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      "execution_count": 2,
      "metadata": {
        "id": "xf-7qMdqxCSf"
      },
      "outputs": [],
      "source": [
        "import string\n",
        "import unicodedata\n",
        "import pandas as pd\n",
        "\n",
        "# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427\n",
        "def unicode2ascii(s):\n",
        "    return ''.join(\n",
        "        c for c in unicodedata.normalize('NFD', s)\n",
        "        if unicodedata.category(c) != 'Mn'\n",
        "        and c in all_letters\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_letters = string.ascii_letters + \"1234567890\" + \" .,;'\\n\"\n",
        "n_letters = len(all_letters)"
      ],
      "metadata": {
        "id": "29V0luabxIpC"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q https://huggingface.co/datasets/sswt/arxiv_sample_50K/resolve/main/arxiv_sample.tar.gz -O data.tar.gz && tar -xzf data.tar.gz"
      ],
      "metadata": {
        "id": "cm2rO13rxLxw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_json(\"./arxiv_sample.json\")\n",
        "print(data.shape)\n",
        "data.sample(n=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "0iKIJTfBxirM",
        "outputId": "9660530e-0b9e-4181-ae2a-9cc3978a5706"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 5)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   title   published  \\\n",
              "19656  Generalized K-fan Multimodal Deep Model with S...  2015-03-26   \n",
              "22624  Bootstrapping Monodromy Defects in the Wess-Zu...  2021-08-11   \n",
              "19165  Attention-Enhanced Neural Network Models for T...  2021-10-06   \n",
              "\n",
              "                                         authors  \\\n",
              "19656             [Gang Chen, Sargur N. Srihari]   \n",
              "22624         [Aleix Gimenez-Grau, Pedro Liendo]   \n",
              "19165  [Wenhui Peng, Zelong Yuan, Jianchun Wang]   \n",
              "\n",
              "                                                 summary  \\\n",
              "19656  Multimodal learning with deep Boltzmann machin...   \n",
              "22624  We use analytical bootstrap techniques to stud...   \n",
              "19165  Deep neural network models have shown a great ...   \n",
              "\n",
              "                                    link  \n",
              "19656  http://arxiv.org/abs/1503.07906v1  \n",
              "22624  http://arxiv.org/abs/2108.05107v3  \n",
              "19165  http://arxiv.org/abs/2110.02471v3  "
            ],
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              "      <th>title</th>\n",
              "      <th>published</th>\n",
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              "      <th>summary</th>\n",
              "      <th>link</th>\n",
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              "    <tr>\n",
              "      <th>19656</th>\n",
              "      <td>Generalized K-fan Multimodal Deep Model with S...</td>\n",
              "      <td>2015-03-26</td>\n",
              "      <td>[Gang Chen, Sargur N. Srihari]</td>\n",
              "      <td>Multimodal learning with deep Boltzmann machin...</td>\n",
              "      <td>http://arxiv.org/abs/1503.07906v1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22624</th>\n",
              "      <td>Bootstrapping Monodromy Defects in the Wess-Zu...</td>\n",
              "      <td>2021-08-11</td>\n",
              "      <td>[Aleix Gimenez-Grau, Pedro Liendo]</td>\n",
              "      <td>We use analytical bootstrap techniques to stud...</td>\n",
              "      <td>http://arxiv.org/abs/2108.05107v3</td>\n",
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              "      <th>19165</th>\n",
              "      <td>Attention-Enhanced Neural Network Models for T...</td>\n",
              "      <td>2021-10-06</td>\n",
              "      <td>[Wenhui Peng, Zelong Yuan, Jianchun Wang]</td>\n",
              "      <td>Deep neural network models have shown a great ...</td>\n",
              "      <td>http://arxiv.org/abs/2110.02471v3</td>\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f195fda-0863-48bc-ad3a-a9544293b583 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-4f195fda-0863-48bc-ad3a-a9544293b583');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3876554-a3ae-453f-bdf1-cad6758528ba')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "</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",
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              "      --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",
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              "    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-f3876554-a3ae-453f-bdf1-cad6758528ba 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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BOS, EOS = ' ', '\\n'\n",
        "lines = data.apply(lambda row: unicode2ascii((row['title'] + ' ; ' + row['summary'])), axis=1) \\\n",
        "            .apply(lambda line: BOS + line.replace(EOS, ' ')) \\\n",
        "            .tolist()"
      ],
      "metadata": {
        "id": "TqWZI1T1xpT4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('title_summary_ascii.txt', 'w') as fp:\n",
        "    fp.write('\\n'.join(lines) + '\\n')"
      ],
      "metadata": {
        "id": "RWWmOqh6yLo1"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!tar -zcf title_summary_ascii.txt.tar.gz title_summary_ascii.txt"
      ],
      "metadata": {
        "id": "ZgoI9yk4y2gl"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from huggingface_hub import HfApi\n",
        "from huggingface_hub import login\n",
        "login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "55397878572645c0a17e4415f3d793ab",
            "b32ae48581754b219ca44dae7876386d",
            "5dd0785e18624c9a979a1410804dfbcf",
            "d559bbd504e9487a89c69a40a6ce7e78",
            "eb507607815846258542ea9462fb69df",
            "e189b379d3ac471eb890d8fd89565ae4",
            "547daa83720541b5901b78f688ff94a8",
            "7d5c6dd4a903486f8d373064bb9eab39",
            "9791592dfefb40b5a34e99768fedfe84",
            "6d9195dbea964525ac0c7113d667c635",
            "a7ff281e09bf40dd83c0458538c16b05",
            "d88f3c9f812b4f6f8914ab0ddca48490",
            "3076677021354c9f9bf5f6c35205af47",
            "c4277491060c4be1813c53678a08c7bb",
            "4d8434e73d7f487c9d3c48c8285259a5",
            "a24c4e287afd48fd903b69ab62162a02",
            "ae9830f0021f46a4a78a049ddebec3c8",
            "7538e8b96eae432d84328201600b3077",
            "f1f1856971314b40bc4d7dc8d391aa6a",
            "cca1e50a99c0413790c59d4b997f8ea3",
            "6e9634956dd64ad8abe50d11f84ed61f",
            "adab4d504c184fc3aa8fcc99a8064afd",
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            "f3cd7afd64914a55883aacd451fe1d47",
            "f766a4ccae0147808d8d225e239680f0",
            "21222216f5144071b3e593ab983b73d0",
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            "806b3a09ec444403bea8aca028460cef",
            "ece3cdbed398463b806087f25c42284a",
            "b57c0e27fde640178d566646f2cc381a",
            "23f06ba47fc746269df4ec9919be4aee"
          ]
        },
        "id": "4mgmJ-7yxv1K",
        "outputId": "606b3ebb-1c38-47d7-c76b-4b6353db1b3f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "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": "55397878572645c0a17e4415f3d793ab"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "api = HfApi()\n",
        "api.upload_file(\n",
        "    path_or_fileobj=\"title_summary_ascii.txt.tar.gz\",\n",
        "    path_in_repo=\"title_summary_ascii.txt.tar.gz\",\n",
        "    repo_id=\"sswt/arxiv_sample_50K\",\n",
        "    repo_type=\"dataset\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "0f2ed90b53c848848dabacb796dca3ef",
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            "449326715f6e41b28d38f2eb388263f4",
            "ee51014a0873457b9f2eb5baf19064c0",
            "6cb9e0cccda64943bf70c4eb8e5a554b",
            "5d702dbcccf0435ea925c2f8ec378402",
            "dba9cb93fe6b4f6585f10ac91f3c41c6",
            "81430f16cc904f96b272a1bacd5beab3",
            "87945e9bc7e84bc6a40e96573b915201",
            "83407c9154d24036913ae84a5864e29d"
          ]
        },
        "id": "BG1Rd0weyI7r",
        "outputId": "2c89dc9c-f12c-4727-bd7c-c8797543a89f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "title_summary_ascii.txt.tar.gz:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f2ed90b53c848848dabacb796dca3ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/datasets/sswt/arxiv_sample_50K/blob/main/title_summary_ascii.txt.tar.gz'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    }
  ]
}