Executive_Summary.ipynb

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   "source": [
    "\n",
    "### Executive Summary generation\n",
    "\n",
    "---\n",
    "\n"
   ]
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   "source": [
    "import pandas as pd\n",
    "from io import StringIO, BytesIO\n",
    "import urllib.request\n",
    "from datasets import load_dataset\n",
    "import pickle"
   ]
  },
  {
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     "output_type": "stream",
     "text": [
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      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: lxml in /usr/lib/python3/dist-packages (4.5.0)\n",
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      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install beautifulsoup4\n",
    "! pip install lxml"
   ]
  },
  {
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   "execution_count": 45,
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     "text": [
      "13966\n",
      "13966\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with open(\"/datasets/o4b/train.source\") as source ,open(\"/datasets/o4b/train.target\") as target:\n",
    "    prompt = source.readlines()\n",
    "    summary = target.readlines()\n",
    "#print(prompt[0])\n",
    "#type(summary[0])\n",
    "print(len(prompt))\n",
    "print(len(summary))"
   ]
  },
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      "  Building wheel for openai (pyproject.toml) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for openai: filename=openai-0.23.1-py3-none-any.whl size=54507 sha256=c81617a818b3e01312a7a334e923b03d550822a7905ef86ceb3c2c32cc8859f3\n",
      "  Stored in directory: /root/.cache/pip/wheels/3e/28/40/6b19cb27f497029109e93d5d6e46d5657536d6380baf4a6c68\n",
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      "Successfully installed openai-0.23.1 openpyxl-3.0.10 pandas-stubs-1.5.0.221003 types-pytz-2022.4.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import openai\n",
    "openai.api_key = \"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\"\n",
    "openai.organization = \"org-JdJEdsktHjLJlLB7I3YotzEM\"\n",
    "openai.Model.list()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:22:38.641511Z",
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     "shell.execute_reply": "2022-10-09T04:22:41.365434Z",
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    },
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    "outputId": "6d812b0b-dc05-44e4-a0d2-c537bbddb729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: datasets in /usr/local/lib/python3.9/dist-packages (2.3.2)\n",
      "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (2022.5.0)\n",
      "Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.8.1)\n",
      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.9/dist-packages (from datasets) (3.8.1)\n",
      "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (2.28.1)\n",
      "Requirement already satisfied: dill<0.3.6 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.3.5.1)\n",
      "Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.0.0)\n",
      "Requirement already satisfied: multiprocess in /usr/local/lib/python3.9/dist-packages (from datasets) (0.70.13)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from datasets) (21.3)\n",
      "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (4.64.0)\n",
      "Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (8.0.0)\n",
      "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.18.0)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from datasets) (1.23.1)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.4.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.3.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (5.4.1)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (3.7.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from packaging->datasets) (3.0.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests>=2.19.0->datasets) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests>=2.19.0->datasets) (2019.11.28)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (2.1.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (1.26.10)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.2.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.7.2)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (4.0.2)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (18.2.0)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (6.0.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.14.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install datasets\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:22:41.368481Z",
     "iopub.status.busy": "2022-10-09T04:22:41.368220Z",
     "iopub.status.idle": "2022-10-09T04:22:41.373067Z",
     "shell.execute_reply": "2022-10-09T04:22:41.372198Z",
     "shell.execute_reply.started": "2022-10-09T04:22:41.368456Z"
    },
    "id": "TnEDesJWyLIv"
   },
   "outputs": [],
   "source": []
  },
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    "execution": {
     "iopub.execute_input": "2022-10-09T04:22:41.374533Z",
     "iopub.status.busy": "2022-10-09T04:22:41.374285Z",
     "iopub.status.idle": "2022-10-09T04:25:05.136192Z",
     "shell.execute_reply": "2022-10-09T04:25:05.135318Z",
     "shell.execute_reply.started": "2022-10-09T04:22:41.374513Z"
    },
    "id": "6I7ZbgS6yNUY",
    "outputId": "38ac0430-63bb-45b9-f40c-4614f783c283"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e60e89de46d470686f9cfb8e4fc0f12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/3.23k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b27da240ec764193b15521d79240ce20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration default\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset cnn_dailymail/default to /root/.cache/huggingface/datasets/cnn_dailymail/default/3.0.0/1b3c71476f6d152c31c1730e83ccb08bcf23e348233f4fcc11e182248e6bf7de...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2b63c59951c491fbacc1d29507eaaf1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2499a6ca7519494a92b730f137bc8deb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/159M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d20042e4523459698fd3b1541b3e8b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/376M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0356e546cf4c47c7b2d1037a2173039f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/12.3M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4c1dcb1068d4095af6ab0a4b2fabaaf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/661k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "070dd7793f26430b877e6f7be7d3c22d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/572k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac67ef38b8cb4e5aa000595a8acda7ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cda12d5a0f7b4de087f3760956f02e2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7369168e60e34ad4885bd8f667876d6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/default/3.0.0/1b3c71476f6d152c31c1730e83ccb08bcf23e348233f4fcc11e182248e6bf7de. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c32935926de4f7f8dd88e1d8184c06f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = load_dataset(\"cnn_dailymail\", version =\"3.0.0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:05.138116Z",
     "iopub.status.busy": "2022-10-09T04:25:05.137874Z",
     "iopub.status.idle": "2022-10-09T04:25:05.142759Z",
     "shell.execute_reply": "2022-10-09T04:25:05.141839Z",
     "shell.execute_reply.started": "2022-10-09T04:25:05.138094Z"
    },
    "id": "nAAcCmez0KmU",
    "outputId": "590d7e9c-ab1a-47ff-8300-9bd3852e95cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features: ['article', 'highlights', 'id']\n"
     ]
    }
   ],
   "source": [
    "print(f\"Features: {dataset['train'].column_names}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:05.146142Z",
     "iopub.status.busy": "2022-10-09T04:25:05.145934Z",
     "iopub.status.idle": "2022-10-09T04:25:05.152204Z",
     "shell.execute_reply": "2022-10-09T04:25:05.151274Z",
     "shell.execute_reply.started": "2022-10-09T04:25:05.146123Z"
    },
    "id": "7dVzg0j00rMZ",
    "outputId": "56a3181f-273b-469b-ba25-dd2869fbe6cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Article (excerpt of 500 characters, total length: 4051): \n",
      "Editor's note: In our Behind the Scenes series, CNN correspondents share their experiences in covering news and analyze the stories behind the events. Here, Soledad O'Brien takes users inside a jail where many of the inmates are mentally ill. An inmate housed on the \"forgotten floor,\" where many mentally ill inmates are housed in Miami before trial. MIAMI, Florida (CNN) -- The ninth floor of the Miami-Dade pretrial detention facility is dubbed the \"forgotten floor.\" Here, inmates with the most s\n",
      "\n",
      "Summary (length: 281):\n",
      "Mentally ill inmates in Miami are housed on the \"forgotten floor\"\n",
      "Judge Steven Leifman says most are there as a result of \"avoidable felonies\"\n",
      "While CNN tours facility, patient shouts: \"I am the son of the president\"\n",
      "Leifman says the system is unjust and he's fighting for change .\n"
     ]
    }
   ],
   "source": [
    "sample = dataset[\"train\"][1]\n",
    "print(f\"\"\"Article (excerpt of 500 characters, total length: {len(sample[\"article\"])}): \"\"\")\n",
    "print(sample[\"article\"][:500])\n",
    "print(f'\\nSummary (length: {len(sample[\"highlights\"])}):')\n",
    "print(sample[\"highlights\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F7KCJynY1PsM"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:05.155205Z",
     "iopub.status.busy": "2022-10-09T04:25:05.154882Z",
     "iopub.status.idle": "2022-10-09T04:25:05.158952Z",
     "shell.execute_reply": "2022-10-09T04:25:05.158055Z",
     "shell.execute_reply.started": "2022-10-09T04:25:05.155183Z"
    },
    "id": "JX-3oSik0rJq"
   },
   "outputs": [],
   "source": [
    "sample_text = dataset[\"train\"][1][\"article\"][:2000]\n",
    "# We'll collect the generated summaries of each model in a dictionary \n",
    "summaries = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:05.163571Z",
     "iopub.status.busy": "2022-10-09T04:25:05.163110Z",
     "iopub.status.idle": "2022-10-09T04:25:07.821090Z",
     "shell.execute_reply": "2022-10-09T04:25:07.819937Z",
     "shell.execute_reply.started": "2022-10-09T04:25:05.163549Z"
    },
    "id": "Ivcrx7vj2FfM",
    "outputId": "19e064a6-487b-4cc1-8dae-588f4550001b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /usr/local/lib/python3.9/dist-packages (3.7)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk) (8.1.3)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from nltk) (4.64.0)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk) (1.1.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.9/dist-packages (from nltk) (2022.7.9)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:07.824691Z",
     "iopub.status.busy": "2022-10-09T04:25:07.824472Z",
     "iopub.status.idle": "2022-10-09T04:25:09.065016Z",
     "shell.execute_reply": "2022-10-09T04:25:09.064375Z",
     "shell.execute_reply.started": "2022-10-09T04:25:07.824664Z"
    },
    "id": "Zx9IDaC92Pum",
    "outputId": "4cae0ca2-fd4b-483f-c584-c82c5fb6df99"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['The U.S. are a country.', 'The U.N. is an organization.']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import sent_tokenize\n",
    "nltk.download(\"punkt\")\n",
    "string = \"The U.S. are a country. The U.N. is an organization.\"\n",
    "sent_tokenize(string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:09.068994Z",
     "iopub.status.busy": "2022-10-09T04:25:09.068781Z",
     "iopub.status.idle": "2022-10-09T04:25:09.072631Z",
     "shell.execute_reply": "2022-10-09T04:25:09.071893Z",
     "shell.execute_reply.started": "2022-10-09T04:25:09.068973Z"
    },
    "id": "Cuu99lyM2h2f"
   },
   "outputs": [],
   "source": [
    "def three_sentence_summary(text):\n",
    "  return \"\\n\".join(sent_tokenize(text)[:3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:09.075491Z",
     "iopub.status.busy": "2022-10-09T04:25:09.075262Z",
     "iopub.status.idle": "2022-10-09T04:25:09.080199Z",
     "shell.execute_reply": "2022-10-09T04:25:09.079594Z",
     "shell.execute_reply.started": "2022-10-09T04:25:09.075472Z"
    },
    "id": "imXNnyX22jdi",
    "outputId": "84e2551a-c749-4fc5-9a26-b76e9c8b992f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Editor\\'s note: In our Behind the Scenes series, CNN correspondents share their experiences in covering news and analyze the stories behind the events.\\nHere, Soledad O\\'Brien takes users inside a jail where many of the inmates are mentally ill. An inmate housed on the \"forgotten floor,\" where many mentally ill inmates are housed in Miami before trial.\\nMIAMI, Florida (CNN) -- The ninth floor of the Miami-Dade pretrial detention facility is dubbed the \"forgotten floor.\"'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summaries[\"baseline\"]=three_sentence_summary(sample_text)\n",
    "summaries[\"baseline\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:09.083204Z",
     "iopub.status.busy": "2022-10-09T04:25:09.083013Z",
     "iopub.status.idle": "2022-10-09T04:25:11.709544Z",
     "shell.execute_reply": "2022-10-09T04:25:11.708733Z",
     "shell.execute_reply.started": "2022-10-09T04:25:09.083187Z"
    },
    "id": "-BYyVC0s2xRp",
    "outputId": "0671f64a-222e-4641-866a-0e25748fd0d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: transformers in /usr/local/lib/python3.9/dist-packages (4.20.1)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.23.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (21.3)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.8.1)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.7.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.28.1)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.64.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (5.4.1)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.7.9)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.13,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.12.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (4.3.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.9/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.10)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests->transformers) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests->transformers) (2019.11.28)\n",
      "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.1.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "383a08c3b26a46f9a33804e149b3a2f0",
      "42b1a30b855746ad93a44c935f7ab750",
      "3f670e4a182c49a29df66cbfd19dff6a",
      "5b66c5371ea048498cae064048ebb896",
      "8db14569c40d45f796a5262290fd192d",
      "97f88f7a626a40c09d122460df2076d7",
      "b993516dd6db428d952091ded57750f7",
      "348ac18b794f43379f21090954d5de02",
      "465494c544904bb88502cded0710224f",
      "1660bf2a39144555800c043287e7e1de",
      "8d39df878ec442c2aed29ca466736ba5",
      "e6733eeacb674162a99fc4ccb7825bb1",
      "5f602c4e8ce2436595f32a729396a9c7",
      "027096afdf1446bb9c8175b022b3b2b0",
      "25d601ecbe704a51aa733a5ee8489d86",
      "fb3a88dd4cff47a9a8dcdf0c19944a87",
      "ccae0153c8fa422c8dcf1516b86293b3",
      "b2b58112303e45ecb0fd3feb1918ac12",
      "6d6a580e7c194fa38013a4a29bea28b2",
      "6938455ae8574211929bfa4aeab740a1",
      "361963f46959432facd1796e7b88e5f6",
      "9c20a534b92e4d42af60b418633e6176"
     ]
    },
    "execution": {
     "iopub.execute_input": "2022-10-09T04:25:11.713261Z",
     "iopub.status.busy": "2022-10-09T04:25:11.713035Z",
     "iopub.status.idle": "2022-10-09T04:28:19.712591Z",
     "shell.execute_reply": "2022-10-09T04:28:19.711397Z",
     "shell.execute_reply.started": "2022-10-09T04:25:11.713235Z"
    },
    "id": "Zw4nRgYw3AeN",
    "outputId": "1d64f9cb-8e7f-4395-ff40-7e5d4c41ee7f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ef40960ded342ab936cd7a7342621e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/689 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "296749f7dc374655bed5b160f2c11122",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/5.99G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dade7e474e6a4e3c8b6dfb26dd4d5594",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/0.99M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11747707530941a499ac1f7e244823f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/446k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0bfdd824b6a43429c09d476dfd462e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.29M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
      "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline, set_seed\n",
    "set_seed(42)\n",
    "pipe = pipeline(\"text-generation\", model=\"gpt2-xl\")\n",
    "gpt2_query = sample_text + \"\\nTL;DR:\\n\"\n",
    "pipe_out = pipe(gpt2_query, max_length=512, clean_up_tokenization_spaces=True) \n",
    "summaries[\"gpt2\"] = \"\\n\".join(sent_tokenize(pipe_out[0][\"generated_text\"][len(gpt2_query) :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:28:19.717282Z",
     "iopub.status.busy": "2022-10-09T04:28:19.717056Z",
     "iopub.status.idle": "2022-10-09T04:29:40.407412Z",
     "shell.execute_reply": "2022-10-09T04:29:40.406150Z",
     "shell.execute_reply.started": "2022-10-09T04:28:19.717258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d140ffd60ace456ab5c85d9d207070f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f64107bb2614befa836ea36f2dd78a5",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "506578ca26f34d6cb2de884d56517e65",
       "version_major": 2,
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     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/dist-packages/transformers/models/t5/tokenization_t5_fast.py:156: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
      "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
      "- Be aware that you SHOULD NOT rely on t5-large automatically truncating your input to 512 when padding/encoding.\n",
      "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
      "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "pipe = pipeline(\"summarization\", model=\"t5-large\")\n",
    "pipe_out = pipe(sample_text)\n",
    "summaries[\"t5\"] = \"\\n\".join(sent_tokenize(pipe_out[0][\"summary_text\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:29:40.413295Z",
     "iopub.status.busy": "2022-10-09T04:29:40.413070Z",
     "iopub.status.idle": "2022-10-09T04:30:22.468469Z",
     "shell.execute_reply": "2022-10-09T04:30:22.467904Z",
     "shell.execute_reply.started": "2022-10-09T04:29:40.413274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c1e7d15cf4d444c9bb720a63e1baa7a",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ec1efab26ca4409bd6ba041203f0afc",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
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    },
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_major": 2,
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Mentally ill inmates are housed on the \"forgotten floor\" of Miami-Dade jail.\\nMost often, they face drug charges or charges of assaulting an officer.\\nJudge Steven Leifman says the arrests often result from confrontations with police.\\nHe says about one-third of all people in the county jails are mentally ill.'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = pipeline(\"summarization\", model=\"facebook/bart-large-cnn\") \n",
    "pipe_out = pipe(sample_text)\n",
    "summaries[\"bart\"] = \"\\n\".join(sent_tokenize(pipe_out[0][\"summary_text\"]))\n",
    "summaries[\"bart\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:30:22.473120Z",
     "iopub.status.busy": "2022-10-09T04:30:22.472740Z",
     "iopub.status.idle": "2022-10-09T04:31:13.773727Z",
     "shell.execute_reply": "2022-10-09T04:31:13.772598Z",
     "shell.execute_reply.started": "2022-10-09T04:30:22.473096Z"
    }
   },
   "outputs": [
    {
     "data": {
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     },
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    },
    {
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    },
    {
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     },
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    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "915acb2405ec4e06bd9be5a6d79d9ef7",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pipe = pipeline(\"summarization\", model=\"google/pegasus-cnn_dailymail\") \n",
    "pipe_out = pipe(sample_text)\n",
    "summaries[\"pegasus\"] = pipe_out[0][\"summary_text\"].replace(\" .<n>\", \".\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:31:13.779327Z",
     "iopub.status.busy": "2022-10-09T04:31:13.779136Z",
     "iopub.status.idle": "2022-10-09T04:31:13.784406Z",
     "shell.execute_reply": "2022-10-09T04:31:13.783697Z",
     "shell.execute_reply.started": "2022-10-09T04:31:13.779307Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GROUND TRUTH\n",
      "Mentally ill inmates in Miami are housed on the \"forgotten floor\"\n",
      "Judge Steven Leifman says most are there as a result of \"avoidable felonies\"\n",
      "While CNN tours facility, patient shouts: \"I am the son of the president\"\n",
      "Leifman says the system is unjust and he's fighting for change .\n",
      "\n",
      "BASELINE\n",
      "Editor's note: In our Behind the Scenes series, CNN correspondents share their experiences in covering news and analyze the stories behind the events.\n",
      "Here, Soledad O'Brien takes users inside a jail where many of the inmates are mentally ill. An inmate housed on the \"forgotten floor,\" where many mentally ill inmates are housed in Miami before trial.\n",
      "MIAMI, Florida (CNN) -- The ninth floor of the Miami-Dade pretrial detention facility is dubbed the \"forgotten floor.\"\n",
      "\n",
      "GPT2\n",
      "- Miami-Dade jails have more mentally ill inmates than other counties - About one-third of the mentally ill in Miami-Dade county are housed here - All the mentally ill prisoners have no shoes, no beds, and no mattresses  \n",
      "\"You don't want to be the judge because you're dealing with a lot of people you don't know,\" Leif\n",
      "\n",
      "T5\n",
      "mentally ill inmates are housed on the ninth floor of a florida jail .\n",
      "most face drug charges or charges of assaulting an officer .\n",
      "judge says arrests often result from confrontations with police .\n",
      "one-third of all people in Miami-dade county jails are mental ill .\n",
      "\n",
      "BART\n",
      "Mentally ill inmates are housed on the \"forgotten floor\" of Miami-Dade jail.\n",
      "Most often, they face drug charges or charges of assaulting an officer.\n",
      "Judge Steven Leifman says the arrests often result from confrontations with police.\n",
      "He says about one-third of all people in the county jails are mentally ill.\n",
      "\n",
      "PEGASUS\n",
      "Mentally ill inmates in Miami are housed on the \"forgotten floor\"<n>The ninth floor is where they're held until they're ready to appear in court.\n",
      "Most often, they face drug charges or charges of assaulting an officer.\n",
      "They end up on the ninth floor severely mentally disturbed .\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"GROUND TRUTH\") \n",
    "print(dataset[\"train\"][1][\"highlights\"])\n",
    "print(\"\")\n",
    "for model_name in summaries: \n",
    "    print(model_name.upper()) \n",
    "    print(summaries[model_name]) \n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:31:13.786622Z",
     "iopub.status.busy": "2022-10-09T04:31:13.786404Z",
     "iopub.status.idle": "2022-10-09T04:31:17.005993Z",
     "shell.execute_reply": "2022-10-09T04:31:17.005131Z",
     "shell.execute_reply.started": "2022-10-09T04:31:13.786604Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    }
   ],
   "source": [
    "! pip install datasets transformers sacrebleu==1.5.1 -qqq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:31:17.009916Z",
     "iopub.status.busy": "2022-10-09T04:31:17.009693Z",
     "iopub.status.idle": "2022-10-09T04:31:17.014057Z",
     "shell.execute_reply": "2022-10-09T04:31:17.013406Z",
     "shell.execute_reply.started": "2022-10-09T04:31:17.009890Z"
    }
   },
   "outputs": [],
   "source": [
    "from datasets import load_metric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:31:17.017001Z",
     "iopub.status.busy": "2022-10-09T04:31:17.016805Z",
     "iopub.status.idle": "2022-10-09T04:31:17.271403Z",
     "shell.execute_reply": "2022-10-09T04:31:17.270358Z",
     "shell.execute_reply.started": "2022-10-09T04:31:17.016984Z"
    }
   },
   "outputs": [],
   "source": [
    "import datasets\n",
    "blue_metric = datasets.load_metric('sacrebleu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T04:32:26.130758Z",
     "iopub.status.busy": "2022-10-09T04:32:26.129984Z",
     "iopub.status.idle": "2022-10-09T04:32:26.156729Z",
     "shell.execute_reply": "2022-10-09T04:32:26.156028Z",
     "shell.execute_reply.started": "2022-10-09T04:32:26.130730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
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       "      <th>counts</th>\n",
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       "      <th>totals</th>\n",
       "      <td>[12, 10, 8, 6]</td>\n",
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       "      <th>precisions</th>\n",
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       "      <td>12</td>\n",
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       "      <th>ref_len</th>\n",
       "      <td>12</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Value\n",
       "score                          0.0\n",
       "counts                [4, 0, 0, 0]\n",
       "totals              [12, 10, 8, 6]\n",
       "precisions  [33.33, 0.0, 0.0, 0.0]\n",
       "bp                             1.0\n",
       "sys_len                         12\n",
       "ref_len                         12"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "blue_metric.add(prediction=\"the the the the the the\", reference=[\"the cat is on the mat\"])\n",
    "results = blue_metric.compute(smooth_method=\"floor\", smooth_value=0) \n",
    "results[\"precisions\"] = [np.round(p, 2) for p in results[\"precisions\"]] \n",
    "pd.DataFrame.from_dict(results, orient=\"index\", columns=[\"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T05:02:36.878036Z",
     "iopub.status.busy": "2022-10-09T05:02:36.877691Z",
     "iopub.status.idle": "2022-10-09T05:02:36.892370Z",
     "shell.execute_reply": "2022-10-09T05:02:36.891678Z",
     "shell.execute_reply.started": "2022-10-09T05:02:36.878013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>57.893007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>[5, 3, 2, 1]</td>\n",
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       "      <th>totals</th>\n",
       "      <td>[5, 4, 3, 2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precisions</th>\n",
       "      <td>[100.0, 75.0, 66.67, 50.0]</td>\n",
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       "    <tr>\n",
       "      <th>bp</th>\n",
       "      <td>0.818731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sys_len</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>ref_len</th>\n",
       "      <td>6</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Value\n",
       "score                        57.893007\n",
       "counts                    [5, 3, 2, 1]\n",
       "totals                    [5, 4, 3, 2]\n",
       "precisions  [100.0, 75.0, 66.67, 50.0]\n",
       "bp                            0.818731\n",
       "sys_len                              5\n",
       "ref_len                              6"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blue_metric.add(\n",
    "    prediction=\"the cat is on mat\", reference=[\"the cat is on the mat\"])\n",
    "results = blue_metric.compute(smooth_method=\"floor\", smooth_value=0)\n",
    "results[\"precisions\"] = [np.round(p, 2) for p in results[\"precisions\"]]\n",
    "pd.DataFrame.from_dict(results, orient=\"index\", columns=[\"Value\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-10-09T05:03:57.302542Z",
     "iopub.status.busy": "2022-10-09T05:03:57.302205Z",
     "iopub.status.idle": "2022-10-09T05:04:01.358502Z",
     "shell.execute_reply": "2022-10-09T05:04:01.357474Z",
     "shell.execute_reply.started": "2022-10-09T05:03:57.302516Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
      "Collecting rouge_score\n",
      "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: absl-py in /usr/local/lib/python3.9/dist-packages (from rouge_score) (1.1.0)\n",
      "Requirement already satisfied: nltk in /usr/local/lib/python3.9/dist-packages (from rouge_score) (3.7)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from rouge_score) (1.23.1)\n",
      "Requirement already satisfied: six>=1.14.0 in /usr/lib/python3/dist-packages (from rouge_score) (1.14.0)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (8.1.3)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (2022.7.9)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (1.1.0)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.9/dist-packages (from nltk->rouge_score) (4.64.0)\n",
      "Building wheels for collected packages: rouge_score\n",
      "  Building wheel for rouge_score (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24936 sha256=3c4128f2c434f665898815fa77a9541113144c9620b12247f2166df0fb93d343\n",
      "  Stored in directory: /root/.cache/pip/wheels/9b/3d/39/09558097d3119ca0a4d462df68f22c6f3c1b345ac63a09b86e\n",
      "Successfully built rouge_score\n",
      "Installing collected packages: rouge_score\n",
      "Successfully installed rouge_score-0.1.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "! pip install rouge_score\n",
    "rouge_metric = load_metric(\"rouge\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
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     "shell.execute_reply.started": "2022-10-09T05:04:16.679498Z"
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   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rouge1</th>\n",
       "      <th>rouge2</th>\n",
       "      <th>rougeL</th>\n",
       "      <th>rougeLsum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>baseline</th>\n",
       "      <td>0.365079</td>\n",
       "      <td>0.145161</td>\n",
       "      <td>0.206349</td>\n",
       "      <td>0.285714</td>\n",
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       "    <tr>\n",
       "      <th>gpt2</th>\n",
       "      <td>0.275229</td>\n",
       "      <td>0.093458</td>\n",
       "      <td>0.201835</td>\n",
       "      <td>0.275229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t5</th>\n",
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       "      <td>0.130435</td>\n",
       "      <td>0.255319</td>\n",
       "      <td>0.382979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bart</th>\n",
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       "      <td>0.222222</td>\n",
       "      <td>0.316832</td>\n",
       "      <td>0.415842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pegasus</th>\n",
       "      <td>0.323232</td>\n",
       "      <td>0.206186</td>\n",
       "      <td>0.282828</td>\n",
       "      <td>0.323232</td>\n",
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       "</table>\n",
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      "text/plain": [
       "            rouge1    rouge2    rougeL  rougeLsum\n",
       "baseline  0.365079  0.145161  0.206349   0.285714\n",
       "gpt2      0.275229  0.093458  0.201835   0.275229\n",
       "t5        0.382979  0.130435  0.255319   0.382979\n",
       "bart      0.475248  0.222222  0.316832   0.415842\n",
       "pegasus   0.323232  0.206186  0.282828   0.323232"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reference = dataset[\"train\"][1][\"highlights\"]\n",
    "records = []\n",
    "rouge_names = [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"]\n",
    "\n",
    "for model_name in summaries:\n",
    "    rouge_metric.add(prediction=summaries[model_name], reference=reference)\n",
    "    score = rouge_metric.compute()\n",
    "    rouge_dict = dict((rn, score[rn].mid.fmeasure) for rn in rouge_names)\n",
    "    records.append(rouge_dict)\n",
    "pd.DataFrame.from_records(records, index=summaries.keys())"
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