{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435,
          "referenced_widgets": [
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            "3f2364ce10f94db8b12ed6c5eb199c78",
            "8dbd9d5b71f243bd8db94d813c505cd7",
            "0f583228eab24c52b477e5798ca4d8f9",
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            "a46f160b861540b593ee16e2751a0cbc",
            "ebf2907bcdfa446e813231a9de833f4e"
          ]
        },
        "id": "lIYdn1woOS1n",
        "outputId": "96749567-af76-43cd-e932-3ee35d81f1e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m23.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m35.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m26.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ddb825cc7884ed8a5aa0abde4e8efd2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install -U -q transformers datasets accelerate sentencepiece\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pprint import pprint\n",
        "from datasets import get_dataset_config_names\n",
        "\n",
        "def fetch_and_print_configs(dataset_name):\n",
        "    \"\"\"helper fn to print configs\"\"\"\n",
        "    configs = get_dataset_config_names(dataset_name)\n",
        "\n",
        "    print(f\"Available configurations for dataset '{dataset_name}':\")\n",
        "    pprint(configs, width=80, compact=True)\n",
        "\n",
        "ds_name = \"math-ai/AutoMathText\"\n",
        "fetch_and_print_configs(ds_name)\n",
        "\n",
        "# Available configurations for dataset 'BEE-spoke-data/knowledge-inoc-concat-v1':\n",
        "# ['default', 'default-0919', 'smorgasbord', 'tb-interleaved-deduped',\n",
        "# 'tb-interleaved-deduped-0919', 'tiny-tb-interleaved']"
      ],
      "metadata": {
        "id": "H1oesXUkLnOg",
        "outputId": "442f454f-c45a-4183-ddc6-1a9544bc34b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "1ff213b83a9d48509c006efb4bd96a83",
            "b13cdb911b204885bf3ca765a0fede08",
            "7bb0e3ffd4ec418c8a45e42a3d98a8fe",
            "931c0c10b5664ce89f36e5e5d05f05ed",
            "aec5843f84d24bc09a60e95bd95617c8",
            "ac0867563a85443aa0679c4cdb29ab92",
            "aedf88fd692a48c0b8f6ddee329e1d08",
            "a5b1097c11454afe8c93923cad8b6368",
            "4fef4e36a69e4e248b4b362955a428ec",
            "b552589673cd4f6784a2af25373bcf48",
            "17cd04ab6c4d480c8e3b6174aa1b6da8"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/16.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ff213b83a9d48509c006efb4bd96a83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available configurations for dataset 'math-ai/AutoMathText':\n",
            "['web-0.50-to-1.00', 'web-0.60-to-1.00', 'web-0.70-to-1.00', 'web-0.80-to-1.00',\n",
            " 'web-full', 'arxiv-0.50-to-1.00', 'arxiv-0.60-to-1.00', 'arxiv-0.70-to-1.00',\n",
            " 'arxiv-0.80-to-1.00', 'arxiv-full', 'code-0.50-to-1.00',\n",
            " 'code-python-0.50-to-1.00', 'code-python-0.60-to-1.00',\n",
            " 'code-python-0.70-to-1.00', 'code-python-0.80-to-1.00', 'code-full']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "\n",
        "config = 'web-0.70-to-1.00'\n",
        "dataset = load_dataset(ds_name, config, num_proc=3)\n",
        "# dataset['train'].features\n",
        "dataset"
      ],
      "metadata": {
        "id": "70B12d3sLhd0",
        "outputId": "eb0c0ff7-c610-4c02-e2d1-6f513f41cc19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
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            "b2cebda89a8441fc97e0a76c9c68f91f",
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            "8c8893049baa4d37af60fc7ccca289b1",
            "137fb1fccd214327b96b1843d5fcb9b5",
            "22444fd78cf5409fab2b0961b94d4f43"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/864M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3332be93b72d4652ab56504b17ed8f10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.78G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79db99eff3404fd6aa6278390fee3ad7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70e60064d543491592d7d1729dcdaf5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['url', 'text', 'date', 'meta'],\n",
              "        num_rows: 437882\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WAZVp76gIGi",
        "outputId": "640d17e0-aa70-4ecb-927f-2b84a8d06236"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m68.8/68.8 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fasttext-langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install fasttext-langdetect tiktoken -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sUKyBuj-ssql"
      },
      "outputs": [],
      "source": [
        "import tiktoken\n",
        "enc = tiktoken.get_encoding(\"cl100k_base\")\n",
        "assert enc.decode(enc.encode(\"hello world\")) == \"hello world\"\n",
        "\n",
        "# To get the tokeniser corresponding to a specific model in the OpenAI API:\n",
        "enc = tiktoken.encoding_for_model(\"gpt-4\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ftlangdetect import detect\n",
        "import re\n",
        "import numpy as np\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "tqdm.pandas()\n",
        "#@title define lang filter fns\n",
        "def normalize_text(text):\n",
        "    \"\"\"\n",
        "    # Example usage\n",
        "    original_text = \"This is a\\nsample   text with\\n\\nmultiple   spaces.\"\n",
        "    normalized_text = normalize_text(original_text)\n",
        "    print(normalized_text)\n",
        "\n",
        "    Args:\n",
        "        text (TYPE): Description\n",
        "\n",
        "    Returns:\n",
        "        TYPE: Description\n",
        "    \"\"\"\n",
        "    text = str(text)\n",
        "    # Remove newlines\n",
        "    text = text.replace(\"\\n\", \" \")\n",
        "\n",
        "    # Normalize whitespace\n",
        "    text = re.sub(r\"\\s+\", \" \", text)\n",
        "\n",
        "    return text\n",
        "\n",
        "def get_lang(text):\n",
        "    \"\"\"langdetect wrapper\n",
        "\n",
        "    Args:\n",
        "        example (TYPE): Description\n",
        "\n",
        "    Returns:\n",
        "        TYPE: Description\n",
        "    \"\"\"\n",
        "    text = str(text)\n",
        "\n",
        "    if len(text) < 3:\n",
        "        return \"unknown\", -1\n",
        "\n",
        "    try:\n",
        "        prediction = detect(text=normalize_text(text)[:5000])\n",
        "        lang = prediction[\"lang\"]\n",
        "        score = prediction[\"score\"]\n",
        "        if not isinstance(lang, str) or not isinstance(score, float):\n",
        "            raise ValueError(f\"weird outputs: {lang}\\t{score}\")\n",
        "\n",
        "        return lang, score\n",
        "    except Exception as e:\n",
        "        return \"unknown-error\", -1\n",
        "\n",
        "def is_en(text:str, thresh:float = 0.7):\n",
        "\n",
        "    lang, score = get_lang(text)\n",
        "\n",
        "    return lang == 'en' and score >= thresh\n",
        "\n",
        "_example = 'da hello like a someboooodee'\n",
        "print(is_en(_example))\n",
        "get_lang(_example)"
      ],
      "metadata": {
        "cellView": "form",
        "id": "-HL0IgHqNnGU",
        "outputId": "22ea5e43-2da1-4f2d-c9cb-63e9be0a546d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('en', 0.44179749488830566)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_df = dataset['train'].to_pandas().sample(n=10000)\n",
        "_df[\"lang\"] = _df.text.progress_apply(lambda x: get_lang(x)[0])\n",
        "_df[\"score\"] = _df.text.progress_apply(lambda x: get_lang(x)[-1])\n",
        "en_df = _df[_df.lang == 'en']\n",
        "en_df[['score']].describe()"
      ],
      "metadata": {
        "id": "f-s8oYAsWlRF",
        "outputId": "438b68be-f5a7-49cb-be88-6e3d7a98c1af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364,
          "referenced_widgets": [
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
            ],
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          "data": {
            "text/plain": [
              "  0%|          | 0/10000 [00:00<?, ?it/s]"
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              "             score\n",
              "count  9578.000000\n",
              "mean      0.467299\n",
              "std       0.153221\n",
              "min       0.074878\n",
              "25%       0.355515\n",
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              "75%       0.574090\n",
              "max       0.956477"
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            "text/html": [
              "\n",
              "  <div id=\"df-ad512250-f955-4949-8773-fb47051958b1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9578.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.467299</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.153221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.074878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.355515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.464051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.574090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.956477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad512250-f955-4949-8773-fb47051958b1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad512250-f955-4949-8773-fb47051958b1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad512250-f955-4949-8773-fb47051958b1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aa456d98-3121-4874-bdde-3208751c791f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa456d98-3121-4874-bdde-3208751c791f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aa456d98-3121-4874-bdde-3208751c791f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_df.lang.value_counts()"
      ],
      "metadata": {
        "id": "qq8nZxfTWza1",
        "outputId": "41d7ec5f-4ee1-4fb5-b8e7-5b531d5a75b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "en     9578\n",
              "es      104\n",
              "pt       93\n",
              "zh       56\n",
              "ja       35\n",
              "ru       24\n",
              "de       23\n",
              "ko       19\n",
              "pl       12\n",
              "fr       12\n",
              "it       11\n",
              "vi        7\n",
              "ro        7\n",
              "tr        5\n",
              "id        4\n",
              "uk        2\n",
              "hu        1\n",
              "hy        1\n",
              "ceb       1\n",
              "sl        1\n",
              "no        1\n",
              "nl        1\n",
              "th        1\n",
              "sv        1\n",
              "Name: lang, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.filter(\n",
        "    lambda x: is_en(x['text'], thresh=0.7), num_proc=12,\n",
        ") # thresh is 0.15 for python code, 0.7 for others\n",
        "dataset"
      ],
      "metadata": {
        "id": "A70VdGJlQEZO",
        "outputId": "7894a73b-6bd3-40d2-94cd-3931d360e922",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153,
          "referenced_widgets": [
            "5ecca84ed73447e190df1661da9a1809",
            "a70a8988348d40c68de3afad45573f18",
            "83a3acd216e24a80920b6dc7574f4a4b",
            "afa9a80bb4ed411783b5c8fe7eeefb9d",
            "2165b4b921344f268480cb84cc3c85d6",
            "f50a782399dc42868bebff63e68dad9b",
            "86b27ee0180e48298e94964fae9a7b28",
            "47b00663bf844b09a00e6f8e5a98bb19",
            "d01073956a3e462eb34d0d347f6c561d",
            "2b48c812623d4104aa959817575de8de",
            "59d0c5327b9a43c4aff6c538f3000cdd"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter (num_proc=12):   0%|          | 0/437882 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ecca84ed73447e190df1661da9a1809"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['url', 'text', 'date', 'meta'],\n",
              "        num_rows: 382396\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "CuPQOu9uNdGa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outname = \"BEE-spoke-data/AutoMathText-top-hf\"\n",
        "dataset.push_to_hub(outname, private=True, config_name=config,\n",
        "                    commit_message=f\"add filtered version of {config}\")"
      ],
      "metadata": {
        "id": "9SMJLqrQMTF7",
        "outputId": "95958c53-e35c-4c5a-86a2-9ebd5a0d17eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293,
          "referenced_widgets": [
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            "d90909fed2c24bfea368deedb93f24b0",
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            "ad0f1d13c4a543a5a07f0fccce28aea5"
          ]
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c1896aa43d545ae87d747b18d46c2be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/77 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "170c6639f4144d07a62c2a14da39bf1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/77 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "79343e9951264342b7b5e038ec0e083e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/77 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81598cd2681644eba70f561cfa9e0d67"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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