Upload Upload2HuggingfaceHub.ipynb
Browse filesAdd notebook for preparation of raw lines
- Upload2HuggingfaceHub.ipynb +1875 -0
Upload2HuggingfaceHub.ipynb
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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"
|
1380 |
+
],
|
1381 |
+
"metadata": {
|
1382 |
+
"id": "cm2rO13rxLxw"
|
1383 |
+
},
|
1384 |
+
"execution_count": 4,
|
1385 |
+
"outputs": []
|
1386 |
+
},
|
1387 |
+
{
|
1388 |
+
"cell_type": "code",
|
1389 |
+
"source": [
|
1390 |
+
"data = pd.read_json(\"./arxiv_sample.json\")\n",
|
1391 |
+
"print(data.shape)\n",
|
1392 |
+
"data.sample(n=3)"
|
1393 |
+
],
|
1394 |
+
"metadata": {
|
1395 |
+
"colab": {
|
1396 |
+
"base_uri": "https://localhost:8080/",
|
1397 |
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"height": 161
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1398 |
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},
|
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"id": "0iKIJTfBxirM",
|
1400 |
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"outputId": "9660530e-0b9e-4181-ae2a-9cc3978a5706"
|
1401 |
+
},
|
1402 |
+
"execution_count": 5,
|
1403 |
+
"outputs": [
|
1404 |
+
{
|
1405 |
+
"output_type": "stream",
|
1406 |
+
"name": "stdout",
|
1407 |
+
"text": [
|
1408 |
+
"(50000, 5)\n"
|
1409 |
+
]
|
1410 |
+
},
|
1411 |
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{
|
1412 |
+
"output_type": "execute_result",
|
1413 |
+
"data": {
|
1414 |
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"text/plain": [
|
1415 |
+
" 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",
|
1433 |
+
"19165 http://arxiv.org/abs/2110.02471v3 "
|
1434 |
+
],
|
1435 |
+
"text/html": [
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"\n",
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" <div id=\"df-4f195fda-0863-48bc-ad3a-a9544293b583\" class=\"colab-df-container\">\n",
|
1438 |
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" <div>\n",
|
1439 |
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"<style scoped>\n",
|
1440 |
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" .dataframe tbody tr th:only-of-type {\n",
|
1441 |
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" vertical-align: middle;\n",
|
1442 |
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" }\n",
|
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"\n",
|
1444 |
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" .dataframe tbody tr th {\n",
|
1445 |
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
1448 |
+
" .dataframe thead th {\n",
|
1449 |
+
" text-align: right;\n",
|
1450 |
+
" }\n",
|
1451 |
+
"</style>\n",
|
1452 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
1453 |
+
" <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",
|
1577 |
+
" 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": [
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1756 |
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"55397878572645c0a17e4415f3d793ab",
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1758 |
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"5dd0785e18624c9a979a1410804dfbcf",
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1759 |
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1760 |
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"eb507607815846258542ea9462fb69df",
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1762 |
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1763 |
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1765 |
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1766 |
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"a7ff281e09bf40dd83c0458538c16b05",
|
1767 |
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|
1768 |
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"3076677021354c9f9bf5f6c35205af47",
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1769 |
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1771 |
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|
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|
1783 |
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"c52dbd24f0e24cb892c624911d9ee9a4",
|
1784 |
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"806b3a09ec444403bea8aca028460cef",
|
1785 |
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"ece3cdbed398463b806087f25c42284a",
|
1786 |
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"b57c0e27fde640178d566646f2cc381a",
|
1787 |
+
"23f06ba47fc746269df4ec9919be4aee"
|
1788 |
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]
|
1789 |
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},
|
1790 |
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"id": "4mgmJ-7yxv1K",
|
1791 |
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"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…"
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1800 |
+
],
|
1801 |
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1802 |
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"version_major": 2,
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1803 |
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"version_minor": 0,
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1804 |
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"model_id": "55397878572645c0a17e4415f3d793ab"
|
1805 |
+
}
|
1806 |
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},
|
1807 |
+
"metadata": {}
|
1808 |
+
}
|
1809 |
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]
|
1810 |
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},
|
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 |
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"height": 67,
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1826 |
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"referenced_widgets": [
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"0f2ed90b53c848848dabacb796dca3ef",
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1828 |
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"f3070f1f7e6a48c29f5b6f53d80a4e67",
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"53236afaac5c468faa154950fb1f9006",
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"449326715f6e41b28d38f2eb388263f4",
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"ee51014a0873457b9f2eb5baf19064c0",
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"5d702dbcccf0435ea925c2f8ec378402",
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|
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"83407c9154d24036913ae84a5864e29d"
|
1838 |
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]
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1839 |
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},
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1840 |
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"id": "BG1Rd0weyI7r",
|
1841 |
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"outputId": "2c89dc9c-f12c-4727-bd7c-c8797543a89f"
|
1842 |
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},
|
1843 |
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"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 |
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],
|
1851 |
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"application/vnd.jupyter.widget-view+json": {
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1852 |
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"version_major": 2,
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"version_minor": 0,
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"model_id": "0f2ed90b53c848848dabacb796dca3ef"
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1855 |
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}
|
1856 |
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},
|
1857 |
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"metadata": {}
|
1858 |
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},
|
1859 |
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{
|
1860 |
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"output_type": "execute_result",
|
1861 |
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"data": {
|
1862 |
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"text/plain": [
|
1863 |
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"'https://huggingface.co/datasets/sswt/arxiv_sample_50K/blob/main/title_summary_ascii.txt.tar.gz'"
|
1864 |
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],
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"metadata": {},
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"execution_count": 11
|
1871 |
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}
|
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]
|
1873 |
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}
|
1874 |
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]
|
1875 |
+
}
|