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Add notebook for preparation of raw lines

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+ {
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+ "source": [
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+ "Get raw lines (title + summary) in ascii from json and upload to HF Hub"
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+ ],
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+ "metadata": {
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+ "id": "7qDGy7VUy8_L"
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+ ],
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+ "metadata": {
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+ },
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+ "metadata": {
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+ "id": "xf-7qMdqxCSf"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import string\n",
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+ "import unicodedata\n",
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+ "import pandas as pd\n",
1354
+ "\n",
1355
+ "# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427\n",
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+ "def unicode2ascii(s):\n",
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+ " return ''.join(\n",
1358
+ " c for c in unicodedata.normalize('NFD', s)\n",
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+ " if unicodedata.category(c) != 'Mn'\n",
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+ " and c in all_letters\n",
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+ " )"
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+ ]
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+ },
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+ {
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+ "source": [
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+ "all_letters = string.ascii_letters + \"1234567890\" + \" .,;'\\n\"\n",
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+ "n_letters = len(all_letters)"
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+ ],
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+ "metadata": {
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+ "id": "29V0luabxIpC"
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+ },
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+ "execution_count": 3,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "!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"
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+ ],
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+ "metadata": {
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+ "id": "cm2rO13rxLxw"
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+ "execution_count": 4,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "data = pd.read_json(\"./arxiv_sample.json\")\n",
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+ "print(data.shape)\n",
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+ "data.sample(n=3)"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 161
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+ },
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+ "id": "0iKIJTfBxirM",
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+ "outputId": "9660530e-0b9e-4181-ae2a-9cc3978a5706"
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+ "execution_count": 5,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
1408
+ "(50000, 5)\n"
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+ ]
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+ },
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ " title published \\\n",
1416
+ "19656 Generalized K-fan Multimodal Deep Model with S... 2015-03-26 \n",
1417
+ "22624 Bootstrapping Monodromy Defects in the Wess-Zu... 2021-08-11 \n",
1418
+ "19165 Attention-Enhanced Neural Network Models for T... 2021-10-06 \n",
1419
+ "\n",
1420
+ " authors \\\n",
1421
+ "19656 [Gang Chen, Sargur N. Srihari] \n",
1422
+ "22624 [Aleix Gimenez-Grau, Pedro Liendo] \n",
1423
+ "19165 [Wenhui Peng, Zelong Yuan, Jianchun Wang] \n",
1424
+ "\n",
1425
+ " summary \\\n",
1426
+ "19656 Multimodal learning with deep Boltzmann machin... \n",
1427
+ "22624 We use analytical bootstrap techniques to stud... \n",
1428
+ "19165 Deep neural network models have shown a great ... \n",
1429
+ "\n",
1430
+ " link \n",
1431
+ "19656 http://arxiv.org/abs/1503.07906v1 \n",
1432
+ "22624 http://arxiv.org/abs/2108.05107v3 \n",
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+ "19165 http://arxiv.org/abs/2110.02471v3 "
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+ ],
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+ "text/html": [
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+ " <div id=\"df-4f195fda-0863-48bc-ad3a-a9544293b583\" class=\"colab-df-container\">\n",
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+ " <div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " <thead>\n",
1454
+ " <tr style=\"text-align: right;\">\n",
1455
+ " <th></th>\n",
1456
+ " <th>title</th>\n",
1457
+ " <th>published</th>\n",
1458
+ " <th>authors</th>\n",
1459
+ " <th>summary</th>\n",
1460
+ " <th>link</th>\n",
1461
+ " </tr>\n",
1462
+ " </thead>\n",
1463
+ " <tbody>\n",
1464
+ " <tr>\n",
1465
+ " <th>19656</th>\n",
1466
+ " <td>Generalized K-fan Multimodal Deep Model with S...</td>\n",
1467
+ " <td>2015-03-26</td>\n",
1468
+ " <td>[Gang Chen, Sargur N. Srihari]</td>\n",
1469
+ " <td>Multimodal learning with deep Boltzmann machin...</td>\n",
1470
+ " <td>http://arxiv.org/abs/1503.07906v1</td>\n",
1471
+ " </tr>\n",
1472
+ " <tr>\n",
1473
+ " <th>22624</th>\n",
1474
+ " <td>Bootstrapping Monodromy Defects in the Wess-Zu...</td>\n",
1475
+ " <td>2021-08-11</td>\n",
1476
+ " <td>[Aleix Gimenez-Grau, Pedro Liendo]</td>\n",
1477
+ " <td>We use analytical bootstrap techniques to stud...</td>\n",
1478
+ " <td>http://arxiv.org/abs/2108.05107v3</td>\n",
1479
+ " </tr>\n",
1480
+ " <tr>\n",
1481
+ " <th>19165</th>\n",
1482
+ " <td>Attention-Enhanced Neural Network Models for T...</td>\n",
1483
+ " <td>2021-10-06</td>\n",
1484
+ " <td>[Wenhui Peng, Zelong Yuan, Jianchun Wang]</td>\n",
1485
+ " <td>Deep neural network models have shown a great ...</td>\n",
1486
+ " <td>http://arxiv.org/abs/2110.02471v3</td>\n",
1487
+ " </tr>\n",
1488
+ " </tbody>\n",
1489
+ "</table>\n",
1490
+ "</div>\n",
1491
+ " <div class=\"colab-df-buttons\">\n",
1492
+ "\n",
1493
+ " <div class=\"colab-df-container\">\n",
1494
+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f195fda-0863-48bc-ad3a-a9544293b583')\"\n",
1495
+ " title=\"Convert this dataframe to an interactive table.\"\n",
1496
+ " style=\"display:none;\">\n",
1497
+ "\n",
1498
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
1499
+ " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
1500
+ " </svg>\n",
1501
+ " </button>\n",
1502
+ "\n",
1503
+ " <style>\n",
1504
+ " .colab-df-container {\n",
1505
+ " display:flex;\n",
1506
+ " gap: 12px;\n",
1507
+ " }\n",
1508
+ "\n",
1509
+ " .colab-df-convert {\n",
1510
+ " background-color: #E8F0FE;\n",
1511
+ " border: none;\n",
1512
+ " border-radius: 50%;\n",
1513
+ " cursor: pointer;\n",
1514
+ " display: none;\n",
1515
+ " fill: #1967D2;\n",
1516
+ " height: 32px;\n",
1517
+ " padding: 0 0 0 0;\n",
1518
+ " width: 32px;\n",
1519
+ " }\n",
1520
+ "\n",
1521
+ " .colab-df-convert:hover {\n",
1522
+ " background-color: #E2EBFA;\n",
1523
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1524
+ " fill: #174EA6;\n",
1525
+ " }\n",
1526
+ "\n",
1527
+ " .colab-df-buttons div {\n",
1528
+ " margin-bottom: 4px;\n",
1529
+ " }\n",
1530
+ "\n",
1531
+ " [theme=dark] .colab-df-convert {\n",
1532
+ " background-color: #3B4455;\n",
1533
+ " fill: #D2E3FC;\n",
1534
+ " }\n",
1535
+ "\n",
1536
+ " [theme=dark] .colab-df-convert:hover {\n",
1537
+ " background-color: #434B5C;\n",
1538
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
1539
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
1540
+ " fill: #FFFFFF;\n",
1541
+ " }\n",
1542
+ " </style>\n",
1543
+ "\n",
1544
+ " <script>\n",
1545
+ " const buttonEl =\n",
1546
+ " document.querySelector('#df-4f195fda-0863-48bc-ad3a-a9544293b583 button.colab-df-convert');\n",
1547
+ " buttonEl.style.display =\n",
1548
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1549
+ "\n",
1550
+ " async function convertToInteractive(key) {\n",
1551
+ " const element = document.querySelector('#df-4f195fda-0863-48bc-ad3a-a9544293b583');\n",
1552
+ " const dataTable =\n",
1553
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1554
+ " [key], {});\n",
1555
+ " if (!dataTable) return;\n",
1556
+ "\n",
1557
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1558
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1559
+ " + ' to learn more about interactive tables.';\n",
1560
+ " element.innerHTML = '';\n",
1561
+ " dataTable['output_type'] = 'display_data';\n",
1562
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1563
+ " const docLink = document.createElement('div');\n",
1564
+ " docLink.innerHTML = docLinkHtml;\n",
1565
+ " element.appendChild(docLink);\n",
1566
+ " }\n",
1567
+ " </script>\n",
1568
+ " </div>\n",
1569
+ "\n",
1570
+ "\n",
1571
+ "<div id=\"df-f3876554-a3ae-453f-bdf1-cad6758528ba\">\n",
1572
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3876554-a3ae-453f-bdf1-cad6758528ba')\"\n",
1573
+ " title=\"Suggest charts.\"\n",
1574
+ " style=\"display:none;\">\n",
1575
+ "\n",
1576
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " width=\"24px\">\n",
1578
+ " <g>\n",
1579
+ " <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",
1580
+ " </g>\n",
1581
+ "</svg>\n",
1582
+ " </button>\n",
1583
+ "\n",
1584
+ "<style>\n",
1585
+ " .colab-df-quickchart {\n",
1586
+ " --bg-color: #E8F0FE;\n",
1587
+ " --fill-color: #1967D2;\n",
1588
+ " --hover-bg-color: #E2EBFA;\n",
1589
+ " --hover-fill-color: #174EA6;\n",
1590
+ " --disabled-fill-color: #AAA;\n",
1591
+ " --disabled-bg-color: #DDD;\n",
1592
+ " }\n",
1593
+ "\n",
1594
+ " [theme=dark] .colab-df-quickchart {\n",
1595
+ " --bg-color: #3B4455;\n",
1596
+ " --fill-color: #D2E3FC;\n",
1597
+ " --hover-bg-color: #434B5C;\n",
1598
+ " --hover-fill-color: #FFFFFF;\n",
1599
+ " --disabled-bg-color: #3B4455;\n",
1600
+ " --disabled-fill-color: #666;\n",
1601
+ " }\n",
1602
+ "\n",
1603
+ " .colab-df-quickchart {\n",
1604
+ " background-color: var(--bg-color);\n",
1605
+ " border: none;\n",
1606
+ " border-radius: 50%;\n",
1607
+ " cursor: pointer;\n",
1608
+ " display: none;\n",
1609
+ " fill: var(--fill-color);\n",
1610
+ " height: 32px;\n",
1611
+ " padding: 0;\n",
1612
+ " width: 32px;\n",
1613
+ " }\n",
1614
+ "\n",
1615
+ " .colab-df-quickchart:hover {\n",
1616
+ " background-color: var(--hover-bg-color);\n",
1617
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1618
+ " fill: var(--button-hover-fill-color);\n",
1619
+ " }\n",
1620
+ "\n",
1621
+ " .colab-df-quickchart-complete:disabled,\n",
1622
+ " .colab-df-quickchart-complete:disabled:hover {\n",
1623
+ " background-color: var(--disabled-bg-color);\n",
1624
+ " fill: var(--disabled-fill-color);\n",
1625
+ " box-shadow: none;\n",
1626
+ " }\n",
1627
+ "\n",
1628
+ " .colab-df-spinner {\n",
1629
+ " border: 2px solid var(--fill-color);\n",
1630
+ " border-color: transparent;\n",
1631
+ " border-bottom-color: var(--fill-color);\n",
1632
+ " animation:\n",
1633
+ " spin 1s steps(1) infinite;\n",
1634
+ " }\n",
1635
+ "\n",
1636
+ " @keyframes spin {\n",
1637
+ " 0% {\n",
1638
+ " border-color: transparent;\n",
1639
+ " border-bottom-color: var(--fill-color);\n",
1640
+ " border-left-color: var(--fill-color);\n",
1641
+ " }\n",
1642
+ " 20% {\n",
1643
+ " border-color: transparent;\n",
1644
+ " border-left-color: var(--fill-color);\n",
1645
+ " border-top-color: var(--fill-color);\n",
1646
+ " }\n",
1647
+ " 30% {\n",
1648
+ " border-color: transparent;\n",
1649
+ " border-left-color: var(--fill-color);\n",
1650
+ " border-top-color: var(--fill-color);\n",
1651
+ " border-right-color: var(--fill-color);\n",
1652
+ " }\n",
1653
+ " 40% {\n",
1654
+ " border-color: transparent;\n",
1655
+ " border-right-color: var(--fill-color);\n",
1656
+ " border-top-color: var(--fill-color);\n",
1657
+ " }\n",
1658
+ " 60% {\n",
1659
+ " border-color: transparent;\n",
1660
+ " border-right-color: var(--fill-color);\n",
1661
+ " }\n",
1662
+ " 80% {\n",
1663
+ " border-color: transparent;\n",
1664
+ " border-right-color: var(--fill-color);\n",
1665
+ " border-bottom-color: var(--fill-color);\n",
1666
+ " }\n",
1667
+ " 90% {\n",
1668
+ " border-color: transparent;\n",
1669
+ " border-bottom-color: var(--fill-color);\n",
1670
+ " }\n",
1671
+ " }\n",
1672
+ "</style>\n",
1673
+ "\n",
1674
+ " <script>\n",
1675
+ " async function quickchart(key) {\n",
1676
+ " const quickchartButtonEl =\n",
1677
+ " document.querySelector('#' + key + ' button');\n",
1678
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
1679
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
1680
+ " try {\n",
1681
+ " const charts = await google.colab.kernel.invokeFunction(\n",
1682
+ " 'suggestCharts', [key], {});\n",
1683
+ " } catch (error) {\n",
1684
+ " console.error('Error during call to suggestCharts:', error);\n",
1685
+ " }\n",
1686
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1687
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1688
+ " }\n",
1689
+ " (() => {\n",
1690
+ " let quickchartButtonEl =\n",
1691
+ " document.querySelector('#df-f3876554-a3ae-453f-bdf1-cad6758528ba button');\n",
1692
+ " quickchartButtonEl.style.display =\n",
1693
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1694
+ " })();\n",
1695
+ " </script>\n",
1696
+ "</div>\n",
1697
+ " </div>\n",
1698
+ " </div>\n"
1699
+ ]
1700
+ },
1701
+ "metadata": {},
1702
+ "execution_count": 5
1703
+ }
1704
+ ]
1705
+ },
1706
+ {
1707
+ "cell_type": "code",
1708
+ "source": [
1709
+ "BOS, EOS = ' ', '\\n'\n",
1710
+ "lines = data.apply(lambda row: unicode2ascii((row['title'] + ' ; ' + row['summary'])), axis=1) \\\n",
1711
+ " .apply(lambda line: BOS + line.replace(EOS, ' ')) \\\n",
1712
+ " .tolist()"
1713
+ ],
1714
+ "metadata": {
1715
+ "id": "TqWZI1T1xpT4"
1716
+ },
1717
+ "execution_count": 6,
1718
+ "outputs": []
1719
+ },
1720
+ {
1721
+ "cell_type": "code",
1722
+ "source": [
1723
+ "with open('title_summary_ascii.txt', 'w') as fp:\n",
1724
+ " fp.write('\\n'.join(lines) + '\\n')"
1725
+ ],
1726
+ "metadata": {
1727
+ "id": "RWWmOqh6yLo1"
1728
+ },
1729
+ "execution_count": 7,
1730
+ "outputs": []
1731
+ },
1732
+ {
1733
+ "cell_type": "code",
1734
+ "source": [
1735
+ "!tar -zcf title_summary_ascii.txt.tar.gz title_summary_ascii.txt"
1736
+ ],
1737
+ "metadata": {
1738
+ "id": "ZgoI9yk4y2gl"
1739
+ },
1740
+ "execution_count": 10,
1741
+ "outputs": []
1742
+ },
1743
+ {
1744
+ "cell_type": "code",
1745
+ "source": [
1746
+ "from datasets import load_dataset\n",
1747
+ "from huggingface_hub import HfApi\n",
1748
+ "from huggingface_hub import login\n",
1749
+ "login()"
1750
+ ],
1751
+ "metadata": {
1752
+ "colab": {
1753
+ "base_uri": "https://localhost:8080/",
1754
+ "height": 145,
1755
+ "referenced_widgets": [
1756
+ "55397878572645c0a17e4415f3d793ab",
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+ "b32ae48581754b219ca44dae7876386d",
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+ "5dd0785e18624c9a979a1410804dfbcf",
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+ "d559bbd504e9487a89c69a40a6ce7e78",
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+ "eb507607815846258542ea9462fb69df",
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+ "e189b379d3ac471eb890d8fd89565ae4",
1762
+ "547daa83720541b5901b78f688ff94a8",
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+ "7d5c6dd4a903486f8d373064bb9eab39",
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+ "9791592dfefb40b5a34e99768fedfe84",
1765
+ "6d9195dbea964525ac0c7113d667c635",
1766
+ "a7ff281e09bf40dd83c0458538c16b05",
1767
+ "d88f3c9f812b4f6f8914ab0ddca48490",
1768
+ "3076677021354c9f9bf5f6c35205af47",
1769
+ "c4277491060c4be1813c53678a08c7bb",
1770
+ "4d8434e73d7f487c9d3c48c8285259a5",
1771
+ "a24c4e287afd48fd903b69ab62162a02",
1772
+ "ae9830f0021f46a4a78a049ddebec3c8",
1773
+ "7538e8b96eae432d84328201600b3077",
1774
+ "f1f1856971314b40bc4d7dc8d391aa6a",
1775
+ "cca1e50a99c0413790c59d4b997f8ea3",
1776
+ "6e9634956dd64ad8abe50d11f84ed61f",
1777
+ "adab4d504c184fc3aa8fcc99a8064afd",
1778
+ "1ea802d1c71e43f8a8b4885b3199e93a",
1779
+ "f3cd7afd64914a55883aacd451fe1d47",
1780
+ "f766a4ccae0147808d8d225e239680f0",
1781
+ "21222216f5144071b3e593ab983b73d0",
1782
+ "2c804d9999e54b30a0cfb97f5b6b0962",
1783
+ "c52dbd24f0e24cb892c624911d9ee9a4",
1784
+ "806b3a09ec444403bea8aca028460cef",
1785
+ "ece3cdbed398463b806087f25c42284a",
1786
+ "b57c0e27fde640178d566646f2cc381a",
1787
+ "23f06ba47fc746269df4ec9919be4aee"
1788
+ ]
1789
+ },
1790
+ "id": "4mgmJ-7yxv1K",
1791
+ "outputId": "606b3ebb-1c38-47d7-c76b-4b6353db1b3f"
1792
+ },
1793
+ "execution_count": 8,
1794
+ "outputs": [
1795
+ {
1796
+ "output_type": "display_data",
1797
+ "data": {
1798
+ "text/plain": [
1799
+ "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
1800
+ ],
1801
+ "application/vnd.jupyter.widget-view+json": {
1802
+ "version_major": 2,
1803
+ "version_minor": 0,
1804
+ "model_id": "55397878572645c0a17e4415f3d793ab"
1805
+ }
1806
+ },
1807
+ "metadata": {}
1808
+ }
1809
+ ]
1810
+ },
1811
+ {
1812
+ "cell_type": "code",
1813
+ "source": [
1814
+ "api = HfApi()\n",
1815
+ "api.upload_file(\n",
1816
+ " path_or_fileobj=\"title_summary_ascii.txt.tar.gz\",\n",
1817
+ " path_in_repo=\"title_summary_ascii.txt.tar.gz\",\n",
1818
+ " repo_id=\"sswt/arxiv_sample_50K\",\n",
1819
+ " repo_type=\"dataset\",\n",
1820
+ ")"
1821
+ ],
1822
+ "metadata": {
1823
+ "colab": {
1824
+ "base_uri": "https://localhost:8080/",
1825
+ "height": 67,
1826
+ "referenced_widgets": [
1827
+ "0f2ed90b53c848848dabacb796dca3ef",
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+ "f3070f1f7e6a48c29f5b6f53d80a4e67",
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+ "53236afaac5c468faa154950fb1f9006",
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+ "449326715f6e41b28d38f2eb388263f4",
1831
+ "ee51014a0873457b9f2eb5baf19064c0",
1832
+ "6cb9e0cccda64943bf70c4eb8e5a554b",
1833
+ "5d702dbcccf0435ea925c2f8ec378402",
1834
+ "dba9cb93fe6b4f6585f10ac91f3c41c6",
1835
+ "81430f16cc904f96b272a1bacd5beab3",
1836
+ "87945e9bc7e84bc6a40e96573b915201",
1837
+ "83407c9154d24036913ae84a5864e29d"
1838
+ ]
1839
+ },
1840
+ "id": "BG1Rd0weyI7r",
1841
+ "outputId": "2c89dc9c-f12c-4727-bd7c-c8797543a89f"
1842
+ },
1843
+ "execution_count": 11,
1844
+ "outputs": [
1845
+ {
1846
+ "output_type": "display_data",
1847
+ "data": {
1848
+ "text/plain": [
1849
+ "title_summary_ascii.txt.tar.gz: 0%| | 0.00/17.5M [00:00<?, ?B/s]"
1850
+ ],
1851
+ "application/vnd.jupyter.widget-view+json": {
1852
+ "version_major": 2,
1853
+ "version_minor": 0,
1854
+ "model_id": "0f2ed90b53c848848dabacb796dca3ef"
1855
+ }
1856
+ },
1857
+ "metadata": {}
1858
+ },
1859
+ {
1860
+ "output_type": "execute_result",
1861
+ "data": {
1862
+ "text/plain": [
1863
+ "'https://huggingface.co/datasets/sswt/arxiv_sample_50K/blob/main/title_summary_ascii.txt.tar.gz'"
1864
+ ],
1865
+ "application/vnd.google.colaboratory.intrinsic+json": {
1866
+ "type": "string"
1867
+ }
1868
+ },
1869
+ "metadata": {},
1870
+ "execution_count": 11
1871
+ }
1872
+ ]
1873
+ }
1874
+ ]
1875
+ }