cohere.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/anushmutyala/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ijson\n",
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from nltk.stem import WordNetLemmatizer, SnowballStemmer\n",
    "from nltk.stem.porter import *\n",
    "import nltk\n",
    "import cohere\n",
    "import requests\n",
    "import datetime\n",
    "from tqdm import tqdm\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anushmutyala/opt/anaconda3/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3444: DtypeWarning: Columns (0) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>abstract</th>\n",
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       "      <th>publication value</th>\n",
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       "      <td>7C7CAEED</td>\n",
       "      <td>On rank correlation in information retrieval e...</td>\n",
       "      <td>Some methods for rank correlation in evaluatio...</td>\n",
       "      <td>\"\"10.1109/ICPR48806.2021.9411963/,  \"\"10.1109...</td>\n",
       "      <td>ACM SIGIR ForumVolume 41Issue 1June 2007  pp  ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Massimo Melucci</td>\n",
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       "      <th>1</th>\n",
       "      <td>7AEE29E3</td>\n",
       "      <td>The Voting Model for People Search</td>\n",
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       "      <td>\"\"10.1109/ACIT53391.2021.9677217/,  \"\"10.4018...</td>\n",
       "      <td>SIGIR '02: Proceedings of the 25th annual inte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Patrick Pantel</td>\n",
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       "      <td>A comparison of indexing techniques for Japane...</td>\n",
       "      <td>A series of Japanese full-text retrieval exper...</td>\n",
       "      <td>\"\"10.1109/SNLP.2009.5340946/,  \"\"10.1007/s105...</td>\n",
       "      <td>SIGIR '93: Proceedings of the 16th annual inte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hideo Fujii</td>\n",
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       "      <th>4</th>\n",
       "      <td>7D5CD2DF</td>\n",
       "      <td>Feature selection for ranking</td>\n",
       "      <td>Ranking is a very important topic in informati...</td>\n",
       "      <td>\"\"10.1007/978-3-030-95391-1_29/,  \"\"10.1007/9...</td>\n",
       "      <td>SIGIR '07: Proceedings of the 30th annual inte...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Xiubo Geng</td>\n",
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       "      <td>629806</td>\n",
       "      <td>Effectiveness and usability of an online help ...</td>\n",
       "      <td>An empirical study is presented which aims at ...</td>\n",
       "      <td>8543', '327540', '395578', '397153', '398612</td>\n",
       "      <td>Proceedings of the 10th international conferen...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Jérôme Simonin,Noëlle Carbonell,Danielle Pelé</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281238</th>\n",
       "      <td>629807</td>\n",
       "      <td>Busy period analysis of finite QBD processes</td>\n",
       "      <td>We present the number of customers served and ...</td>\n",
       "      <td>340965</td>\n",
       "      <td>ACM SIGMETRICS Performance Evaluation Review</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Chaitanya Garikiparthi,Appie van de Liefvoort,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281239</th>\n",
       "      <td>629808</td>\n",
       "      <td>The Grid as a Single Entity: Towards a Behavio...</td>\n",
       "      <td>Grids emerged in the last decade as large dist...</td>\n",
       "      <td>0</td>\n",
       "      <td>Proceedings of the OTM 2008 Confederated Inter...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Jesús Montes,Alberto Sánchez,Julio J. Valdés,M...</td>\n",
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       "      <td>Multimodal system evaluation using modality ef...</td>\n",
       "      <td>In this paper, we propose two new objective me...</td>\n",
       "      <td>294663', '302639', '572828</td>\n",
       "      <td>Proceedings of the 10th international conferen...</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Manolis Perakakis,Alexandros Potamianos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281241</th>\n",
       "      <td>629813</td>\n",
       "      <td>Oppositional target domain estimation using gr...</td>\n",
       "      <td>In this paper we address the problem of estima...</td>\n",
       "      <td>0</td>\n",
       "      <td>Applied Soft Computing</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Maryam Shokri,Hamid R. Tizhoosh,Mohamed S. Kamel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>281242 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                               name  \\\n",
       "0       7C7CAEED  On rank correlation in information retrieval e...   \n",
       "1       7AEE29E3                 The Voting Model for People Search   \n",
       "2       7D68490B                Document clustering with committees   \n",
       "3       7A488256  A comparison of indexing techniques for Japane...   \n",
       "4       7D5CD2DF                      Feature selection for ranking   \n",
       "...          ...                                                ...   \n",
       "281237    629806  Effectiveness and usability of an online help ...   \n",
       "281238    629807       Busy period analysis of finite QBD processes   \n",
       "281239    629808  The Grid as a Single Entity: Towards a Behavio...   \n",
       "281240    629811  Multimodal system evaluation using modality ef...   \n",
       "281241    629813  Oppositional target domain estimation using gr...   \n",
       "\n",
       "                                                 abstract  \\\n",
       "0       Some methods for rank correlation in evaluatio...   \n",
       "1                                                     NaN   \n",
       "2       Document clustering is useful in many informat...   \n",
       "3       A series of Japanese full-text retrieval exper...   \n",
       "4       Ranking is a very important topic in informati...   \n",
       "...                                                   ...   \n",
       "281237  An empirical study is presented which aims at ...   \n",
       "281238  We present the number of customers served and ...   \n",
       "281239  Grids emerged in the last decade as large dist...   \n",
       "281240  In this paper, we propose two new objective me...   \n",
       "281241  In this paper we address the problem of estima...   \n",
       "\n",
       "                                                citations  \\\n",
       "0        \"\"10.1109/ICPR48806.2021.9411963/,  \"\"10.1109...   \n",
       "1                                                     NaN   \n",
       "2        \"\"10.1109/ACIT53391.2021.9677217/,  \"\"10.4018...   \n",
       "3        \"\"10.1109/SNLP.2009.5340946/,  \"\"10.1007/s105...   \n",
       "4        \"\"10.1007/978-3-030-95391-1_29/,  \"\"10.1007/9...   \n",
       "...                                                   ...   \n",
       "281237       8543', '327540', '395578', '397153', '398612   \n",
       "281238                                             340965   \n",
       "281239                                                  0   \n",
       "281240                         294663', '302639', '572828   \n",
       "281241                                                  0   \n",
       "\n",
       "                                        publication value    year  \\\n",
       "0       ACM SIGIR ForumVolume 41Issue 1June 2007  pp  ...     NaN   \n",
       "1                                                     NaN     NaN   \n",
       "2       SIGIR '02: Proceedings of the 25th annual inte...     NaN   \n",
       "3       SIGIR '93: Proceedings of the 16th annual inte...     NaN   \n",
       "4       SIGIR '07: Proceedings of the 30th annual inte...     NaN   \n",
       "...                                                   ...     ...   \n",
       "281237  Proceedings of the 10th international conferen...  2008.0   \n",
       "281238       ACM SIGMETRICS Performance Evaluation Review  2008.0   \n",
       "281239  Proceedings of the OTM 2008 Confederated Inter...  2008.0   \n",
       "281240  Proceedings of the 10th international conferen...  2008.0   \n",
       "281241                             Applied Soft Computing  2009.0   \n",
       "\n",
       "                                                  authors  \n",
       "0                                         Massimo Melucci  \n",
       "1                                                     NaN  \n",
       "2                                          Patrick Pantel  \n",
       "3                                             Hideo Fujii  \n",
       "4                                              Xiubo Geng  \n",
       "...                                                   ...  \n",
       "281237      Jérôme Simonin,Noëlle Carbonell,Danielle Pelé  \n",
       "281238  Chaitanya Garikiparthi,Appie van de Liefvoort,...  \n",
       "281239  Jesús Montes,Alberto Sánchez,Julio J. Valdés,M...  \n",
       "281240            Manolis Perakakis,Alexandros Potamianos  \n",
       "281241   Maryam Shokri,Hamid R. Tizhoosh,Mohamed S. Kamel  \n",
       "\n",
       "[281242 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.read_csv('final_embedding.csv')\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Some methods for rank correlation in evaluatio...\n",
       "2         Document clustering is useful in many informat...\n",
       "3         A series of Japanese full-text retrieval exper...\n",
       "4         Ranking is a very important topic in informati...\n",
       "5         Content-targeted advertising, the task of auto...\n",
       "                                ...                        \n",
       "281237    An empirical study is presented which aims at ...\n",
       "281238    We present the number of customers served and ...\n",
       "281239    Grids emerged in the last decade as large dist...\n",
       "281240    In this paper, we propose two new objective me...\n",
       "281241    In this paper we address the problem of estima...\n",
       "Name: abstract, Length: 281212, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_abstracts = result['abstract'].dropna()\n",
    "result_abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "abstract_list = result_abstracts.to_list()\n",
    "truth_examples = [(\"clustering algorithms\", abstract_list[0]), (\"ranking models\", abstract_list[1]), (\"advertisement optimization\", abstract_list[2]), (\"social tagging\", abstract_list[3]), (\"clustering algorithms\", abstract_list[4]), (\"collaborative filtering\", abstract_list[5]), (\"sentence ranking\", abstract_list[6])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Paste your API key here. Remember to not share publicly\n",
    "api_key = 'S35VtqP4EcUmvM6Eq8t4Fta72yHwUEUUPefOqlmm'\n",
    "\n",
    "# Create and retrieve a Cohere API key from os.cohere.ai\n",
    "co = cohere.Client(api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Create the prompt (Run this cell to execute required code) {display-mode: \"form\"}\n",
    "\n",
    "class cohereExtractor():\n",
    "    def __init__(self, examples, example_labels, labels, task_desciption, example_prompt):\n",
    "        self.examples = examples\n",
    "        self.example_labels = example_labels\n",
    "        self.labels = labels\n",
    "        self.task_desciption = task_desciption\n",
    "        self.example_prompt = example_prompt\n",
    "\n",
    "    def make_prompt(self, example):\n",
    "        examples = self.examples + [example]\n",
    "        labels = self.example_labels + [\"\"]\n",
    "        return (self.task_desciption +\n",
    "                \"\\n---\\n\".join( [examples[i] + \"\\n\" +\n",
    "                                self.example_prompt + \n",
    "                                 labels[i] for i in range(len(examples))]))\n",
    "\n",
    "    def extract(self, example):\n",
    "      extraction = co.generate(\n",
    "          model='small',\n",
    "          prompt=self.make_prompt(example),\n",
    "          max_tokens=10,\n",
    "          temperature=0.1,\n",
    "          stop_sequences=[\"\\n\"])\n",
    "      return(extraction.generations[0].text[:-1])\n",
    "\n",
    "\n",
    "cohereResearchExtractor = cohereExtractor([e[1] for e in truth_examples], \n",
    "                                       [e[0] for e in truth_examples], [],\n",
    "                                       \"\", \n",
    "                                       \"extract key concept from research paper: \")\n",
    "\n",
    "# Uncomment to inspect the full prompt:\n",
    "# print(cohereMovieExtractor.make_prompt('<input text here>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some methods for rank correlation in evaluation are examined and their relative advantages and disadvantages are discussed. In particular, it is suggested that different test statistics should be used for providing additional information about the experiments other that the one provided by statistical significance testing. Kendall's τ is often used for testing-rank correlation, yet it is little appropriate if the objective of the test is different from what τ was designed for. In particular, attention should be paid to the null hypothesis. Other measures for rank correlation are described. If one test statistic suggests to reject a hypothesis, other test statistics should be used to support or to revise the decision. The paper then focuses on rank correlation between webpage lists ordered by PageRank for applying the general reflections on these test statistics. An interpretation of PageRank behaviour is provided on the basis of the discussion of the test statistics for rank correlation.\n",
      "extract key concept from research paper: clustering algorithms\n",
      "---\n",
      "Document clustering is useful in many information retrieval tasks: document browsing, organization and viewing of retrieval results, generation of Yahoo-like hierarchies of documents, etc. The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. We present a clustering algorithm called CBC (Clustering By Committee) that is shown to produce higher quality clusters in document clustering tasks as compared to several well known clustering algorithms. It initially discovers a set of tight clusters (high intra-group similarity), called committees, that are well scattered in the similarity space (low inter-group similarity). The union of the committees is but a subset of all elements. The algorithm proceeds by assigning elements to their most similar committee. Evaluating cluster quality has always been a difficult task. We present a new evaluation methodology that is based on the editing distance between output clusters and manually constructed classes (the answer key). This evaluation measure is more intuitive and easier to interpret than previous evaluation measures.\n",
      "extract key concept from research paper: ranking models\n",
      "---\n",
      "A series of Japanese full-text retrieval experiments were conducted using an inference network document retrieval model. The retrieval performance of two major indexing methods, character-based and word-based, were evaluated. Using structured queries, the character-based indexing performed retrieval as well as, or slightly better, than the word-based system. This result has practical significance since the character-based indexing speed is considerably faster than the traditional word-based indexing. All the queries in this experiment were automatically formulated from natural language input.\n",
      "extract key concept from research paper: advertisement optimization\n",
      "---\n",
      "Ranking is a very important topic in information retrieval. While algorithms for learning ranking models have been intensively studied, this is not the case for feature selection, despite of its importance. The reality is that many feature selection methods used in classification are directly applied to ranking. We argue that because of the striking differences between ranking and classification, it is better to develop different feature selection methods for ranking. To this end, we propose a new feature selection method in this paper. Specifically, for each feature we use its value to rank the training instances, and define the ranking accuracy in terms of a performance measure or a loss function as the importance of the feature. We also define the correlation between the ranking results of two features as the similarity between them. Based on the definitions, we formulate the feature selection issue as an optimization problem, for which it is to find the features with maximum total importance scores and minimum total similarity scores. We also demonstrate how to solve the optimization problem in an efficient way. We have tested the effectiveness of our feature selection method on two information retrieval datasets and with two ranking models. Experimental results show that our method can outperform traditional feature selection methods for the ranking task.\n",
      "extract key concept from research paper: social tagging\n",
      "---\n",
      "Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.\n",
      "extract key concept from research paper: clustering algorithms\n",
      "---\n",
      "Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot of work has reported that different users may describe the same concept in different ways. We address the problem of personalized tag recommendation for text documents. In particular, we model personalized tag recommendation as a \"query and ranking\" problem and propose a novel graph-based ranking algorithm for interrelated multi-type objects. When a user issues a tagging request, both the document and the user are treated as a part of the query. Tags are then ranked by our graph-based ranking algorithm which takes into consideration both relevance to the document and preference of the user. Finally, the top ranked tags are presented to the user as suggestions. Experiments on a large-scale tagging data set collected from Del.icio.us have demonstrated that our proposed algorithm significantly outperforms algorithms which fail to consider the diversity of different users' interests.\n",
      "extract key concept from research paper: collaborative filtering\n",
      "---\n",
      "Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1 , which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.\n",
      "extract key concept from research paper: sentence ranking\n",
      "---\n",
      "<input text here>\n",
      "extract key concept from research paper: \n"
     ]
    }
   ],
   "source": [
    "print(cohereResearchExtractor.make_prompt('<input text here>'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
    {
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    {
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     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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    {
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     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  0%|          | 14/10000 [00:01<14:50, 11.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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      "  0%|          | 18/10000 [00:01<14:54, 11.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    },
    {
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     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n",
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
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     "name": "stderr",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:  You are past your generate endpoint character limit, please request higher tier access!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/9_/f81q3syj4qd40f4gxdmsrw3c0000gn/T/ipykernel_12050/174452866.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabstract_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mextracted_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcohereResearchExtractor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m         \u001b[0mtraining_set\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mextracted_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/9_/f81q3syj4qd40f4gxdmsrw3c0000gn/T/ipykernel_12050/3281683500.py\u001b[0m in \u001b[0;36mextract\u001b[0;34m(self, example)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m       extraction = co.generate(\n\u001b[0m\u001b[1;32m     21\u001b[0m           \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'small'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m           \u001b[0mprompt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/cohere/client.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, prompt, model, preset, num_generations, max_tokens, temperature, k, p, frequency_penalty, presence_penalty, stop_sequences, return_likelihoods, truncate)\u001b[0m\n\u001b[1;32m    111\u001b[0m             \u001b[0;34m'truncate'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m         })\n\u001b[0;32m--> 113\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjson_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcohere\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGENERATE_URL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0mgenerations\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mGeneration\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/cohere/client.py\u001b[0m in \u001b[0;36m__request\u001b[0;34m(self, json_body, endpoint)\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 281\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'POST'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m                 \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    540\u001b[0m         }\n\u001b[1;32m    541\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    653\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    654\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    437\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunked\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m                 resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    440\u001b[0m                     \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    697\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    698\u001b[0m             \u001b[0;31m# Make the request on the httplib connection object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 699\u001b[0;31m             httplib_response = self._make_request(\n\u001b[0m\u001b[1;32m    700\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    443\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    444\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 445\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    446\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    438\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1370\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1371\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1372\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    320\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 280\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    281\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    282\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    706\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1239\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1240\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1241\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1242\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1243\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1097\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "training_set = []\n",
    "for text in tqdm(abstract_list[:10000]):\n",
    "    try:\n",
    "        extracted_text = cohereResearchExtractor.extract(text)\n",
    "        training_set.append([extracted_text, text])\n",
    "    except Exception as e:\n",
    "        print('ERROR: ', e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(training_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
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