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        "!pip install -q tensorflow-datasets tfds-nightly"
      ],
      "execution_count": null,
      "outputs": [
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          "text": [
            "\u001b[K     |████████████████████████████████| 9.8 MB 5.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 247 kB 71.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 151 kB 74.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 829 kB 31.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 62 kB 958 kB/s \n",
            "\u001b[K     |████████████████████████████████| 2.3 MB 65.8 MB/s \n",
            "\u001b[?25h  Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for future (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",
            "multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.26.0 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 4.1 MB 5.1 MB/s \n",
            "\u001b[?25h"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_x9-rjF9ygED"
      },
      "source": [
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGBrOAl1pTbv"
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "!pip install -q kaggle"
      ],
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      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WcnyCcQpWsY"
      },
      "source": [
        "kaggle_username =''\n",
        "kaggle_key = ''\n",
        "!echo '{\"username\":kaggle_username,\"key\":kaggle_key}' > ~/.kaggle/kaggle.json"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "WYB8mOUopgOw",
        "outputId": "f34867a2-0634-41ab-9f2c-60a843e39c2a"
      },
      "source": [
        "!kaggle datasets download -d a0155991rliwei/c4-200m -p /content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading c4-200m.zip to /content\n",
            "100% 19.3G/19.3G [03:35<00:00, 79.4MB/s]\n",
            "100% 19.3G/19.3G [03:35<00:00, 96.1MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ujxx6RGqRtO"
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      "source": [
        "### Load the dataset and shard it"
      ]
    },
    {
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      "source": [
        "!pip install sentencepiece"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 4.9 MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q transformers"
      ],
      "metadata": {
        "id": "q0N3KiNprJsj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import (\n",
        "   AutoConfig,\n",
        "   AutoTokenizer,\n",
        "   AutoModelForSequenceClassification,\n",
        ")\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "ocx2nCbvqPcZ"
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      "execution_count": null,
      "outputs": []
    },
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        "model_name = 't5-base'\n",
        "# Initialise tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
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              "Downloading:   0%|          | 0.00/1.17k [00:00<?, ?B/s]"
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        "!unzip -q /content/c4-200m.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yK9Vz-JKsIlz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "256d1432-c16b-49d2-c8d4-9c91db4cf902"
      },
      "source": [
        "c4_builder = tfds.core.builder_from_directory('/content/c4200m/1.0.0')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:Load dataset info from /content/c4200m/1.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fbs-cabXvaqe",
        "outputId": "c4b5cf72-283c-4c1f-967b-923340e0e558"
      },
      "source": [
        "# Metadata are avalailable as usual\n",
        "num_train_examples = c4_builder.info.splits['train'].num_examples\n",
        "print(num_train_examples)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "183894319\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c4_builder.download_and_prepare()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qYiuD7MbuB4W",
        "outputId": "e581020b-491e-4602-e02d-1420f99e3cf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:Reusing dataset c4200m (/content/c4200m/1.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = c4_builder.as_dataset(split='train', shuffle_files=True)\n",
        "train_df = tfds.as_dataframe(train_data.take(550000))\n",
        "train_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wW73sHNvuvH6",
        "outputId": "12466e84-8604-4893-97c9-8fe77c2aec76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:Constructing tf.data.Dataset c4200m for split train, from /content/c4200m/1.0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(550000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "l0ioHof4vsny",
        "outputId": "10223ec1-13f0-476c-d53b-62f34f5e74ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "      <th></th>\n",
              "      <th>input</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b\"Can be empenty'' for more damage, but not ne...</td>\n",
              "      <td>b'Can be empathy for more damage, but not need...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b'Miguelx completed Pollster Badge.'</td>\n",
              "      <td>b'Miguelx completed the Pollster Badge.'</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b'This classic three day itinerary is take you...</td>\n",
              "      <td>b'This classic three-day itinerary takes you t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b'Kimbrelle shares an inspirational story wher...</td>\n",
              "      <td>b'Kimbrelle shares an inspirational story wher...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b'Variation: The utility players get a guideli...</td>\n",
              "      <td>b'Variation: The utility players get a guideli...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               input                                             output\n",
              "0  b\"Can be empenty'' for more damage, but not ne...  b'Can be empathy for more damage, but not need...\n",
              "1               b'Miguelx completed Pollster Badge.'           b'Miguelx completed the Pollster Badge.'\n",
              "2  b'This classic three day itinerary is take you...  b'This classic three-day itinerary takes you t...\n",
              "3  b'Kimbrelle shares an inspirational story wher...  b'Kimbrelle shares an inspirational story wher...\n",
              "4  b'Variation: The utility players get a guideli...  b'Variation: The utility players get a guideli..."
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['input'] = train_df['input'].str.decode(encoding = 'UTF-8')\n",
        "train_df['output'] = train_df['output'].str.decode(encoding = 'UTF-8')\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "JX0blLmbzg-G",
        "outputId": "5bca1ad7-709d-461a-d463-80a0f9320bf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>input</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The steps below describe how to remove data fo...</td>\n",
              "      <td>The steps below describe how to remove data fo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>When I wake up it\\'s usually comes out dreamsI...</td>\n",
              "      <td>When I wake up it\\'s usually dreams I\\'m think...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>One of the cardinal factors to be considered t...</td>\n",
              "      <td>One of the cardinal factors to consider when t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Answers » Regions » Is in Nagorno-Karabakt reg...</td>\n",
              "      <td>Answers » Regions » Is Nagorno-Karabakh region...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Flaneuring in fun at maple creek SK!</td>\n",
              "      <td>Flaneuring Fun in Maple Creek SK!</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               input                                             output\n",
              "0  The steps below describe how to remove data fo...  The steps below describe how to remove data fo...\n",
              "1  When I wake up it\\'s usually comes out dreamsI...  When I wake up it\\'s usually dreams I\\'m think...\n",
              "2  One of the cardinal factors to be considered t...  One of the cardinal factors to consider when t...\n",
              "3  Answers » Regions » Is in Nagorno-Karabakt reg...  Answers » Regions » Is Nagorno-Karabakh region...\n",
              "4               Flaneuring in fun at maple creek SK!                  Flaneuring Fun in Maple Creek SK!"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.to_csv('/content/drive/MyDrive/c4_200m/c4_200m_550k.csv', index=False)"
      ],
      "metadata": {
        "id": "ZL3vXy6HyhzC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Also Create a 1MM dataset"
      ],
      "metadata": {
        "id": "O0RDvT9yz8G1"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGIOWlz7u355",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8630dbd4-5d11-4574-b722-eb73cef1dd91"
      },
      "source": [
        "train_data = c4_builder.as_dataset(split='train', shuffle_files=True)\n",
        "train_df = tfds.as_dataframe(train_data.take(550000))\n",
        "train_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:Constructing tf.data.Dataset c4200m for split train, from /content/c4200m/1.0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['input'] = train_df['input'].str.decode(encoding = 'UTF-8')\n",
        "train_df['output'] = train_df['output'].str.decode(encoding = 'UTF-8')\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DvEAQOz60DBz",
        "outputId": "6ed9c218-1175-4b62-9f6e-0195d11aafc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>input</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Medell he, Ohio W. Shannon Kansas,R. C.</td>\n",
              "      <td>Medell, Ohio; W. Shannon. Kansas; R. C.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>quarter of 1999 $ 25 million was repaid under ...</td>\n",
              "      <td>quarter of 1999, $25 million was repaid under ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>It used as service center by the Block office ...</td>\n",
              "      <td>It can be used as a service center by the Bloc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tom offered two this time, one of old restaura...</td>\n",
              "      <td>Tom offered two this time, one of old restaura...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>You can see 'Spring beauties' at The Botanical...</td>\n",
              "      <td>You can see Spring Beauties at The Botanical G...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               input                                             output\n",
              "0            Medell he, Ohio W. Shannon Kansas,R. C.            Medell, Ohio; W. Shannon. Kansas; R. C.\n",
              "1  quarter of 1999 $ 25 million was repaid under ...  quarter of 1999, $25 million was repaid under ...\n",
              "2  It used as service center by the Block office ...  It can be used as a service center by the Bloc...\n",
              "3  Tom offered two this time, one of old restaura...  Tom offered two this time, one of old restaura...\n",
              "4  You can see 'Spring beauties' at The Botanical...  You can see Spring Beauties at The Botanical G..."
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.to_csv('/content/drive/MyDrive/c4_200m/c4_200m_1M.csv', index=False)"
      ],
      "metadata": {
        "id": "4IVLTBFI0GoH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5cglDzbf0JV3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}