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            "\u001b[K     |████████████████████████████████| 115 kB 61.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 74.9 MB/s \n",
            "\u001b[?25h"
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        }
      ],
      "source": [
        "!pip install transformers sentencepiece datasets -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Downloading the corpus of prompts"
      ],
      "metadata": {
        "id": "p4we601YqkK1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "ds = load_dataset(\"daspartho/stable-diffusion-prompts\")\n",
        "ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385,
          "referenced_widgets": [
            "f1e9e880e9aa428cb721e628a37fe960",
            "4f65334dcf5547408c45613d6bb1cc0d",
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            "dfa2f92f282d43009b97adb87e0c4a09",
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            "d09457c1da3847ddbce2b810b1c522cc",
            "db9d627b0ebd47749319ea2f8c6d7605",
            "3efa34808aee40709edbafd776190f33",
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            "be8b44458feb480ea8898f7ac9076f2b",
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            "0e23f47f789d486d96f0aee61caaaed9",
            "0787a7bd519c4062af8dffdaf97ab7c7",
            "6a81731993cd472e876ec4fe30bfc8f6",
            "73cb8c390a1148ceb362ad4aa65a8eea",
            "b653b3d4dbec45378de19e72f8ae6af1",
            "bf45e226bf944c5fbac0b7258f466dda",
            "8a2b2cb1e1ce401b869025b2b0246310",
            "02d36d510f2b423dbb5027a42094aa8a",
            "511a79c8ff9947238d4035bf7e2c1000",
            "7c066a6f84114805b19bb586fe380898",
            "0b099d3f80324cb9b0ec3fa9a01ab9b4",
            "7d263e51af3941baa77b1bfeec1f6aab",
            "3eba6f4af9e94f43b24b5b794b2ee5c6",
            "db236f58f077440ab44fc324f42345fb",
            "61ead388dfe9489eb731889fa2317ffb",
            "5c7909030c4c455a8a02090498ea32ee",
            "d082fa80a5af45748e6dfe45384403d1",
            "4d0f608145c4483a965b310da2075547",
            "4ae8fc9b5a5c48c68ca9dd2e06c38484",
            "9500c642c67245e7ab7775c3915bd1fc",
            "b5aced09c9d749c5980706a9b6153430",
            "57dc22b4c3aa42bab25ca510d6a7cdc7",
            "1b0b9b8fbbe348e9b1056c7ce7d95652",
            "d0ef4f6bf6c84458a90254a6917edac9",
            "da79a697d99b43bab4d9b24e4c1040b0",
            "a1f0a3915462406dbe4a97c10e657740",
            "f8c4d3909fb24ff3a4d481a4acc8620e",
            "08c5c1a545f04a679ae0f605f1161752",
            "0e4b0b5f449e4356b7813892be31a900",
            "585f6157782c48b58ee8d4a7a0b21c56",
            "be47a7794e994c568e4a9628f2c265e1",
            "83be34a8139548ed87309c5ef47c42bc",
            "94b4e38e899f445cb97ddc80e7fcc5a4",
            "24fa859d1bd345a49e55d080c28d6eae",
            "bf744d2087e44c71b9399062cf154c32",
            "fa34e23034d44f3ca47f91d0888a098f",
            "c1602183756d48ff823502e4e9e9f274",
            "7aafaa86cca344369e4b685daaf2cca9",
            "130837e7fed4466baf8b59c1e5ce57e2",
            "acad16c931b4454ca1b29ae7e5070d5f",
            "34fa586edcc646cbb37e9e370d8c9fe8",
            "1d50bae6f96f4ccdb458d910acd89c41",
            "b532298420d74b4ead4c81c4955d15b0",
            "90a3be56e89a4cf8a8be09465bb82ce3",
            "a0177c9c73054e8e92c98dadf3f7009b",
            "d8a4b87853cb488ba6418337e588d296",
            "4a2bd74f5f024738a3fbebebe1976020",
            "da82fe7ac4a441569b32065c85b9dcd4",
            "497aa60ebdda40e3abf71f1f77289da4",
            "0b79718338744bde8419028e7d2c70f2",
            "0f4b69d941a54f28978825cc255b7cb2"
          ]
        },
        "id": "EoTp6m9Pp_FO",
        "outputId": "76e1fc41-d757-4033-ca4b-db0c0043c90f"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/426 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1e9e880e9aa428cb721e628a37fe960"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration daspartho--stable-diffusion-prompts-5637e444d3df76f9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/daspartho___parquet/daspartho--stable-diffusion-prompts-5637e444d3df76f9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a49ec8649de4a60a650107afaf87a4b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/102M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02d36d510f2b423dbb5027a42094aa8a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4ae8fc9b5a5c48c68ca9dd2e06c38484"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/1819808 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "585f6157782c48b58ee8d4a7a0b21c56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/daspartho___parquet/daspartho--stable-diffusion-prompts-5637e444d3df76f9/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34fa586edcc646cbb37e9e370d8c9fe8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['prompt'],\n",
              "        num_rows: 1819808\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example = ds['train'][0]['prompt']\n",
        "example"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "z04egzIZwYUF",
        "outputId": "53ce3db4-14c4-414d-d185-f594b9c60f70"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'beautiful porcelain ivory fair face woman biomechanical cyborg, close - up, sharp focus, studio light, iris van herpen haute couture headdress made of rhizomorphs, daisies, brackets, colorful corals, fractal mushrooms, puffballs, octane render, ultra sharp, 8 k '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Transform the dataset into an iterator of batches of prompts"
      ],
      "metadata": {
        "id": "omxPwW2lrxcG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_training_corpus():\n",
        "    return (\n",
        "        ds[\"train\"][i : i + 1000][\"prompt\"]\n",
        "        for i in range(0, len(ds[\"train\"]), 1000)\n",
        "        )\n",
        "\n",
        "training_corpus = get_training_corpus()"
      ],
      "metadata": {
        "id": "XoaTtMXNqvbR"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load the tokenizer"
      ],
      "metadata": {
        "id": "Dj9GKzWXxgDe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "old_tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")"
      ],
      "metadata": {
        "id": "tZMwQJ2NrEOs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "0bf4cfd1752d4dfa929a36c668d6c1f3",
            "bae592edb8014e38bee1ec8d9326e73f",
            "9dd1266cb0114dd08b4782ecc98f335a",
            "d1fa2d7e72e74130934736ba8e4ae286",
            "f0fceb05f61444f3909831af6ee1e0ad",
            "ca9fe8cca85c472cb52bbfc06775a666",
            "f9ca127375e54b8c9a049c45485e55f4",
            "1e081d4d7632408da813d498bd35d580",
            "e8b1595e84b14a1d834f65bdbb985a85",
            "a41c396b3c2c4e4083310b02d06bcfb6",
            "2889122f1f4e47329904aaa3e85ff6fc",
            "ed98d1cd5e2b415fad774507219b9963",
            "32f47e0c714940aa9faf3b5b24a1bca3",
            "52fd54a5dcbc47beb373cff57d833501",
            "2062aa49c86046ddbbb4943b38a65243",
            "da48d55fd6aa487a8cc2104270260596",
            "2029d1d83693462db414a1afc544dd95",
            "858bab4cc91648d0babf463b2872bbe6",
            "73563d0d31f94efc89d0f9e7bf8ff0a2",
            "1c5617f882f94ca9bb4a359951f7bc6b",
            "492a6ecf3ce44d6d8c7ee1e3c44de6c8",
            "6fb843a8613846f5b718e4ec5033b225",
            "d55a56e1188b4818a6bcd04c8bc6a28e",
            "9522500e205842e58bc8fb6341a77c57",
            "903a08c0bee942abaee790a10fe85f41",
            "02e7ed804e3048c99e19177abc955cdd",
            "0fec32a8645e4383a8d59a1858f2b43c",
            "7d481434ad7f445a8aca8fbb3dbbf6a8",
            "1c76da6f82994b33bf70973036463f74",
            "843d26a5d71040a3bdd5cbf09209810a",
            "2d1124e01cf24f10a0aba6bd572e7415",
            "588c0edaf37244959935a668741b9911",
            "4ed1b6fcb06743d285a24f8cb7ffb012",
            "b1152f5866ea43559f7e7e36bf26bf80",
            "02fd8877871746c3a9191c7c9d7205a6",
            "b6faca0c08184deabcf5c0f1cee1f888",
            "d17f2f49d43644ab9d3e681e65fd57cc",
            "39affcdfc0d04fdf8828b56d938de87d",
            "f3b1ccb13b6c4f5ebc9468d87eed77bc",
            "9a20c0e095c94e6c96b18cb79cf3d87b",
            "fc0996e922fd4e76a8498219c3756128",
            "a94634f895a74ffab2d192c5c9b240b3",
            "72fb6001046841d79804e170397d02a4",
            "09bfbecb8e22463399d1bd453268ecee"
          ]
        },
        "outputId": "3d15fe62-6ce8-4a5c-bd80-0470b150a937"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bf4cfd1752d4dfa929a36c668d6c1f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed98d1cd5e2b415fad774507219b9963"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d55a56e1188b4818a6bcd04c8bc6a28e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1152f5866ea43559f7e7e36bf26bf80"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's how if performs before training"
      ],
      "metadata": {
        "id": "DtjA9_CgxVr0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = old_tokenizer.tokenize(example)\n",
        "tokens, len(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-_KxtE2wuGT",
        "outputId": "1bf8f343-c347-46ab-8cd7-0c139f9294a2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['beaut',\n",
              "  'iful',\n",
              "  'Ġpor',\n",
              "  'cel',\n",
              "  'ain',\n",
              "  'Ġivory',\n",
              "  'Ġfair',\n",
              "  'Ġface',\n",
              "  'Ġwoman',\n",
              "  'Ġbiome',\n",
              "  'chan',\n",
              "  'ical',\n",
              "  'Ġcy',\n",
              "  'borg',\n",
              "  ',',\n",
              "  'Ġclose',\n",
              "  'Ġ-',\n",
              "  'Ġup',\n",
              "  ',',\n",
              "  'Ġsharp',\n",
              "  'Ġfocus',\n",
              "  ',',\n",
              "  'Ġstudio',\n",
              "  'Ġlight',\n",
              "  ',',\n",
              "  'Ġir',\n",
              "  'is',\n",
              "  'Ġvan',\n",
              "  'Ġher',\n",
              "  'pen',\n",
              "  'Ġha',\n",
              "  'ute',\n",
              "  'Ġcout',\n",
              "  'ure',\n",
              "  'Ġhead',\n",
              "  'dress',\n",
              "  'Ġmade',\n",
              "  'Ġof',\n",
              "  'Ġrh',\n",
              "  'iz',\n",
              "  'omorph',\n",
              "  's',\n",
              "  ',',\n",
              "  'Ġda',\n",
              "  'is',\n",
              "  'ies',\n",
              "  ',',\n",
              "  'Ġbrackets',\n",
              "  ',',\n",
              "  'Ġcolorful',\n",
              "  'Ġcor',\n",
              "  'als',\n",
              "  ',',\n",
              "  'Ġfract',\n",
              "  'al',\n",
              "  'Ġmushrooms',\n",
              "  ',',\n",
              "  'Ġpuff',\n",
              "  'balls',\n",
              "  ',',\n",
              "  'Ġoct',\n",
              "  'ane',\n",
              "  'Ġrender',\n",
              "  ',',\n",
              "  'Ġultra',\n",
              "  'Ġsharp',\n",
              "  ',',\n",
              "  'Ġ8',\n",
              "  'Ġk',\n",
              "  'Ġ'],\n",
              " 70)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Training a new tokenizer"
      ],
      "metadata": {
        "id": "4A0582gDx5-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = old_tokenizer.train_new_from_iterator(training_corpus, 52000)"
      ],
      "metadata": {
        "id": "7Fey6LJDxypX"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Let's see how the trained tokenizer performs"
      ],
      "metadata": {
        "id": "OJ3wxgNKyzbP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokens = tokenizer.tokenize(example)\n",
        "tokens, len(tokens)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUzb1ozkykjE",
        "outputId": "b83859de-7699-4453-c6ff-d1e832f39623"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(['beautiful',\n",
              "  'Ġporcelain',\n",
              "  'Ġivory',\n",
              "  'Ġfair',\n",
              "  'Ġface',\n",
              "  'Ġwoman',\n",
              "  'Ġbiomechanical',\n",
              "  'Ġcyborg',\n",
              "  ',',\n",
              "  'Ġclose',\n",
              "  'Ġ-',\n",
              "  'Ġup',\n",
              "  ',',\n",
              "  'Ġsharp',\n",
              "  'Ġfocus',\n",
              "  ',',\n",
              "  'Ġstudio',\n",
              "  'Ġlight',\n",
              "  ',',\n",
              "  'Ġiris',\n",
              "  'Ġvan',\n",
              "  'Ġherpen',\n",
              "  'Ġhaute',\n",
              "  'Ġcouture',\n",
              "  'Ġheaddress',\n",
              "  'Ġmade',\n",
              "  'Ġof',\n",
              "  'Ġrhizomorphs',\n",
              "  ',',\n",
              "  'Ġdaisies',\n",
              "  ',',\n",
              "  'Ġbrackets',\n",
              "  ',',\n",
              "  'Ġcolorful',\n",
              "  'Ġcorals',\n",
              "  ',',\n",
              "  'Ġfractal',\n",
              "  'Ġmushrooms',\n",
              "  ',',\n",
              "  'Ġpuffballs',\n",
              "  ',',\n",
              "  'Ġoctane',\n",
              "  'Ġrender',\n",
              "  ',',\n",
              "  'Ġultra',\n",
              "  'Ġsharp',\n",
              "  ',',\n",
              "  'Ġ8',\n",
              "  'Ġk',\n",
              "  'Ġ'],\n",
              " 50)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Saving the tokenizer"
      ],
      "metadata": {
        "id": "2lI2I57dzVWG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained(\"prompt-tokenizer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MWMiJB7gzKg_",
        "outputId": "9a4a3694-0db7-4756-d013-b2991f5f5f0d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('prompt-tokenizer/tokenizer_config.json',\n",
              " 'prompt-tokenizer/special_tokens_map.json',\n",
              " 'prompt-tokenizer/vocab.json',\n",
              " 'prompt-tokenizer/merges.txt',\n",
              " 'prompt-tokenizer/added_tokens.json',\n",
              " 'prompt-tokenizer/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Uploading the tokenizer to HuggingFace Hub"
      ],
      "metadata": {
        "id": "OkCz4wCRztbK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Be sure to login with your auth token below to push the tokenizer to Hub"
      ],
      "metadata": {
        "id": "kPBs6OR6z1lV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "Ow35d8CdzatL",
        "outputId": "41fe653e-999c-4373-bb96-413923ca265a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
          "referenced_widgets": [
            "d9f4fff0e1314a83a6b58ee6aa2ac039",
            "99aad7ce3f4e47429efe614af426d66c",
            "4bc242a288c341489ef0292c465c4072",
            "6de2c82e3f8a4bbe94aee24558b0e943",
            "a280d413768d44c2802a70d3cc8d7072",
            "8dcb697b86fb4774bf6fa695941574e4",
            "f543aab8670b464a8288a39df191e842",
            "bc008d55f4e84d1cb6a3fda07d31ac68",
            "00864c2cbc7c48299b639f587f0aa801",
            "09a6634bf0874dfcb79a835441fe195f",
            "2688a261ddc84aefaec4f1c03c552cf9",
            "2bb2d545db224877a8ff0c5f0a1892d1",
            "735f523e59524bec8b99ab283141f702",
            "da04fd37fb164f9f86f232d2ff8ee78c",
            "5e45448a936b48a197834ab5b2e7b69f",
            "6cd81c36f3c44bc9bdb65548be9d9750",
            "1c08da741825464cbfc8ed1d9c67fcd6"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid.\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(\"prompt-tokenizer\")"
      ],
      "metadata": {
        "id": "PE_1E-aUze-B",
        "outputId": "74a7ac62-c455-40a5-df7d-dc5e09cd57de",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/daspartho/prompt-tokenizer/commit/5a57ceb314dfd4622e4137237c30dc76f8250508', commit_message='Upload tokenizer', commit_description='', oid='5a57ceb314dfd4622e4137237c30dc76f8250508', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}