{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
" from pandas.core import (\n"
]
}
],
"source": [
"import pandas as pd\n",
"import os\n",
"import opendatasets as od"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
"Your Kaggle username:Your Kaggle Key:Your Kaggle Key:Dataset URL: https://www.kaggle.com/datasets/awester/arxiv-embeddings\n",
"Downloading arxiv-embeddings.zip to .\\arxiv-embeddings\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 4.09G/4.09G [03:28<00:00, 21.1MB/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Assign the Kaggle data set URL into variable\n",
"dataset = 'https://www.kaggle.com/datasets/awester/arxiv-embeddings/data'\n",
"# Using opendatasets let's download the data sets\n",
"od.download(dataset)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mC:\\temp\\Temp\\ipykernel_2344\\708505339.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_json\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:\\\\Users\\\\Gordon\\\\OneDrive - The Hong Kong Polytechnic University\\\\YEAR2 SEM2\\\\NLP\\\\URIS\\\\Dataset\\\\arxiv-embeddings\\\\ml-arxiv-embeddings.json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36mread_json\u001b[1;34m(path_or_buf, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, precise_float, date_unit, encoding, encoding_errors, lines, chunksize, compression, nrows, storage_options, dtype_backend, engine)\u001b[0m\n\u001b[0;32m 789\u001b[0m \u001b[0mconvert_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 790\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 791\u001b[1;33m json_reader = JsonReader(\n\u001b[0m\u001b[0;32m 792\u001b[0m \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 793\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer, orient, typ, dtype, convert_axes, convert_dates, keep_default_dates, precise_float, date_unit, encoding, lines, chunksize, compression, nrows, storage_options, encoding_errors, dtype_backend, engine)\u001b[0m\n\u001b[0;32m 903\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"ujson\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 904\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_data_from_filepath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 905\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_preprocess_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 906\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_preprocess_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda3\\lib\\site-packages\\pandas\\io\\json\\_json.py\u001b[0m in \u001b[0;36m_preprocess_data\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 915\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"read\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 916\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 918\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"read\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnrows\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 919\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Anaconda3\\lib\\codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m 317\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 318\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 319\u001b[1;33m \u001b[1;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 320\u001b[0m \u001b[1;31m# decode input (taking the buffer into account)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"data = pd.read_json(\"C:\\\\Users\\\\Gordon\\\\OneDrive - The Hong Kong Polytechnic University\\\\YEAR2 SEM2\\\\NLP\\\\URIS\\\\Dataset\\\\arxiv-embeddings\\\\ml-arxiv-embeddings.json\")"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"chunksize = 10000\n",
"chunks = []\n",
"i=0\n",
"for chunk in pd.read_json(\"C:\\\\Users\\\\Gordon\\\\OneDrive - The Hong Kong Polytechnic University\\\\YEAR2 SEM2\\\\NLP\\\\URIS\\\\Dataset\\\\arxiv-embeddings\\\\ml-arxiv-embeddings.json\", lines=True, chunksize=chunksize):\n",
" chunks.append(chunk)\n",
" i+=1\n",
" if i==10:\n",
" break\n",
"\n",
"# Now, 'chunks' is a list of DataFrame objects. You can concatenate them into a single DataFrame if needed:\n",
"# data = pd.concat(chunks)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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" <th>80000</th>\n",
" <td>1906.05546</td>\n",
" <td>Da Sun Handason Tam</td>\n",
" <td>Da Sun Handason Tam, Wing Cheong Lau, Bin Hu, ...</td>\n",
" <td>Identifying Illicit Accounts in Large Scale E-...</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.SI cs.LG</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Rapid and massive adoption of mobile/ online...</td>\n",
" <td>[{'version': 'v1', 'created': 'Thu, 13 Jun 201...</td>\n",
" <td>2019-06-14</td>\n",
" <td>[[Tam, Da Sun Handason, ], [Lau, Wing Cheong, ...</td>\n",
" <td>[-0.005185681860893, 0.00532205728814, 0.01307...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80001</th>\n",
" <td>1906.05551</td>\n",
" <td>Kai Fan Dr</td>\n",
" <td>Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan</td>\n",
" <td>Lattice Transformer for Speech Translation</td>\n",
" <td>accepted to ACL 2019</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.CL</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Recent advances in sequence modeling have hi...</td>\n",
" <td>[{'version': 'v1', 'created': 'Thu, 13 Jun 201...</td>\n",
" <td>2019-06-14</td>\n",
" <td>[[Zhang, Pei, ], [Chen, Boxing, ], [Ge, Niyu, ...</td>\n",
" <td>[-0.0306410882622, 0.004218348767608, 0.018301...</td>\n",
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" <th>80002</th>\n",
" <td>1906.05560</td>\n",
" <td>Hung-Hsuan Chen</td>\n",
" <td>Yu-Wei Kao and Hung-Hsuan Chen</td>\n",
" <td>Associated Learning: Decomposing End-to-end Ba...</td>\n",
" <td>34 pages, 6 figures, 7 tables</td>\n",
" <td>MIT Neural Computation 33(1), 2021</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.NE cs.LG stat.ML</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Backpropagation (BP) is the cornerstone of t...</td>\n",
" <td>[{'version': 'v1', 'created': 'Thu, 13 Jun 201...</td>\n",
" <td>2021-02-10</td>\n",
" <td>[[Kao, Yu-Wei, ], [Chen, Hung-Hsuan, ]]</td>\n",
" <td>[-0.030108174309134, 0.014727415516972, 0.0341...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80003</th>\n",
" <td>1906.05571</td>\n",
" <td>Ting Yao</td>\n",
" <td>Zhaofan Qiu and Ting Yao and Chong-Wah Ngo and...</td>\n",
" <td>Learning Spatio-Temporal Representation with L...</td>\n",
" <td>CVPR 2019</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.CV</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Convolutional Neural Networks (CNN) have bee...</td>\n",
" <td>[{'version': 'v1', 'created': 'Thu, 13 Jun 201...</td>\n",
" <td>2019-06-14</td>\n",
" <td>[[Qiu, Zhaofan, ], [Yao, Ting, ], [Ngo, Chong-...</td>\n",
" <td>[-0.015157531015574, 0.035704407840967005, 0.0...</td>\n",
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" <tr>\n",
" <th>80004</th>\n",
" <td>1906.05572</td>\n",
" <td>Wenquan Wu</td>\n",
" <td>Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, ...</td>\n",
" <td>Proactive Human-Machine Conversation with Expl...</td>\n",
" <td>Accepted by ACL 2019</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.CL</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Though great progress has been made for huma...</td>\n",
" <td>[{'version': 'v1', 'created': 'Thu, 13 Jun 201...</td>\n",
" <td>2019-11-11</td>\n",
" <td>[[Wu, Wenquan, ], [Guo, Zhen, ], [Zhou, Xiangy...</td>\n",
" <td>[-0.020636107772588, -0.017156293615698003, 0....</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>89995</th>\n",
" <td>1909.12898</td>\n",
" <td>Mahsa Ghasemi</td>\n",
" <td>Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo,...</td>\n",
" <td>Identifying Sparse Low-Dimensional Structures ...</td>\n",
" <td>Accepted for publication in American Control C...</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.LG cs.SY eess.SY stat.ML</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>We consider the problem of learning low-dime...</td>\n",
" <td>[{'version': 'v1', 'created': 'Fri, 27 Sep 201...</td>\n",
" <td>2020-04-09</td>\n",
" <td>[[Ghasemi, Mahsa, ], [Hashemi, Abolfazl, ], [V...</td>\n",
" <td>[-0.015149267390370001, 0.020566524937748, 0.0...</td>\n",
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" <td>1909.12901</td>\n",
" <td>Feifan Wang</td>\n",
" <td>Feifan Wang, Runzhou Jiang, Liqin Zheng, Chun ...</td>\n",
" <td>3D U-Net Based Brain Tumor Segmentation and Su...</td>\n",
" <td>Third place award of the 2019 MICCAI BraTS cha...</td>\n",
" <td>None</td>\n",
" <td>10.1007/978-3-030-46640-4_13</td>\n",
" <td>None</td>\n",
" <td>eess.IV cs.CV</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Past few years have witnessed the prevalence...</td>\n",
" <td>[{'version': 'v1', 'created': 'Sun, 15 Sep 201...</td>\n",
" <td>2020-05-26</td>\n",
" <td>[[Wang, Feifan, ], [Jiang, Runzhou, ], [Zheng,...</td>\n",
" <td>[0.0012591709382830001, 0.003147927578538, 0.0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89997</th>\n",
" <td>1909.12902</td>\n",
" <td>Denys Dutykh</td>\n",
" <td>Beno\\^it Colange and Laurent Vuillon and Sylva...</td>\n",
" <td>Interpreting Distortions in Dimensionality Red...</td>\n",
" <td>5 pages, 6 figures, 22 references. Paper prese...</td>\n",
" <td>Paper presented at IEEE Vis 2019 conference at...</td>\n",
" <td>10.1109/VISUAL.2019.8933568</td>\n",
" <td>None</td>\n",
" <td>cs.CV cs.IR cs.LG</td>\n",
" <td>http://creativecommons.org/licenses/by-nc-sa/4.0/</td>\n",
" <td>To perform visual data exploration, many dim...</td>\n",
" <td>[{'version': 'v1', 'created': 'Fri, 20 Sep 201...</td>\n",
" <td>2020-02-20</td>\n",
" <td>[[Colange, Benoît, ], [Vuillon, Laurent, ], [L...</td>\n",
" <td>[-0.009024421684443, 0.018310621380805, 0.0397...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89998</th>\n",
" <td>1909.12903</td>\n",
" <td>Shupeng Gui</td>\n",
" <td>Shupeng Gui, Xiangliang Zhang, Pan Zhong, Shua...</td>\n",
" <td>PINE: Universal Deep Embedding for Graph Nodes...</td>\n",
" <td>24 pages, 4 figures, 3 tables. arXiv admin not...</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.LG stat.ML</td>\n",
" <td>http://arxiv.org/licenses/nonexclusive-distrib...</td>\n",
" <td>Graph node embedding aims at learning a vect...</td>\n",
" <td>[{'version': 'v1', 'created': 'Wed, 25 Sep 201...</td>\n",
" <td>2019-10-01</td>\n",
" <td>[[Gui, Shupeng, ], [Zhang, Xiangliang, ], [Zho...</td>\n",
" <td>[0.003639858681708, -0.005150159355252, 0.0067...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89999</th>\n",
" <td>1909.12906</td>\n",
" <td>Karol Arndt</td>\n",
" <td>Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, ...</td>\n",
" <td>Meta Reinforcement Learning for Sim-to-real Do...</td>\n",
" <td>Submitted to ICRA 2020</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>cs.CV cs.RO</td>\n",
" <td>http://creativecommons.org/licenses/by/4.0/</td>\n",
" <td>Modern reinforcement learning methods suffer...</td>\n",
" <td>[{'version': 'v1', 'created': 'Mon, 16 Sep 201...</td>\n",
" <td>2019-10-01</td>\n",
" <td>[[Arndt, Karol, ], [Hazara, Murtaza, ], [Ghadi...</td>\n",
" <td>[0.0035310059320180004, -0.009807205758988, 0....</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10000 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" id submitter \\\n",
"80000 1906.05546 Da Sun Handason Tam \n",
"80001 1906.05551 Kai Fan Dr \n",
"80002 1906.05560 Hung-Hsuan Chen \n",
"80003 1906.05571 Ting Yao \n",
"80004 1906.05572 Wenquan Wu \n",
"... ... ... \n",
"89995 1909.12898 Mahsa Ghasemi \n",
"89996 1909.12901 Feifan Wang \n",
"89997 1909.12902 Denys Dutykh \n",
"89998 1909.12903 Shupeng Gui \n",
"89999 1909.12906 Karol Arndt \n",
"\n",
" authors \\\n",
"80000 Da Sun Handason Tam, Wing Cheong Lau, Bin Hu, ... \n",
"80001 Pei Zhang, Boxing Chen, Niyu Ge, Kai Fan \n",
"80002 Yu-Wei Kao and Hung-Hsuan Chen \n",
"80003 Zhaofan Qiu and Ting Yao and Chong-Wah Ngo and... \n",
"80004 Wenquan Wu, Zhen Guo, Xiangyang Zhou, Hua Wu, ... \n",
"... ... \n",
"89995 Mahsa Ghasemi, Abolfazl Hashemi, Haris Vikalo,... \n",
"89996 Feifan Wang, Runzhou Jiang, Liqin Zheng, Chun ... \n",
"89997 Beno\\^it Colange and Laurent Vuillon and Sylva... \n",
"89998 Shupeng Gui, Xiangliang Zhang, Pan Zhong, Shua... \n",
"89999 Karol Arndt, Murtaza Hazara, Ali Ghadirzadeh, ... \n",
"\n",
" title \\\n",
"80000 Identifying Illicit Accounts in Large Scale E-... \n",
"80001 Lattice Transformer for Speech Translation \n",
"80002 Associated Learning: Decomposing End-to-end Ba... \n",
"80003 Learning Spatio-Temporal Representation with L... \n",
"80004 Proactive Human-Machine Conversation with Expl... \n",
"... ... \n",
"89995 Identifying Sparse Low-Dimensional Structures ... \n",
"89996 3D U-Net Based Brain Tumor Segmentation and Su... \n",
"89997 Interpreting Distortions in Dimensionality Red... \n",
"89998 PINE: Universal Deep Embedding for Graph Nodes... \n",
"89999 Meta Reinforcement Learning for Sim-to-real Do... \n",
"\n",
" comments \\\n",
"80000 None \n",
"80001 accepted to ACL 2019 \n",
"80002 34 pages, 6 figures, 7 tables \n",
"80003 CVPR 2019 \n",
"80004 Accepted by ACL 2019 \n",
"... ... \n",
"89995 Accepted for publication in American Control C... \n",
"89996 Third place award of the 2019 MICCAI BraTS cha... \n",
"89997 5 pages, 6 figures, 22 references. Paper prese... \n",
"89998 24 pages, 4 figures, 3 tables. arXiv admin not... \n",
"89999 Submitted to ICRA 2020 \n",
"\n",
" journal-ref \\\n",
"80000 None \n",
"80001 None \n",
"80002 MIT Neural Computation 33(1), 2021 \n",
"80003 None \n",
"80004 None \n",
"... ... \n",
"89995 None \n",
"89996 None \n",
"89997 Paper presented at IEEE Vis 2019 conference at... \n",
"89998 None \n",
"89999 None \n",
"\n",
" doi report-no categories \\\n",
"80000 None None cs.SI cs.LG \n",
"80001 None None cs.CL \n",
"80002 None None cs.NE cs.LG stat.ML \n",
"80003 None None cs.CV \n",
"80004 None None cs.CL \n",
"... ... ... ... \n",
"89995 None None cs.LG cs.SY eess.SY stat.ML \n",
"89996 10.1007/978-3-030-46640-4_13 None eess.IV cs.CV \n",
"89997 10.1109/VISUAL.2019.8933568 None cs.CV cs.IR cs.LG \n",
"89998 None None cs.LG stat.ML \n",
"89999 None None cs.CV cs.RO \n",
"\n",
" license \\\n",
"80000 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"80001 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"80002 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"80003 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"80004 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"... ... \n",
"89995 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"89996 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"89997 http://creativecommons.org/licenses/by-nc-sa/4.0/ \n",
"89998 http://arxiv.org/licenses/nonexclusive-distrib... \n",
"89999 http://creativecommons.org/licenses/by/4.0/ \n",
"\n",
" abstract \\\n",
"80000 Rapid and massive adoption of mobile/ online... \n",
"80001 Recent advances in sequence modeling have hi... \n",
"80002 Backpropagation (BP) is the cornerstone of t... \n",
"80003 Convolutional Neural Networks (CNN) have bee... \n",
"80004 Though great progress has been made for huma... \n",
"... ... \n",
"89995 We consider the problem of learning low-dime... \n",
"89996 Past few years have witnessed the prevalence... \n",
"89997 To perform visual data exploration, many dim... \n",
"89998 Graph node embedding aims at learning a vect... \n",
"89999 Modern reinforcement learning methods suffer... \n",
"\n",
" versions update_date \\\n",
"80000 [{'version': 'v1', 'created': 'Thu, 13 Jun 201... 2019-06-14 \n",
"80001 [{'version': 'v1', 'created': 'Thu, 13 Jun 201... 2019-06-14 \n",
"80002 [{'version': 'v1', 'created': 'Thu, 13 Jun 201... 2021-02-10 \n",
"80003 [{'version': 'v1', 'created': 'Thu, 13 Jun 201... 2019-06-14 \n",
"80004 [{'version': 'v1', 'created': 'Thu, 13 Jun 201... 2019-11-11 \n",
"... ... ... \n",
"89995 [{'version': 'v1', 'created': 'Fri, 27 Sep 201... 2020-04-09 \n",
"89996 [{'version': 'v1', 'created': 'Sun, 15 Sep 201... 2020-05-26 \n",
"89997 [{'version': 'v1', 'created': 'Fri, 20 Sep 201... 2020-02-20 \n",
"89998 [{'version': 'v1', 'created': 'Wed, 25 Sep 201... 2019-10-01 \n",
"89999 [{'version': 'v1', 'created': 'Mon, 16 Sep 201... 2019-10-01 \n",
"\n",
" authors_parsed \\\n",
"80000 [[Tam, Da Sun Handason, ], [Lau, Wing Cheong, ... \n",
"80001 [[Zhang, Pei, ], [Chen, Boxing, ], [Ge, Niyu, ... \n",
"80002 [[Kao, Yu-Wei, ], [Chen, Hung-Hsuan, ]] \n",
"80003 [[Qiu, Zhaofan, ], [Yao, Ting, ], [Ngo, Chong-... \n",
"80004 [[Wu, Wenquan, ], [Guo, Zhen, ], [Zhou, Xiangy... \n",
"... ... \n",
"89995 [[Ghasemi, Mahsa, ], [Hashemi, Abolfazl, ], [V... \n",
"89996 [[Wang, Feifan, ], [Jiang, Runzhou, ], [Zheng,... \n",
"89997 [[Colange, Benoît, ], [Vuillon, Laurent, ], [L... \n",
"89998 [[Gui, Shupeng, ], [Zhang, Xiangliang, ], [Zho... \n",
"89999 [[Arndt, Karol, ], [Hazara, Murtaza, ], [Ghadi... \n",
"\n",
" embedding \n",
"80000 [-0.005185681860893, 0.00532205728814, 0.01307... \n",
"80001 [-0.0306410882622, 0.004218348767608, 0.018301... \n",
"80002 [-0.030108174309134, 0.014727415516972, 0.0341... \n",
"80003 [-0.015157531015574, 0.035704407840967005, 0.0... \n",
"80004 [-0.020636107772588, -0.017156293615698003, 0.... \n",
"... ... \n",
"89995 [-0.015149267390370001, 0.020566524937748, 0.0... \n",
"89996 [0.0012591709382830001, 0.003147927578538, 0.0... \n",
"89997 [-0.009024421684443, 0.018310621380805, 0.0397... \n",
"89998 [0.003639858681708, -0.005150159355252, 0.0067... \n",
"89999 [0.0035310059320180004, -0.009807205758988, 0.... \n",
"\n",
"[10000 rows x 15 columns]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunks[8]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"new_data = []\n",
"for p in chunks:\n",
" temp = p[[\"id\",\"title\",\"embedding\"]]\n",
" new_data.append(temp)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"for i, df in enumerate(new_data):\n",
" df.to_csv(f\"arxiv_{i}.csv\", index=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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"name": "python",
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