Vocab_comparison.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gojiteji/AI_vocab_comparison/blob/main/Vocab_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lIYdn1woOS1n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2eaa0bb-a977-46ce-bbd8-1e585ff900ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: transformers[sentencepiece] in /usr/local/lib/python3.8/dist-packages (4.25.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (6.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (4.64.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (1.21.6)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.13.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (3.8.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (21.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (2.23.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.11.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (2022.6.2)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92,>=0.1.91 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (0.1.97)\n",
            "Requirement already satisfied: protobuf<=3.20.2 in /usr/local/lib/python3.8/dist-packages (from transformers[sentencepiece]) (3.19.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers[sentencepiece]) (4.4.0)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers[sentencepiece]) (3.0.9)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers[sentencepiece]) (2022.9.24)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers[sentencepiece]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer,T5Tokenizer\n",
        "T5tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n",
        "mT5tokenizer = AutoTokenizer.from_pretrained(\"google/mt5-small\")"
      ],
      "metadata": {
        "id": "Jh7P9pPzdwpn"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "T5vocab = T5tokenizer.convert_ids_to_tokens(range(T5tokenizer.vocab_size))\n",
        "mT5vocab = mT5tokenizer.convert_ids_to_tokens(range(mT5tokenizer.vocab_size))"
      ],
      "metadata": {
        "id": "uFBeXuuBd8b-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib_venn import venn2,venn3\n",
        "from matplotlib import pyplot\n",
        "colors=['red','green']\n",
        "v=venn2(subsets=([set(T5vocab),set(mT5vocab)]),set_labels = ('T5', 'mT5'),set_colors=colors)\n",
        "i = 0\n",
        "for text in v.set_labels:\n",
        "  text.set_color(colors[i])\n",
        "  i+=1\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "16oKz54hejAW",
        "outputId": "dafcdfb0-ebdf-430e-9d58-746fcf481976"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mBART25tokenizer = AutoTokenizer.from_pretrained(\"facebook/mbart-large-cc25\")\n",
        "mBART50tokenizer = AutoTokenizer.from_pretrained(\"facebook/mbart-large-50\")\n",
        "mBART25vocab = mBART25tokenizer.convert_ids_to_tokens(range(mBART25tokenizer.vocab_size))\n",
        "mBART50vocab = mBART50tokenizer.convert_ids_to_tokens(range(mBART50tokenizer.vocab_size))\n",
        "mT5vocab = mT5tokenizer.convert_ids_to_tokens(range(mT5tokenizer.vocab_size))"
      ],
      "metadata": {
        "id": "RResTcwFet_b"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colors=['red','blue','green']\n",
        "v=venn3(subsets=([set(mBART25vocab),set(mBART50vocab),set(mT5vocab)]),set_labels = ( 'mBART25','mBART50','mT5'),set_colors=colors)\n",
        "i = 0\n",
        "for text in v.set_labels:\n",
        "  text.set_color(colors[i])\n",
        "  i+=1\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "id": "y_wEHJgtjB8g",
        "outputId": "b85f243c-40f9-4431-8a4a-1649a4f09e0a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(mBART50vocab)-set(mBART25vocab) & set(mBART50vocab)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IblR_EWFum3Z",
        "outputId": "4d3918e7-2180-4edf-e0c8-a653bffa91c1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'af_ZA',\n",
              " 'az_AZ',\n",
              " 'bn_IN',\n",
              " 'fa_IR',\n",
              " 'gl_ES',\n",
              " 'he_IL',\n",
              " 'hr_HR',\n",
              " 'id_ID',\n",
              " 'ka_GE',\n",
              " 'km_KH',\n",
              " 'mk_MK',\n",
              " 'ml_IN',\n",
              " 'mn_MN',\n",
              " 'mr_IN',\n",
              " 'pl_PL',\n",
              " 'ps_AF',\n",
              " 'pt_XX',\n",
              " 'sl_SI',\n",
              " 'sv_SE',\n",
              " 'sw_KE',\n",
              " 'ta_IN',\n",
              " 'te_IN',\n",
              " 'th_TH',\n",
              " 'tl_XX',\n",
              " 'uk_UA',\n",
              " 'ur_PK',\n",
              " 'xh_ZA'}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "GPTJtokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
        "GPT2tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
      ],
      "metadata": {
        "id": "tF-YPpabjrhz"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "GPTJvocab = GPTJtokenizer.convert_ids_to_tokens(range(GPTJtokenizer.vocab_size))\n",
        "GPT2vocab = GPT2tokenizer.convert_ids_to_tokens(range(GPT2tokenizer.vocab_size))"
      ],
      "metadata": {
        "id": "CnmjuuxKmK1y"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colors=['red','blue']\n",
        "v=venn2(subsets=([set(GPTJvocab),set(GPT2vocab)]),set_labels = ( 'GPT-J','GPT2'),set_colors=colors)\n",
        "i = 0\n",
        "for text in v.set_labels:\n",
        "  text.set_color(colors[i])\n",
        "  i+=1\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "eRyE-j9Al0_E",
        "outputId": "04e5d864-fe91-4e11-8cda-3c31d861b378"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GPT2jatokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-roberta-base\")\n",
        "!pip install fugashi ipadic\n",
        "BERTjatokenizer = AutoTokenizer.from_pretrained(\"cl-tohoku/bert-base-japanese\")\n",
        "T5jatokenizer = AutoTokenizer.from_pretrained(\"sonoisa/t5-base-japanese\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167,
          "referenced_widgets": [
            "74118e37cf4e46fbb1366d42cd42e1cd",
            "47b8484c309b4b929c9966cf485f254e",
            "d1163862a21c4de1abb2d3693f8b3dc0",
            "6b360e27e64345169c83e0eb13d3753a",
            "8ab05bd80dbc42f08a535ba381773ad2",
            "46c314619c43420ca4f2ce9c08ac76d1",
            "630270c4bef048c88cf8b826b82de7b3",
            "35089674cc3849ccb3f8179fe473aa4f",
            "f16d690f8c03499495c31454b77e8a57",
            "8bef82a033e64275a27f30f8006a3cf7",
            "bc42d6a5598e4598a64f1dda4d8b527a",
            "c2b5e9d2b19146d088b200dcfb619d95",
            "23c6afe915494586855a56a00a0980b3",
            "6cb3e00e151e4f3ba98e2d5c3a69cf38",
            "15d15843b65640d4b06a841bce2f026d",
            "08cce45ea9c949c797a270d190edbad0",
            "416d406a3d9948a892281a25b141137a",
            "105691e6aefd4810a701603fbc1e617b",
            "f910d7b259564e1eb725469946ffa3af",
            "24398e0209f842b5b97ba04efd6ad3e8",
            "46d4d975d158416fbab1bfe26e610781",
            "0e92c825bb28474ca7a778faeddaa501",
            "167e85262e3e4b3885d53ddbc29dff96",
            "e160cc32f7bb42b6895cf9da5856baaa",
            "7e69ddf604e544cba8d07e6c9cabfb1b",
            "7fb9039e979f437ab3cce4d21928f646",
            "ec1cf1c6cf4c45bfbfc67296b9a04408",
            "f7e8c84b4feb44dd97cb0475b57b1cce",
            "28d377ba65ce48279f55cf0a8bca4a60",
            "4630a78a2322493e82b9837e59650a8b",
            "43ce9b7326424e6981b127e5848f4de5",
            "183975bfd658476eb614e388bf477fc5",
            "deea4bbf26c545c68250baac4a583cb1"
          ]
        },
        "id": "yytOZ6Xind6s",
        "outputId": "a5f020be-903c-44b5-e734-590f63234d54"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/806k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74118e37cf4e46fbb1366d42cd42e1cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/153 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c2b5e9d2b19146d088b200dcfb619d95"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/259 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "167e85262e3e4b3885d53ddbc29dff96"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: fugashi in /usr/local/lib/python3.8/dist-packages (1.2.1)\n",
            "Requirement already satisfied: ipadic in /usr/local/lib/python3.8/dist-packages (1.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "GPT2javocab =[(v[1:] if v[0]==\"▁\" else v )for v in GPT2jatokenizer.convert_ids_to_tokens(range(GPT2jatokenizer.vocab_size))]\n",
        "BERTjavocab =[(v[2:] if v[:2]==\"##\" else v )for v in BERTjatokenizer.convert_ids_to_tokens(range(BERTjatokenizer.vocab_size))]\n",
        "T5javocab = [(v[1:] if v[0]==\"▁\" else v )for v in T5jatokenizer.convert_ids_to_tokens(range(T5jatokenizer.vocab_size))]"
      ],
      "metadata": {
        "id": "buSzur9-n9iG"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colors=['red','blue','green']\n",
        "v=venn3(subsets=([set(GPT2javocab),set(BERTjavocab),set(T5javocab)]),set_labels = ( 'GPT2-ja','BERT-ja','T5-ja'),set_colors=colors)\n",
        "i = 0\n",
        "for text in v.set_labels:\n",
        "  text.set_color(colors[i])\n",
        "  i+=1\n",
        "pyplot.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "A5jIoDGXoX7E",
        "outputId": "7ec3bed6-db1c-4e9a-d3d5-92dce891ed42"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "set(T5javocab)-(set(GPT2javocab) & set(T5javocab))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixpsg_KT0PIq",
        "outputId": "5b46dd42-4aab-49d5-adf8-5f039f4a73b8"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ホリ',\n",
              " 'にはならない',\n",
              " '-2005',\n",
              " '秋田市',\n",
              " '瑞宝章',\n",
              " 'ngc',\n",
              " '深夜番組',\n",
              " 'stream',\n",
              " '2〜3',\n",
              " 'come',\n",
              " 'に入省',\n",
              " '幸い',\n",
              " '鈞',\n",
              " 'any',\n",
              " '意見を',\n",
              " 'ことを考え',\n",
              " 'を負って',\n",
              " 'を提唱した',\n",
              " 'ゆかり',\n",
              " '1-3',\n",
              " 'デヒドロゲナーゼ',\n",
              " 'らしめ',\n",
              " 'wiki',\n",
              " '臘',\n",
              " 'tru',\n",
              " '二輪車',\n",
              " '夏に',\n",
              " '主戦場',\n",
              " 'ログイン',\n",
              " 'に売却',\n",
              " 'ですが',\n",
              " 'mura',\n",
              " '〜5',\n",
              " '--2005',\n",
              " 'look',\n",
              " 'アミド',\n",
              " 'ction',\n",
              " 'ピリ',\n",
              " 'を起こさ',\n",
              " '行こう',\n",
              " 'ーション',\n",
              " '井戸端',\n",
              " 'golden',\n",
              " 'ハシ',\n",
              " 'ようで',\n",
              " '<extra_id_47>',\n",
              " 'スマ',\n",
              " 'の教員',\n",
              " '訃報',\n",
              " '久美子',\n",
              " 'のカテゴリ',\n",
              " '<extra_id_25>',\n",
              " 'ジンバブエ',\n",
              " 'を見ると',\n",
              " 'sai',\n",
              " 'ケネス',\n",
              " 'なりません',\n",
              " '掛かった',\n",
              " '読んで',\n",
              " 'テット',\n",
              " '良質な記事の選考',\n",
              " '歳の若さで',\n",
              " '選手に関するカテゴリ',\n",
              " '囚われ',\n",
              " '同志社',\n",
              " 'だけを',\n",
              " 'メインキャラクター',\n",
              " '舞う',\n",
              " '届いた',\n",
              " 'になります',\n",
              " 'どの程度',\n",
              " 'その直後',\n",
              " '<extra_id_10>',\n",
              " 'ちゅうけいきょく',\n",
              " 'とりあえず',\n",
              " '内訳',\n",
              " '決定した',\n",
              " '工学科',\n",
              " '<extra_id_11>',\n",
              " 'に集中',\n",
              " '本件',\n",
              " 'ノーベル賞',\n",
              " '正当',\n",
              " 'まして',\n",
              " '<extra_id_14>',\n",
              " 'が出て',\n",
              " '知っていた',\n",
              " 'そこには',\n",
              " 'sal',\n",
              " '<extra_id_31>',\n",
              " '見送られ',\n",
              " '1/4',\n",
              " 'ポータル',\n",
              " '<extra_id_89>',\n",
              " 'できます',\n",
              " '対立する',\n",
              " 'sei',\n",
              " 'アダルトビデオ',\n",
              " 'ディレクトリ',\n",
              " '<extra_id_44>',\n",
              " 'の大字',\n",
              " '<extra_id_85>',\n",
              " '枇',\n",
              " 'ヴィット',\n",
              " '向かわせ',\n",
              " 'even',\n",
              " '大学在学中',\n",
              " 'ashi',\n",
              " 'fuku',\n",
              " '色が',\n",
              " '-2012',\n",
              " 'ヨークシャー',\n",
              " 'hill',\n",
              " '済ませ',\n",
              " 'の門人',\n",
              " 'tl',\n",
              " '人文科学',\n",
              " \"(')\",\n",
              " '過去ログ',\n",
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