Upload 11 files

.gitattributes

@@ -56,3 +56,13 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+arxiv_0.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_1.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_2.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_3.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_4.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_5.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_6.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_7.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_8.csv filter=lfs diff=lfs merge=lfs -text
+arxiv_9.csv filter=lfs diff=lfs merge=lfs -text

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load_dataset.ipynb

@@ -0,0 +1,550 @@
+{
+ "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",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\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": {
+      "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>id</th>\n",
+       "      <th>submitter</th>\n",
+       "      <th>authors</th>\n",
+       "      <th>title</th>\n",
+       "      <th>comments</th>\n",
+       "      <th>journal-ref</th>\n",
+       "      <th>doi</th>\n",
+       "      <th>report-no</th>\n",
+       "      <th>categories</th>\n",
+       "      <th>license</th>\n",
+       "      <th>abstract</th>\n",
+       "      <th>versions</th>\n",
+       "      <th>update_date</th>\n",
+       "      <th>authors_parsed</th>\n",
+       "      <th>embedding</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <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",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89996</th>\n",
+       "      <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",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}