, -2.76539382e-02,  6.40464760e-03,\n",
              "        6.15377794e-04,  7.02709146e-03, -2.34442819e-02,  1.78269669e-03,\n",
              "        8.66848007e-02, -1.14074059e-01,  2.67470675e-03,  1.99027956e-02],\n",
              "      dtype=float32)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word2vec_rus['печь_NOUN']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "efVdJ1V8n7qz",
        "outputId": "c0f05f4c-76e4-4081-9d7e-f1341c2612f6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('печка_NOUN', 0.8358493447303772),\n",
              " ('подтопок_NOUN', 0.7017350792884827),\n",
              " ('печной_ADJ', 0.6326409578323364),\n",
              " ('полати_NOUN', 0.6188918948173523),\n",
              " ('топиться_VERB', 0.6180375218391418),\n",
              " ('горнушка_NOUN', 0.6172196865081787),\n",
              " ('топиться::по-черному_VERB', 0.6171697974205017),\n",
              " ('истапливать_VERB', 0.6131374835968018),\n",
              " ('топка_NOUN', 0.6026757955551147),\n",
              " ('печурка_NOUN', 0.6010894775390625)]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word2vec_rus.most_similar(['печь_NOUN'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbUbSZhK9gcs",
        "outputId": "0de557f3-e1ed-4ca4-fc0d-cf1d2822e97c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('напекать_VERB', 0.647373378276825),\n",
              " ('испечь_VERB', 0.6360712051391602),\n",
              " ('жарить_VERB', 0.6299607753753662),\n",
              " ('нардек_NOUN', 0.5905023813247681),\n",
              " ('выпекать_VERB', 0.5834865570068359),\n",
              " ('яшный_ADJ', 0.5677016377449036),\n",
              " ('варить_VERB', 0.5661611557006836),\n",
              " ('пирог_NOUN', 0.5652976036071777),\n",
              " ('тандыр_NOUN', 0.5573608875274658),\n",
              " ('месить::тесто_VERB', 0.5557463765144348)]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "word2vec_rus.most_similar(['печь_VERB'])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}