{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# simple_wikipedia_LM\n",
        "\n",
        "\n",
        "phase 2 processing\n"
      ],
      "metadata": {
        "id": "BOFzKwFefa6L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets -q"
      ],
      "metadata": {
        "id": "D_iTn-fMWlzu",
        "outputId": "c582164f-39ba-4b1d-b3f9-a74f085ad36f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.9/7.9 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m493.7/493.7 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m311.2/311.2 kB\u001b[0m \u001b[31m25.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m31.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m33.8 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[31m5.5 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[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.0/295.0 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U -q transformers datasets accelerate\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "AqxVoWHpdoy5",
        "outputId": "5e802866-288f-40bf-8ace-48bfe780e1d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162,
          "referenced_widgets": [
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          ]
        }
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/261.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/261.4 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m4.4 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…"
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
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        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/226242 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a041b990243f4865b7c2ca758d03f897"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 226242\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5954\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5954\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"pszemraj/simple_wikipedia_LM\")\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "xx = random.choice(dataset['train']['text'])\n",
        "print(xx)"
      ],
      "metadata": {
        "id": "ikcyi496WqKu",
        "outputId": "0a215795-b4c2-470c-e27d-1d0ed3ae35e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mary Alden (18 June 1883 - 2 July 1946) was an American actress. She acted in Hollywood and on Broadway. She was in a lot of popular movies. Those included Birth of a Nation in 1915 and Ladies of the Mob in 1928.\n",
            "Alden was born in New York. She died in Los Angeles at age sixty-three.\n",
            "American movie actors\n",
            "American stage actors\n",
            "Actors from New York (state)\n",
            "1883 births\n",
            "1946 deaths\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('popular')"
      ],
      "metadata": {
        "id": "gAVTtYSKW5hc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence_splitter -q"
      ],
      "metadata": {
        "id": "6FLti1stbjf1",
        "outputId": "c0440577-7e7f-4a7b-ee42-6cf011ba049b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/45.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.0/45.0 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_trailing_list_items(text):\n",
        "    # Split the text into lines\n",
        "    lines = text.strip().split('\\n')\n",
        "\n",
        "    # Iterate from the end of the text to find the last valid sentence line\n",
        "    last_valid_line = len(lines) - 1\n",
        "    for i in reversed(range(len(lines))):\n",
        "        if is_valid_sentence(lines[i]):\n",
        "            last_valid_line = i\n",
        "            break\n",
        "\n",
        "    # Join the lines up to the last valid sentence with newlines\n",
        "    cleaned_text = '\\n'.join(lines[:last_valid_line + 1])\n",
        "\n",
        "    return cleaned_text\n",
        "\n",
        "# The function is_valid_sentence remains the same\n",
        "def is_valid_sentence(sentence):\n",
        "    return re.match(r'^[A-Z].*[\\.!?]$', sentence.strip())\n",
        "\n",
        "# Example usage with your provided text\n",
        "text = \"\"\"\n",
        "Desert sand is a colour which is like the color of desert sand.\n",
        "It is a deep tone of beige.\n",
        "In 1998, desert sand was made into a Crayola crayon colour.\n",
        "The color shown at right matches the palest of the three colors in the 3-color Desert Camouflage Uniform of United States Army.In 1990, it began to replace the 6-color Desert Battle Dress Uniform.\n",
        "Meaning of desert sand\n",
        "In interior design, the tones of desert sand are called desert colors. This is because they suggest the colors of the landscape and the design of the Native American cultures of the Southwestern United States. The desert colors are used in many places (with both tones of sky blue or turquoise and tones of maroon to complement them) in Southwest Design.\n",
        "The colors desert sand, earth yellow, sand, and field drab are all on the list of the twelve standard camouflage colors used by the United States Army.\n",
        "Tones of desert sand color comparison chart\n",
        "Related pages\n",
        "List of colors\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "result =remove_trailing_list_items(text)\n",
        "text"
      ],
      "metadata": {
        "id": "rcsDYsPnaxO9",
        "outputId": "74cdc087-7ba1-4f0b-9187-b590acaf2b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nDesert sand is a colour which is like the color of desert sand.\\nIt is a deep tone of beige.\\nIn 1998, desert sand was made into a Crayola crayon colour.\\nThe color shown at right matches the palest of the three colors in the 3-color Desert Camouflage Uniform of United States Army.In 1990, it began to replace the 6-color Desert Battle Dress Uniform.\\nMeaning of desert sand\\nIn interior design, the tones of desert sand are called desert colors. This is because they suggest the colors of the landscape and the design of the Native American cultures of the Southwestern United States. The desert colors are used in many places (with both tones of sky blue or turquoise and tones of maroon to complement them) in Southwest Design.\\nThe colors desert sand, earth yellow, sand, and field drab are all on the list of the twelve standard camouflage colors used by the United States Army.\\nTones of desert sand color comparison chart\\nRelated pages\\nList of colors\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "id": "m9Vb2t_JbHk9",
        "outputId": "a723858f-4b3d-4201-876b-42c68822c2ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Desert sand is a colour which is like the color of desert sand.\\nIt is a deep tone of beige.\\nIn 1998, desert sand was made into a Crayola crayon colour.\\nThe color shown at right matches the palest of the three colors in the 3-color Desert Camouflage Uniform of United States Army.In 1990, it began to replace the 6-color Desert Battle Dress Uniform.\\nMeaning of desert sand\\nIn interior design, the tones of desert sand are called desert colors. This is because they suggest the colors of the landscape and the design of the Native American cultures of the Southwestern United States. The desert colors are used in many places (with both tones of sky blue or turquoise and tones of maroon to complement them) in Southwest Design.\\nThe colors desert sand, earth yellow, sand, and field drab are all on the list of the twelve standard camouflage colors used by the United States Army.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result)"
      ],
      "metadata": {
        "id": "1rVkEuqJbIVo",
        "outputId": "f571096b-bc13-4344-b064-098125119357",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Desert sand is a colour which is like the color of desert sand.\n",
            "It is a deep tone of beige.\n",
            "In 1998, desert sand was made into a Crayola crayon colour.\n",
            "The color shown at right matches the palest of the three colors in the 3-color Desert Camouflage Uniform of United States Army.In 1990, it began to replace the 6-color Desert Battle Dress Uniform.\n",
            "Meaning of desert sand\n",
            "In interior design, the tones of desert sand are called desert colors. This is because they suggest the colors of the landscape and the design of the Native American cultures of the Southwestern United States. The desert colors are used in many places (with both tones of sky blue or turquoise and tones of maroon to complement them) in Southwest Design.\n",
            "The colors desert sand, earth yellow, sand, and field drab are all on the list of the twelve standard camouflage colors used by the United States Army.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(remove_trailing_list_items(xx))"
      ],
      "metadata": {
        "id": "yw6uOqbXbyKN",
        "outputId": "95927d08-ba80-42fe-bcf7-677338c73d49",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mary Alden (18 June 1883 - 2 July 1946) was an American actress. She acted in Hollywood and on Broadway. She was in a lot of popular movies. Those included Birth of a Nation in 1915 and Ladies of the Mob in 1928.\n",
            "Alden was born in New York. She died in Los Angeles at age sixty-three.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G4s1ZxqJdM4S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "ia0LVt64ddY_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.push_to_hub(\"pszemraj/simple_wikipedia_LM\", config_name=\"original\")"
      ],
      "metadata": {
        "id": "ZWD-wkMiddrp",
        "outputId": "57d5bfba-3696-4eda-f16e-e1513e2fd6c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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            "bf9aac9cb45543309a29a98ef92a390e",
            "26c7b43c81de43febf697ec8a3e41364",
            "beb95a53106a4388a8b791cf429f0d93",
            "2b4b55b2644146f59ff86293dedf1c1b",
            "91660cfebefa4008a50e5bb4624021b0",
            "2dbbf4fdb87447ebbcda0989cea2e754",
            "88adb374d8f94ab788bcf5029e2cc94c",
            "dcb80fcfde7c4f6c929d39bdecb0da2d",
            "b44dbe9ef2ad402a9f31aaa08fc9f28b",
            "817f6a5ae58242e0868cee26bcae3105",
            "05dd89a35c62409db3c3424941ac5f0f",
            "f4329c1303064ff1b12d9c755f24b24d",
            "94c115099af04513be376b3cbed69413",
            "acda59eedc9444b1b0e7780659acbb52",
            "ddd789d8e03c4af38c45fa714a4fc678",
            "907d0f12b1e3423890c286d32308365e",
            "41a8ca0d71ef4977935fae6275466222",
            "49f9dd0d33524fb6a9bfd24726314980",
            "334ab8ec76b24423970d490c1932fedb",
            "925d6021e63641ba9f194de3f1c29392",
            "ef56bad336284e05a10b53c0faae8ca3",
            "657f195dcb4e4865aa7e413115b484ad",
            "163fb46cc75f4292b7857d3ed40b4a76",
            "aa48130c70084a0ba91f33e5a5d9b519",
            "61be48cec77c40cc80820356c3865c5b",
            "ed846a2e8aff457eb534332f89e62dda",
            "5869ceefe9bc46d9804f371f70e429c1",
            "52a8685530e54b5ab9a6e6d99af81b2c",
            "cf7e4de85261498299c12c3d4252dcd1",
            "c0dffd674195437b9fa1b317b662a739",
            "7c1668d4e8004a1b928b80ffef2a7045",
            "cfff644d1eb5487681ab4b7fd5fd7b73",
            "4a8773d81f3f466d9b0aa341c48ff23e",
            "11b03fbafd9a45fd8668be103a6d8d19",
            "1327c9cd746441dd82da9056dbde58f9",
            "74709b764aaf46f1aa7a40f6e37527e8",
            "4e3616b524e94b1a8f83bc909cbd0bb1",
            "3b339f3c21b541688cfe03f6f3e1a65c",
            "7c1e6ea8801e45f8bb987a700be4559d",
            "4d935ba6cb95450799cb95964be2641b",
            "4a971628fc1b45a093a52dffd4725c10",
            "4cd6383a98064670a3b01aaa06dab05a",
            "8c9d771569d9402486e057826ff90229",
            "2e865f09f47a446ab7c599af534d12de",
            "1810f21b9a6346e8a59f6753534d7a4e",
            "4a70d18361e94c43981522a69b2cf492",
            "87291c33749a442382d89521a4a10be8",
            "dbe8afa3a5484e2a825913c19aa53aa6",
            "9d0435356cec43528eeb58be0ebaff69",
            "ed3d45855afb4f99a3f2a8876ef87c84",
            "0c8ab711bae3472581a213fd45c2a4a5",
            "a140129fb478489f84d70676e621da5b",
            "0f6ade43c968416da1a186a7f8eaa8f0",
            "506fbfd8714d4225945e082b29153925",
            "a2019c1b022f4f0e9e14070a057d9f65",
            "aaa62535899e4757b934527503638409",
            "8b48c0254e13468885e9535abaa4c934",
            "83b060fb82584c4ab55ca16aae764c97",
            "77e764cdfdea476893ff5cdb4b38631a",
            "f5af195616f443779bdd435ab6ee18ee",
            "e18843ddf494479babe62dc3d4a387e7",
            "cf8cfc51b8a84bbeba3988f19b8d34fe",
            "18a92e23358e486bb638b5fdeb226c0d",
            "615edddb039a4f1190b76d0832d3185d",
            "d156ee5384504d99bd5e535d20fa58f5",
            "e59d6445028840428298e25c9f01a198",
            "dc9bb9bc57b1438a98e2a621ecd837ca",
            "58f20661429349948b4826b56dfeb019",
            "4f5c3c78b6b34faaa33bd1c374bbdb11",
            "f5411d26e6dc42328a9cb25cd882e9b0",
            "ab13214c9c2d4e5f9b0003a318bba39c",
            "023e1969553b4a57a952e307f124b7e5",
            "28af4b7f05c0450c925f60b9d9fea00d",
            "35cb095b655d4d45b927a8a3cc25645e",
            "0d497bf4e87c4f5c99ea6723281aa94d",
            "bd05a9fc4fbe4f2d9a4567ca099f51e4"
          ]
        }
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a60a4d9b74214e029476e4e6cabccc6c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/227 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05dd89a35c62409db3c3424941ac5f0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "657f195dcb4e4865aa7e413115b484ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/6 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a8773d81f3f466d9b0aa341c48ff23e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e865f09f47a446ab7c599af534d12de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/6 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2019c1b022f4f0e9e14070a057d9f65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading metadata:   0%|          | 0.00/1.63k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e59d6445028840428298e25c9f01a198"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_v2(example):\n",
        "    return {\"text\": remove_trailing_list_items(example[\"text\"])}\n",
        "sw_v2 = dataset.map(process_v2, )\n",
        "sw_v2"
      ],
      "metadata": {
        "id": "6RLEAKbpd5d7",
        "outputId": "752ce025-2a9e-4f06-fee1-857e33933159",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
            "932d1d3eb2fe48bca293cd90e61a9d88",
            "07937e45725845e092edbd5d46ce042c",
            "2c17bf13967d4055844f220daa62d8aa",
            "ce1ced11e4f9432ab624d51ef182ffbc",
            "9b89aed4812945ffa5b39970d076a8cc",
            "6564a2e9d0e34a59ad482a9bd4d633bc",
            "32e7e27fd4814a32b2f3bd00d2a49b8b",
            "96ca30e566b24e038442ab0bd5d3de41",
            "344c20b1da254807af12cd4bfabe4260",
            "860b5d7684a94237bcc1edd01b755837",
            "959882babca84f95b47caf453f8e3cf8",
            "13cea88c22bc49128950796858de9a57",
            "7d1e542dd26945a98fcb386de46012eb",
            "e65759061dac4a05a636f68d8f9713ca",
            "b31f1bc647ee4721806a3a52e605823d",
            "dbe9c6f4be784484bf6489a51474a629",
            "24362c0f7d404248bd7ce4221258b502",
            "ab69a1bc1bb94e35a5308ae98254304c",
            "1522729bab3a48e1b388b54f6d6ddf97",
            "e3eb59d766f14e77ac4ea0a9c8ac8b9b",
            "8244dead221e42faadde2ca01a78e154",
            "5f8cb59b5fdb499089e2898627e71704",
            "13fb6852acf441bf92ef652eb09796cd",
            "f2b060a3f6ce42bc9c192e969829acdd",
            "547f3dc212e1451f8570212b3f43670e",
            "e8c85ca41ebd435b840536c6089f03a3",
            "10be2d21cd7d47a4aa92b9a4dca702ed",
            "cf327e2fbbbd494da8677cb2ccd35760",
            "17a903fee9e742a1b486ce2210cd330d",
            "e4e4f42de5714fb8b03bb76f6ea16554",
            "d4a9da11979942a197e1c55a0cd37719",
            "7de52c222c6d44908f7f6b762ea7c5c5",
            "e7d6088fbc04497f8dfeb4880e229029"
          ]
        }
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/226242 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "932d1d3eb2fe48bca293cd90e61a9d88"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13cea88c22bc49128950796858de9a57"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13fb6852acf441bf92ef652eb09796cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 226242\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5954\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5954\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uRzl4naQeBMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "def count_words(text):\n",
        "    words = re.findall(r\"\\b\\w+\\b\", text)\n",
        "    return len(words)\n",
        "\n",
        "\n",
        "def get_wordcounts(example):\n",
        "    return {\"word_count\": count_words(example[\"text\"])}"
      ],
      "metadata": {
        "id": "VfhM3sieY8Zn"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sw_v2 = sw_v2.map(get_wordcounts, num_proc=4)\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "_df = pd.DataFrame(sw_v2[\"train\"][\"word_count\"], columns=[\"word_count\"])\n",
        "print(_df.describe())"
      ],
      "metadata": {
        "id": "o-NhqCqyZplW",
        "outputId": "e3a26c46-3415-4199-add4-79f5ce7f6295",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269,
          "referenced_widgets": [
            "96a0d86f8906439aa816a69e34cf9163",
            "d33672f012964a9fbfb0e4f7dfaeb22e",
            "ff5612ed22e1499aba88d84ba4be0376",
            "5daae238e95743849220e9eb8b90b40d",
            "8641579408d54182836a0629ac855677",
            "7aaeb8798e15416c9f455f9f19c18faa",
            "8ea3f3bd205547a9bc1f95d069f72bde",
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            "04cd9744dbba4cbe99244809cd7dba16",
            "74f8f6b85baa482ba2224fc4c3ba939e",
            "169e5cd5593341cca6f1aa7b83fc7ec7",
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            "637e13c79d644223b69620af53cf62fa",
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            "7c709ff65b7549298f63e0674976d0e6",
            "41a8d5ad3278434296600fc61e5de0df",
            "46129bf636e54a8f9393314ee41bcf8f",
            "e96a2608dc134073a2d3adc8b0b056f8",
            "604237fd0c2046a58ab85964134a0936",
            "03e04e8ff0c94a88b66fc1b6b3549a21",
            "9d6086b07abf42d6b83bee4b3b82e37c",
            "18e1da5a0a2a4aa2be813237a73c7ba1",
            "3a1b84f64e164c67b40564c99dd38930",
            "27094a503a1b472d9eaae579935faf5d",
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            "2866f745f7cb4939a162b27514363bb6",
            "322e61eb951b4ed9887fcfb5024e6f81",
            "0350350177e24cb696a9e7f820f5ade3",
            "eb011cee11e041c39ee49e9ea03aade5",
            "03da7ce65e6f4cf9960b8918c84b020f"
          ]
        }
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/226242 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "96a0d86f8906439aa816a69e34cf9163"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "169e5cd5593341cca6f1aa7b83fc7ec7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03e04e8ff0c94a88b66fc1b6b3549a21"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          word_count\n",
            "count  226242.000000\n",
            "mean      151.480189\n",
            "std       555.623285\n",
            "min         0.000000\n",
            "25%        28.000000\n",
            "50%        61.000000\n",
            "75%       126.000000\n",
            "max     34191.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sw_v2 = sw_v2.filter(lambda x: x['word_count'] > 6)\n",
        "sw_v2"
      ],
      "metadata": {
        "id": "HnIYThzdeYz0",
        "outputId": "18269e2a-1541-467a-a75e-f7b84056635a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356,
          "referenced_widgets": [
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            "fca4fee3cbb24a2a9f55f803ab94904e",
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            "9102e07335e54e5c8948b83b1f28440f",
            "9f40b8fea27949ac8000c3016ce43cb8"
          ]
        }
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/226175 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "110478b4e2b14123b79fc3b4359b8d09"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b674e0d51ed248a1b6c49779c6cc5d0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/5954 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0de83dfbc7554cf0816c8a54092a0194"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text', 'word_count'],\n",
              "        num_rows: 225984\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text', 'word_count'],\n",
              "        num_rows: 5949\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text', 'word_count'],\n",
              "        num_rows: 5943\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sw_v2 = sw_v2.remove_columns(['word_count'])\n",
        "sw_v2"
      ],
      "metadata": {
        "id": "Tzfakw-keuSu",
        "outputId": "f60a93ce-bf46-4b10-e039-d8505892cacd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 225984\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5949\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'url', 'title', 'text'],\n",
              "        num_rows: 5943\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sw_v2.push_to_hub(\"pszemraj/simple_wikipedia_LM\",)"
      ],
      "metadata": {
        "id": "MdsnZ1_0fD-x",
        "outputId": "5b6c9926-7744-4b9c-950d-e59d026e9841",
        "colab": {
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          "height": 337,
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            "f24697820c7a4e848ae7a6566e4f580d",
            "9e2f66621c2b48909b0cbbcb14b43296"
          ]
        }
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e6ea7f1879844258a08704bf467beca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a8a94b94949a46618bc60b2225e3c2e5"
            }
          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f408fa710576471582f46dbf24c2b2a1"
            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3fa0e1c552a14bdaac1c9dfa450c239a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3899ae6f994d4e8fa37b84a82a90a70d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "model_id": "31779bfdf4f041d785d502d56fb135ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "model_id": "eb62d4ad48a6498c845e9d6df9a361d8"
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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