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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Install the modified `dom-tokenizers`"
      ],
      "metadata": {
        "id": "p4cA_Al2R8Fm"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxuqwFstR6UD",
        "outputId": "ce2d7275-520d-44fe-a48f-e570a08cab35"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'dom-tokenizers'...\n",
            "remote: Enumerating objects: 613, done.\u001b[K\n",
            "remote: Counting objects: 100% (187/187), done.\u001b[K\n",
            "remote: Compressing objects: 100% (80/80), done.\u001b[K\n",
            "remote: Total 613 (delta 118), reused 144 (delta 96), pack-reused 426\u001b[K\n",
            "Receiving objects: 100% (613/613), 153.44 KiB | 946.00 KiB/s, done.\n",
            "Resolving deltas: 100% (335/335), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/gbenson/dom-tokenizers.git -b emit-unique-tokens-0.0.18-pre1 --single-branch"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cd dom-tokenizers && pip install -e .[train]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iURZKR40UCxY",
        "outputId": "96304e65-e45c-40c4-cd22-c2f032da8200",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
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          "text": [
            "Obtaining file:///content/dom-tokenizers\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting python-magic (from dom-tokenizers==0.0.18)\n",
            "  Downloading python_magic-0.4.27-py2.py3-none-any.whl (13 kB)\n",
            "Requirement already satisfied: tokenizers in /usr/local/lib/python3.10/dist-packages (from dom-tokenizers==0.0.18) (0.19.1)\n",
            "Collecting unidecode (from dom-tokenizers==0.0.18)\n",
            "  Downloading Unidecode-1.3.8-py3-none-any.whl (235 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m235.5/235.5 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting vec64>0.0.2 (from dom-tokenizers==0.0.18)\n",
            "  Downloading vec64-0.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13 kB)\n",
            "Collecting datasets (from dom-tokenizers==0.0.18)\n",
            "  Downloading datasets-2.20.0-py3-none-any.whl (547 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (from dom-tokenizers==0.0.18) (9.4.0)\n",
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            "Collecting pyarrow>=15.0.0 (from datasets->dom-tokenizers==0.0.18)\n",
            "  Downloading pyarrow-16.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (40.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets->dom-tokenizers==0.0.18) (0.6)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets->dom-tokenizers==0.0.18)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "Collecting xxhash (from datasets->dom-tokenizers==0.0.18)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets->dom-tokenizers==0.0.18)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (23.2.0)\n",
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            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->dom-tokenizers==0.0.18) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.21.2->datasets->dom-tokenizers==0.0.18) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets->dom-tokenizers==0.0.18) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->dom-tokenizers==0.0.18) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets->dom-tokenizers==0.0.18) (1.16.0)\n",
            "Building wheels for collected packages: dom-tokenizers\n",
            "  Building editable for dom-tokenizers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dom-tokenizers: filename=dom_tokenizers-0.0.18-0.editable-py3-none-any.whl size=7367 sha256=874e55c8a7d8ef3a1e23890fd293fb93357b316bbec0803c196872264e5c356c\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_kdcdqaq/wheels/ab/e4/d2/9b9419db21d26514602a3dbb7582a3c195f9229169192ca43b\n",
            "Successfully built dom-tokenizers\n",
            "Installing collected packages: xxhash, vec64, unidecode, requests, python-magic, pyarrow, dill, multiprocess, dom-tokenizers, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 dom-tokenizers-0.0.18 multiprocess-0.70.16 pyarrow-16.1.0 python-magic-0.4.27 requests-2.32.3 unidecode-1.3.8 vec64-0.0.3 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train a tokenizer on `gbenson/webui-dom-snapshots`\n",
        "\n",
        "We don't need the tokenizer, but this `dom-tokenizers` is modified to output everything [`vec64`](https://pypi.org/project/vec64/) matched into a file as a side-effect."
      ],
      "metadata": {
        "id": "aXQW8CBmVOa4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!train-tokenizer -n30000 -N4536 gbenson/webui-dom-snapshots"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CqHIS-8xVVKe",
        "outputId": "5ee13972-5f24-4af5-cb90-642267d66250"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output directory: /content/dom-tokenizer-30k\n",
            "\n",
            "Downloading readme: 100% 9.16k/9.16k [00:00<00:00, 22.8MB/s]\n",
            "tokenizer_config.json: 100% 48.0/48.0 [00:00<00:00, 300kB/s]\n",
            "config.json: 100% 570/570 [00:00<00:00, 2.86MB/s]\n",
            "vocab.txt: 100% 232k/232k [00:00<00:00, 4.84MB/s]\n",
            "tokenizer.json: 100% 466k/466k [00:00<00:00, 2.32MB/s]\n",
            "Generating up to 30,000 tokens from 4,536 examples:\n",
            "\u001b[2K[00:00:01] Tokenize words                 ██████████████████ 845289   /   845289\n",
            "\u001b[2K[00:01:01] Count pairs                    ██████████████████ 845289   /   845289\n",
            "\u001b[2K[00:00:12] Compute merges                 ██████████████████ 23908    /    23908\n",
            "\n",
            "['training', 'complete']\n",
            "['token', '##izer', 'state', 'saved']\n",
            "['see', 'you', 'soon', '##!', '##!']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merge the new tokens into the existing dataset"
      ],
      "metadata": {
        "id": "Uo_FraY1W5IK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "old_tokens = {row[\"text\"] for row in load_dataset(\"gbenson/webui-tokens-unlabelled\")[\"train\"]}\n",
        "print(len(old_tokens))"
      ],
      "metadata": {
        "id": "L_oxSzmFW-nL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5fb5717-f4a2-41b1-e3c9-5a3a0c1aa667"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1194369\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"unique-tokens\") as fp:\n",
        "    new_tokens = {line.rstrip() for line in fp.readlines()}\n",
        "print(len(new_tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PsGE3OvBAaJ",
        "outputId": "0eb60459-8bca-4dcf-ba66-4d69b4eabedd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1446447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_tokens.update(old_tokens)\n",
        "print(len(new_tokens))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcr4ht1BBh9r",
        "outputId": "211021c9-c707-457a-dcf0-21f32272d1c3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1616815\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas import DataFrame\n",
        "df = DataFrame.from_dict({\"text\": list(new_tokens)})\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HMwB6q-cCD-S",
        "outputId": "f9fcfcfc-3960-4b78-f903-f768b73567e0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                text\n",
              "0                                             504869\n",
              "1  mE7Ke2EFHY29Tk2R8kYkFqDPDfonKFMrc0EeB1dYm6jHX8...\n",
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              "\n",
              "  @keyframes spin {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "dataset = Dataset.from_pandas(df)"
      ],
      "metadata": {
        "id": "juVr3_L5D3LC"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Upload to Hugging Face"
      ],
      "metadata": {
        "id": "sqbaaByQYG5q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.push_to_hub(\"gbenson/webui-tokens-unlabelled\")"
      ],
      "metadata": {
        "id": "ICObGLZvYHRZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165,
          "referenced_widgets": [
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        },
        "outputId": "72ab0158-bade-43e5-f01c-23d4498f7792"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4bbee348a78f44d39bb51049b5ee9646"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/1617 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d572e70402f54b23a317ea821142a439"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/1.18k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f63cdc1af818446e9a0c34a47f48e33f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co./datasets/gbenson/webui-tokens-unlabelled/commit/c22d8f38aec9d30666a8b159b12c20255fb67362', commit_message='Upload dataset', commit_description='', oid='c22d8f38aec9d30666a8b159b12c20255fb67362', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "U5p8o08kE2hC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}