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        "# !pip install --upgrade pip\n",
        "# !pip install --disable-pip-version-check \\\n",
        "#     torch==1.13.1 \\\n",
        "#     torchdata==0.5.1 --quiet\n",
        "\n",
        "# !pip install \\\n",
        "#     transformers==4.27.2 \\\n",
        "#     datasets==2.11.0 \\\n",
        "#     evaluate==0.4.0 \\\n",
        "#     rouge_score==0.1.2 \\\n",
        "#     peft==0.3.0 --quiet\n",
        "\n",
        "# # Installing the Reinforcement Learning library directly from github.\n",
        "# !pip install git+https://github.com/lvwerra/trl.git@25fa1bd"
      ]
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      "source": [
        "#installing necessary libraries\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, GenerationConfig\n",
        "from datasets import load_dataset\n",
        "from peft import PeftModel, PeftConfig, LoraConfig, TaskType\n",
        "\n",
        "# trl: Transformer Reinforcement Learning library\n",
        "from trl import PPOTrainer, PPOConfig, AutoModelForSeq2SeqLMWithValueHead\n",
        "from trl import create_reference_model\n",
        "from trl.core import LengthSampler\n",
        "\n",
        "import torch\n",
        "import evaluate\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# tqdm library makes the loops show a smart progress meter.\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()"
      ],
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              "model_id": "d053de073e724d619c2da53a9f1eafce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8ba8424b665465da1016b7db33178fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-0ce315941ab394ef/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94e7fa3b61004d5d91c9a872f0fe46f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'output'],\n",
              "        num_rows: 178\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_name=\"google/flan-t5-base\""
      ],
      "metadata": {
        "id": "rSHLUbm8hp8V"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def trainable_model_parameters(model):\n",
        "    trainable_model_params = 0\n",
        "    all_model_params = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_model_params += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_model_params += param.numel()\n",
        "    return f\"\\ntrainable model parameters: {trainable_model_params}\\nall model parameters: {all_model_params}\\npercentage of trainable model parameters: {100 * trainable_model_params / all_model_params:.2f}%\""
      ],
      "metadata": {
        "id": "H9xI5lkGhuFK"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lora_config = LoraConfig(\n",
        "    r=32, # Rank\n",
        "    lora_alpha=32,\n",
        "    target_modules=[\"q\", \"v\"],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=TaskType.SEQ_2_SEQ_LM # FLAN-T5\n",
        ")\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_name,\n",
        "                                              torch_dtype=torch.bfloat16)\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(model,\n",
        "                                       'Sakil/flan-t5_fine_tuned_summarization_alpaca_updated_final',\n",
        "                                       lora_config=lora_config,\n",
        "                                       torch_dtype=torch.bfloat16,\n",
        "                                       device_map=\"auto\",\n",
        "                                       is_trainable=True)\n",
        "\n",
        "print(f'PEFT model parameters to be updated:\\n{trainable_model_parameters(peft_model)}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286,
          "referenced_widgets": [
            "49693b6619764e4186add40b2ad3bcf6",
            "f9171146002f4f2fbeddbcea514e1745",
            "0f91f6c0942e40b1b8a72a542ff39467",
            "7896259729a34ef3ab8c3b48674d32e0",
            "63c65fdc3f374e79a2620fd586357800",
            "08fa10c98f21423d8ec243b3dd3bebf0",
            "c7bfa88d0dc9425e93acf994fb91a47b",
            "30d24b4dd84648348a34b636b5a6fb6c",
            "6b424d0614b443cca02112abbcf858aa",
            "2a4abafc017346999caeaf6b60d34495",
            "53adbe94a1ee4983bd2853e6d8f0c5c2",
            "6a84716cd57e4ce48f5089a4851fd245",
            "8afd686b4c1545c4939c5294f68ec713",
            "0efc37d639554341bd724513e06e6160",
            "46d3972604ec47d2b008306f83c7b5c0",
            "170a2060f2504ac9a713394921212e6c",
            "ea1a134ac52b44c188e44a06088e29bb",
            "8e66cb6948234322a7af05a06c9b9942",
            "594d94f8e2dc44a4996678a2c2eedf98",
            "c22cb390b0a54549b1142721ada6e469",
            "e59e6974ddfe446f98386a04ab605ce5",
            "c1f251b0b46047c6933c5a58e7ce7934",
            "45437e596e8d4a92a59768bd6d0c4777",
            "307d66d01b614039abf0fff877b8d29c",
            "b7dc929e232e40c7afa73b582624ce44",
            "35149502f34a4db99d1d201897fc369a",
            "0a82d4482cb849ed893816e57f0f11be",
            "91a6fdc6847f4d2db4e51bca3669064f",
            "e6b2f0208aac44dd81d4f68e99df830a",
            "dc452359fb2c440bb04c462b652d809f",
            "92602b551e074d82afbb025be600cb71",
            "350906d5d25b4318b89a17e746ebc4ba",
            "1edfd5f05f064d31b262b79c6a5fdc58",
            "c8655e04d81e4947b51807e23507c681",
            "47af0e7fe3234f6da021708b8b8355ce",
            "068d1643d067440bb55abbb80f2098f5",
            "064f47ba689e492b8d1453d8384b78ad",
            "408688d4dfc74bcb932d3de33d6f4304",
            "0c0bd4e86de3482bb6d5ba699beba5fa",
            "e49c3acebd274687896dadc38f4d8d93",
            "ba4567d9d9844d93adfe04b25ecc9807",
            "47423da8a1fa47c982c96a85d902031d",
            "65d9381fd47046a58daf8401412a2802",
            "e72d13e14253436f8c7cf72eccb1feea",
            "94ec6eca33cc4542a96d08dc94b68d53",
            "e7af110dcfd242b5bc0ede939e877af1",
            "e2af8a1c1449456db8fe02f822fddf1b",
            "2073df6ccca0467eb4ef754407dd6da1",
            "52230163cf2346519e4d736655022933",
            "4c1da70188a9497284e022ff0a297904",
            "c9fde8b2b9f8425e8608a6b66b64eb0a",
            "ab5c26ff22e14093983b6641a2c4c2ac",
            "17f68766d8e6487e9f4c05ddcef67363",
            "fbcf38bc43364ffcb0af9860349ecb65",
            "95a6ea5e88124a89aba8fd9259bb33be"
          ]
        },
        "id": "08Wh9kojh8Am",
        "outputId": "c1a52b0b-aa25-4f87-fdcf-d1f660c3ef5d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49693b6619764e4186add40b2ad3bcf6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6a84716cd57e4ce48f5089a4851fd245"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45437e596e8d4a92a59768bd6d0c4777"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/334 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c8655e04d81e4947b51807e23507c681"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/14.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94ec6eca33cc4542a96d08dc94b68d53"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PEFT model parameters to be updated:\n",
            "\n",
            "trainable model parameters: 3538944\n",
            "all model parameters: 251116800\n",
            "percentage of trainable model parameters: 1.41%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ppo_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(peft_model,\n",
        "                                                               torch_dtype=torch.bfloat16,\n",
        "                                                               is_trainable=True)\n",
        "\n",
        "print(f'PPO model parameters to be updated (ValueHead + 769 params):\\n{trainable_model_parameters(ppo_model)}\\n')\n",
        "print(ppo_model.v_head)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCH4ymIii0qG",
        "outputId": "e37bd1ea-04c7-4794-c865-86e6833bce1a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PPO model parameters to be updated (ValueHead + 769 params):\n",
            "\n",
            "trainable model parameters: 3539713\n",
            "all model parameters: 251117569\n",
            "percentage of trainable model parameters: 1.41%\n",
            "\n",
            "ValueHead(\n",
            "  (dropout): Dropout(p=0.1, inplace=False)\n",
            "  (summary): Linear(in_features=768, out_features=1, bias=True)\n",
            "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ref_model = create_reference_model(ppo_model)\n",
        "\n",
        "print(f'Reference model parameters to be updated:\\n{trainable_model_parameters(ref_model)}\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EVM-83vjmCS",
        "outputId": "c3cd5338-6434-42bd-8e2b-508f43dd94c3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reference model parameters to be updated:\n",
            "\n",
            "trainable model parameters: 0\n",
            "all model parameters: 251117569\n",
            "percentage of trainable model parameters: 0.00%\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "toxicity_model_name = \"facebook/roberta-hate-speech-dynabench-r4-target\"\n",
        "toxicity_tokenizer = AutoTokenizer.from_pretrained(toxicity_model_name, device_map=\"auto\")\n",
        "toxicity_model = AutoModelForSequenceClassification.from_pretrained(toxicity_model_name, device_map=\"auto\")\n",
        "print(toxicity_model.config.id2label)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227,
          "referenced_widgets": [
            "f2df2b63d8484768a65f4f7dacf88e6f",
            "af1dd0e9d02c451898b7377dce0b9184",
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            "28342974d33b4acba0b0a2a5644d9957",
            "c0df5722ffa34c14b49cd2a0aae7fb76",
            "bed68fb177e14483a915e21092b4e475",
            "34d6d1f391324dd186e191b4e5ffd934",
            "8094fc3f170e47da8107fa952db25b1b",
            "be305af34c4e4a348e0e37abd8fde474",
            "32babb67d9824e8ab6a4363aa1928ad9",
            "a4343c9e3a754c889c19758cd19913e3",
            "482b3ac41a44459aa11a743303c37e3e",
            "d35895594f8b4d92a316c69f8c2993b4",
            "0f38183581694f179a6942f3a4ef4bf5",
            "3f6f57bc6b784a24a1c45b3e9d484f43",
            "2a82c568f1cf4d18ac380f4b7e6762a8",
            "d8108b17d22b4b83b96cd3429d2b97cc",
            "3b1660ae7af543fd88292a8c0c687074",
            "48e8e543f5684567b2b15189af42a9b9",
            "25ed5b508b01492da50439ca0ea36d44",
            "a82a330c88224f5a99e0fedf67a662e5",
            "7ffa2f62cfa94a86887818574b003e77",
            "41470f9849ee4d3383011756c8820e43",
            "48bb3ce1db8744d3b7bc0c02134b6aea",
            "9da2b9c2b0ad49c3af5d93b791c5ae19",
            "b68d5038e0a94603858cd6877528a935",
            "b9142aeda36e40f8af55caf33be8c593",
            "2417dae9ec7b4861a2bc4b0a69b12aa2",
            "7f7d503b1e744544b7ef9ebcc5b082e0",
            "f3c1771d51a445dbb6ac17abb86b682f",
            "c14264b455444a23a334289535fa5587",
            "43c8f64b7de6484caaeca3e7743168da",
            "34af488c2cdb4687bf73bd243d2b9fb2",
            "486f5a79c51143b6b8f6ad8e862dff91",
            "bf1720ce822b4e9b8a8138fd4bc3a8ba",
            "f44b189047a34e94bf0cb9e986f37419",
            "1678c2f5823e4b4c9feca02e62a09382",
            "53e8bbb75aa74eaf8faf1e64e752b155",
            "96a343a1af2b4c18b117083f3351aa79",
            "d5dbde8eb5cc42258e5170c0b2cd7e32",
            "0f3cdf293f554822b8e2cf2733c6663b",
            "b8bfdc7b54f04520bd6c47403256f032",
            "485965ce99fc44e3b493929ae731d69d",
            "e8d429354aa9421196762f538a730c66",
            "799a7fefe87743e4ae0e2014fd5bfdb3",
            "88e73f3a2f094300b1fa9143f0cc64f6",
            "e7213af6ebe64dc3b603b414bb5f2517",
            "f59e252851484a448f357a00cfa4c031",
            "57659f04736f4874a6ce6481575c829f",
            "a6ebd4c6ba854cafaccc3e335963a173",
            "770ef40cd937462db9afdab89bf29377",
            "803044bf5e8948f3bfb0dbbadf127ed7",
            "893f66a16aef4315b97040f722e10aa2",
            "4efe48fcb59a4ac2a2027da68a816fc0",
            "bf91c37d2a5c417eafff49bc2a70c32d",
            "fbf5c0a5302941fdb4b5863520851422",
            "7cf51f8e3c4b49ebb85e7592d001523a",
            "e17a3f0aca2145b5967c4f1e3ebb8448",
            "8e3a7c25ed8045549411dd5a17e0946e",
            "48332ebab0274fc59706c33842e842a2",
            "15759a59ea274411a38059070291a6f9",
            "fc151f71f8364c0482e47d546f5c7e6f",
            "754f15c5507f4ba09a1c37749bb8ed2e",
            "d8e67ddb25644b1b810846fffb5053d4",
            "6dd8689ab5834c028105d978b3e12530",
            "2c8bc41ed50848999f13456ef1fe7102"
          ]
        },
        "id": "QgjYL1X7kHRy",
        "outputId": "19314052-5d6f-49a9-9a33-2214acac41e9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2df2b63d8484768a65f4f7dacf88e6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "482b3ac41a44459aa11a743303c37e3e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41470f9849ee4d3383011756c8820e43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "486f5a79c51143b6b8f6ad8e862dff91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/816 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "799a7fefe87743e4ae0e2014fd5bfdb3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fbf5c0a5302941fdb4b5863520851422"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 'nothate', 1: 'hate'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def collator(data):\n",
        "    return dict((key, [d[key] for d in data]) for key in data[0])\n",
        "\n",
        "test_data = [{\"key1\": \"value1\", \"key2\": \"value2\", \"key3\": \"value3\"}]\n",
        "print(f'Collator input: {test_data}')\n",
        "print(f'Collator output: {collator(test_data)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXmYr034kgAD",
        "outputId": "2c008918-0767-4fdf-fae9-380046d5d1ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collator input: [{'key1': 'value1', 'key2': 'value2', 'key3': 'value3'}]\n",
            "Collator output: {'key1': ['value1'], 'key2': ['value2'], 'key3': ['value3']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_name, device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ee45cfa0696244be915f34d94ca42f85",
            "5c4204c087e849daaba5274d3d76521a",
            "cd8a4045fd9845a9bd9cb10290211b4f",
            "de416abfdb144ce5b2737b13f91fdc39",
            "e713ad829f2f4393afeeae88e9796df7",
            "5a915157463a4e03b669f58cc6eca41f",
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            "7e5db3bfe0c1460c937460dc63491fc1",
            "e9add9a7f7204ba69ed8a76253e0cba8"
          ]
        },
        "id": "ACivPgGRlho6",
        "outputId": "ef0b268d-65a1-48ab-a16b-16c3fc99f271"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ee45cfa0696244be915f34d94ca42f85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dce5439f50a04f949f2de990c86c3908"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "79a1b81b0bb94e3a999ed1958d4cc05c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f4dbb328f0f2411f89624fd1dfd85f3d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def build_dataset(model_name,\n",
        "                  dataset_name,\n",
        "                  input_min_text_length,\n",
        "                  input_max_text_length):\n",
        "\n",
        "    \"\"\"\n",
        "    Preprocess the dataset and split it into train and test parts.\n",
        "\n",
        "    Parameters:\n",
        "    - model_name (str): Tokenizer model name.\n",
        "    - dataset_name (str): Name of the dataset to load.\n",
        "    - input_min_text_length (int): Minimum length of the dialogues.\n",
        "    - input_max_text_length (int): Maximum length of the dialogues.\n",
        "\n",
        "    Returns:\n",
        "    - dataset_splits (datasets.dataset_dict.DatasetDict): Preprocessed dataset containing train and test parts.\n",
        "    \"\"\"\n",
        "\n",
        "    # load dataset (only \"train\" part will be enough for this lab).\n",
        "    dataset=load_dataset(\"csv\",data_files=\"/content/train_alpaca_dataset_summary.csv\",split=\"train\")\n",
        "    # dataset = load_dataset(dataset_name, split=\"train\")\n",
        "\n",
        "    # Filter the dialogues of length between input_min_text_length and input_max_text_length characters.\n",
        "    dataset = dataset.filter(lambda x: len(x[\"input\"]) > input_min_text_length and len(x[\"input\"]) <= input_max_text_length, batched=False)\n",
        "\n",
        "    # Prepare tokenizer. Setting device_map=\"auto\" allows to switch between GPU and CPU automatically.\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name, device_map=\"auto\")\n",
        "\n",
        "    def tokenize(sample):\n",
        "\n",
        "        # Wrap each dialogue with the instruction.\n",
        "        prompt = f\"\"\"\n",
        "Summarize the following conversation.\n",
        "\n",
        "{sample[\"input\"]}\n",
        "\n",
        "Summary:\n",
        "\"\"\"\n",
        "        sample[\"input_ids\"] = tokenizer.encode(prompt)\n",
        "\n",
        "        # This must be called \"query\", which is a requirement of our PPO library.\n",
        "        sample[\"query\"] = tokenizer.decode(sample[\"input_ids\"])\n",
        "        return sample\n",
        "\n",
        "    # Tokenize each dialogue.\n",
        "    dataset = dataset.map(tokenize, batched=False)\n",
        "    dataset.set_format(type=\"torch\")\n",
        "\n",
        "    # Split the dataset into train and test parts.\n",
        "    dataset_splits = dataset.train_test_split(test_size=0.2, shuffle=False, seed=42)\n",
        "\n",
        "    return dataset_splits"
      ],
      "metadata": {
        "id": "FWCS8Sgtl7lt"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = build_dataset(model_name=model_name,\n",
        "                        dataset_name='/content/train_alpaca_dataset_summary.csv',\n",
        "                        input_min_text_length=200,\n",
        "                        input_max_text_length=1000)\n",
        "\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238,
          "referenced_widgets": [
            "7af3e0fc035441e58ee31a1a86c50b56",
            "073f877c5d9e487386b9230e774a972e",
            "7d92c9a31ac742fbbdc7b5150b398a7e",
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            "46efb86debf24144b92eb85345f0c21a",
            "acc91a6dbc5c49839ba3a32366998176",
            "26fe6eb9983e41ca8bf34316b33f0098",
            "38282d42adaa4573bbb2b73df9fed2d8",
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            "29d19bce2c1c453ebaf56c9a8b20f8c3",
            "3582fc05f653416dbc0899cfcb6cc763",
            "8f7b78cdf09d46f7972c3d9b255432ad",
            "240a544c9f064e25a89cd18913cac231",
            "c1616b94c1234284bdb23f8a959b9079",
            "0d4d6c49b6c44c58b99a1f30ca415bba",
            "0b06334abca84104b953d111d2630e77",
            "b499a1a4b0774b1e82f65f95e140a17f",
            "a55abf8d65914b25b2a611ff4cb2d495",
            "a772dc70c7134cc1af171da6223bc348",
            "cd52b0d679c944b290391980ddfa261d"
          ]
        },
        "id": "_TwZLKIRmSyt",
        "outputId": "1e540e70-e423-47b7-9585-2799ab1d31ab"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/csv/default-0ce315941ab394ef/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/178 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7af3e0fc035441e58ee31a1a86c50b56"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/77 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29d19bce2c1c453ebaf56c9a8b20f8c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['input', 'output', 'input_ids', 'query'],\n",
            "        num_rows: 61\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['input', 'output', 'input_ids', 'query'],\n",
            "        num_rows: 16\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "non_toxic_text = \"I dont like the movie.\"\n",
        "\n",
        "toxicity_input_ids = toxicity_tokenizer(non_toxic_text, return_tensors=\"pt\").input_ids\n",
        "# Move the toxicity model to the same device as the input tensor\n",
        "toxicity_model.to(toxicity_input_ids.device)\n",
        "logits = toxicity_model(input_ids=toxicity_input_ids).logits\n",
        "print(f'logits [not hate, hate]: {logits.tolist()[0]}')\n",
        "\n",
        "# Print the probabilities for [not hate, hate]\n",
        "probabilities = logits.softmax(dim=-1).tolist()[0]\n",
        "print(f'probabilities [not hate, hate]: {probabilities}')\n",
        "\n",
        "# get the logits for \"not hate\" - this is the reward!\n",
        "not_hate_index = 0\n",
        "nothate_reward = (logits[:, not_hate_index]).tolist()\n",
        "print(f'reward (high): {nothate_reward}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p1e15fk4oDzM",
        "outputId": "bf52db41-d27e-421c-f7f1-ceaabca2ab51"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "logits [not hate, hate]: [4.681546688079834, -4.2141923904418945]\n",
            "probabilities [not hate, hate]: [0.9998630285263062, 0.0001369526144117117]\n",
            "reward (high): [4.681546688079834]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "toxic_text = \"Today is very bad weather in Bangalore,terrible\"\n",
        "\n",
        "toxicity_input_ids = toxicity_tokenizer(toxic_text, return_tensors=\"pt\").input_ids\n",
        "\n",
        "logits = toxicity_model(toxicity_input_ids).logits\n",
        "print(f'logits [not hate, hate]: {logits.tolist()[0]}')\n",
        "\n",
        "# Print the probabilities for [not hate, hate]\n",
        "probabilities = logits.softmax(dim=-1).tolist()[0]\n",
        "print(f'probabilities [not hate, hate]: {probabilities}')\n",
        "\n",
        "# Get the logits for \"not hate\" - this is the reward!\n",
        "nothate_reward = (logits[:, not_hate_index]).tolist()\n",
        "print(f'reward (low): {nothate_reward}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0HVAbFToJ3D",
        "outputId": "9e7492a4-504b-42cd-eb12-6903360fece9"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "logits [not hate, hate]: [4.636322498321533, -4.2341766357421875]\n",
            "probabilities [not hate, hate]: [0.9998595714569092, 0.00014045278658159077]\n",
            "reward (low): [4.636322498321533]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = 0 if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "sentiment_pipe = pipeline(\"sentiment-analysis\",\n",
        "                          model=toxicity_model_name,\n",
        "                          device=device)\n",
        "reward_logits_kwargs = {\n",
        "    \"top_k\": None, # Return all scores.\n",
        "    \"function_to_apply\": \"none\", # Set to \"none\" to retrieve raw logits.\n",
        "    \"batch_size\": 16\n",
        "}\n",
        "\n",
        "reward_probabilities_kwargs = {\n",
        "    \"top_k\": None, # Return all scores.\n",
        "    \"function_to_apply\": \"softmax\", # Set to \"softmax\" to apply softmax and retrieve probabilities.\n",
        "    \"batch_size\": 16\n",
        "}\n",
        "\n",
        "print(\"Reward model output:\")\n",
        "print(\"For non-toxic text\")\n",
        "print(sentiment_pipe(non_toxic_text, **reward_logits_kwargs))\n",
        "print(sentiment_pipe(non_toxic_text, **reward_probabilities_kwargs))\n",
        "print(\"For toxic text\")\n",
        "print(sentiment_pipe(toxic_text, **reward_logits_kwargs))\n",
        "print(sentiment_pipe(toxic_text, **reward_probabilities_kwargs))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgcTTuP1n-_C",
        "outputId": "e2c2fac9-740c-4d29-a89f-e4a4a6d33503"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reward model output:\n",
            "For non-toxic text\n",
            "[{'label': 'nothate', 'score': 4.681546688079834}, {'label': 'hate', 'score': -4.214191436767578}]\n",
            "[{'label': 'nothate', 'score': 0.9998630285263062}, {'label': 'hate', 'score': 0.00013695262896362692}]\n",
            "For toxic text\n",
            "[{'label': 'nothate', 'score': 4.636322498321533}, {'label': 'hate', 'score': -4.234176158905029}]\n",
            "[{'label': 'nothate', 'score': 0.9998595714569092}, {'label': 'hate', 'score': 0.000140452801133506}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate=1.41e-5\n",
        "max_ppo_epochs=1\n",
        "mini_batch_size=4\n",
        "batch_size=16\n",
        "\n",
        "config = PPOConfig(\n",
        "    model_name=model_name,\n",
        "    learning_rate=learning_rate,\n",
        "    ppo_epochs=max_ppo_epochs,\n",
        "    mini_batch_size=mini_batch_size,\n",
        "    batch_size=batch_size\n",
        ")\n",
        "\n",
        "ppo_trainer = PPOTrainer(config=config,\n",
        "                         model=ppo_model,\n",
        "                         ref_model=ref_model,\n",
        "                         tokenizer=tokenizer,\n",
        "                         dataset=dataset[\"train\"],\n",
        "                         data_collator=collator)"
      ],
      "metadata": {
        "id": "HbvEEQaPmZgX"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output_min_length = 100\n",
        "output_max_length = 400\n",
        "output_length_sampler = LengthSampler(output_min_length, output_max_length)\n",
        "\n",
        "generation_kwargs = {\n",
        "    \"min_length\": 5,\n",
        "    \"top_k\": 0.0,\n",
        "    \"top_p\": 1.0,\n",
        "    \"do_sample\": True\n",
        "}\n",
        "\n",
        "reward_kwargs = {\n",
        "    \"top_k\": None, # Return all scores.\n",
        "    \"function_to_apply\": \"none\", # You want the raw logits without softmax.\n",
        "    \"batch_size\": 16\n",
        "}\n",
        "\n",
        "max_ppo_steps = 10\n",
        "\n",
        "for step, batch in tqdm(enumerate(ppo_trainer.dataloader)):\n",
        "    # Break when you reach max_steps.\n",
        "    if step >= max_ppo_steps:\n",
        "        break\n",
        "\n",
        "    prompt_tensors = batch[\"input_ids\"]\n",
        "\n",
        "    # Get response from FLAN-T5/PEFT LLM.\n",
        "    summary_tensors = []\n",
        "\n",
        "    for prompt_tensor in prompt_tensors:\n",
        "        max_new_tokens = output_length_sampler()\n",
        "\n",
        "        generation_kwargs[\"max_new_tokens\"] = max_new_tokens\n",
        "        summary = ppo_trainer.generate(prompt_tensor, **generation_kwargs)\n",
        "\n",
        "        summary_tensors.append(summary.squeeze()[-max_new_tokens:])\n",
        "\n",
        "    # This needs to be called \"response\".\n",
        "    batch[\"response\"] = [tokenizer.decode(r.squeeze()) for r in summary_tensors]\n",
        "\n",
        "    # Compute reward outputs.\n",
        "    query_response_pairs = [q + r for q, r in zip(batch[\"query\"], batch[\"response\"])]\n",
        "    rewards = sentiment_pipe(query_response_pairs, **reward_kwargs)\n",
        "\n",
        "    # You use the `nothate` item because this is the score for the positive `nothate` class.\n",
        "    reward_tensors = [torch.tensor(reward[not_hate_index][\"score\"]) for reward in rewards]\n",
        "\n",
        "    # Run PPO step.\n",
        "    stats = ppo_trainer.step(prompt_tensors, summary_tensors, reward_tensors)\n",
        "    ppo_trainer.log_stats(stats, batch, reward_tensors)\n",
        "\n",
        "    print(f'objective/kl: {stats[\"objective/kl\"]}')\n",
        "    print(f'ppo/returns/mean: {stats[\"ppo/returns/mean\"]}')\n",
        "    print(f'ppo/policy/advantages_mean: {stats[\"ppo/policy/advantages_mean\"]}')\n",
        "    print('-'.join('' for x in range(100)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83Nr-G2Zm7TT",
        "outputId": "7cfae39d-d2ed-4853-83ad-4aff9cf74daa"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "1it [00:13, 13.62s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "objective/kl: -0.01259386446326971\n",
            "ppo/returns/mean: 2.3082876205444336\n",
            "ppo/policy/advantages_mean: 1.64875416430732e-07\n",
            "---------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\r2it [00:25, 12.64s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "objective/kl: -0.01883194036781788\n",
            "ppo/returns/mean: 2.2919187545776367\n",
            "ppo/policy/advantages_mean: -8.187188171859816e-08\n",
            "---------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "3it [00:40, 13.59s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "objective/kl: 0.03665304183959961\n",
            "ppo/returns/mean: 2.6652402877807617\n",
            "ppo/policy/advantages_mean: -1.0882768464171022e-08\n",
            "---------------------------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inferencing"
      ],
      "metadata": {
        "id": "XbdtqqdBqRIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 16\n",
        "compare_results = {}\n",
        "\n",
        "df_batch = dataset[\"test\"][0:batch_size]\n",
        "\n",
        "compare_results[\"query\"] = df_batch[\"query\"]\n",
        "prompt_tensors = df_batch[\"input_ids\"]\n",
        "\n",
        "summary_tensors_ref = []\n",
        "summary_tensors = []\n",
        "\n",
        "# Get response from ppo and base model.\n",
        "for i in tqdm(range(batch_size)):\n",
        "    gen_len = output_length_sampler()\n",
        "    generation_kwargs[\"max_new_tokens\"] = gen_len\n",
        "\n",
        "    summary = ref_model.generate(\n",
        "        input_ids=torch.as_tensor(prompt_tensors[i]).unsqueeze(dim=0).to(device),\n",
        "        **generation_kwargs\n",
        "    ).squeeze()[-gen_len:]\n",
        "    summary_tensors_ref.append(summary)\n",
        "\n",
        "    summary = ppo_model.generate(\n",
        "        input_ids=torch.as_tensor(prompt_tensors[i]).unsqueeze(dim=0).to(device),\n",
        "        **generation_kwargs\n",
        "    ).squeeze()[-gen_len:]\n",
        "    summary_tensors.append(summary)\n",
        "\n",
        "# Decode responses.\n",
        "compare_results[\"response_before\"] = [tokenizer.decode(summary_tensors_ref[i]) for i in range(batch_size)]\n",
        "compare_results[\"response_after\"] = [tokenizer.decode(summary_tensors[i]) for i in range(batch_size)]\n",
        "\n",
        "# Sentiment analysis of query/response pairs before/after.\n",
        "texts_before = [d + s for d, s in zip(compare_results[\"query\"], compare_results[\"response_before\"])]\n",
        "rewards_before = sentiment_pipe(texts_before, **reward_kwargs)\n",
        "compare_results[\"reward_before\"] = [reward[not_hate_index][\"score\"] for reward in rewards_before]\n",
        "\n",
        "texts_after = [d + s for d, s in zip(compare_results[\"query\"], compare_results[\"response_after\"])]\n",
        "rewards_after = sentiment_pipe(texts_after, **reward_kwargs)\n",
        "compare_results[\"reward_after\"] = [reward[not_hate_index][\"score\"] for reward in rewards_after]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHJ5VM1knrO3",
        "outputId": "35b9bfc3-ae01-40fa-d53e-c77ceb42401f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 16/16 [00:27<00:00,  1.72s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_colwidth', 500)\n",
        "df_compare_results = pd.DataFrame(compare_results)\n",
        "df_compare_results[\"reward_diff\"] = df_compare_results['reward_after'] - df_compare_results['reward_before']\n",
        "df_compare_results_sorted = df_compare_results.sort_values(by=['reward_diff'], ascending=False).reset_index(drop=True)\n"
      ],
      "metadata": {
        "id": "enwvyC9FqXCt"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_compare_results_sorted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nzk9xon6qxiC",
        "outputId": "43d5139c-bb1a-44ef-a763-6a4a839de42c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  query  \\\n",
              "0   Summarize the following conversation. The internet has drastically altered our lives in a variety of ways, drastically changing the ways we do business, interact with others, and consume media. The internet has made it so that communication can be instant across distances large and small, while e-commerce and online banking allow us to make purchases and payments quicker, easier, and more securely than ever before. It has given voice to formerly marginalized communities and provided access t...   \n",
              "1                                                                                                                                                                                               Summarize the following conversation. Many forms of music have roots in folk styles. These include a variety of forms from different countries and regions, including bluegrass from the American South, Irish traditional music, Indian classical music and klezmer from Eastern Europe, amongst others. Summary: </s>   \n",
              "2                                                                                                                                                                                                                                          Summarize the following conversation. The United States of America is located in North America, between Canada and Mexico. It is the world’s third-largest country, after Russia and China. The United States has 50 states and the capital is Washington, DC. Summary: </s>   \n",
              "3                                                                                                                                                                                                                            Summarize the following conversation. The flu is an infection caused by the influenza virus. Symptoms of the flu include fever, sore throat, and body aches. To prevent the spread of the virus, people should practice good hygiene, such as washing their hands regularly. Summary: </s>   \n",
              "4   Summarize the following conversation. Climate change is becoming an increasing concern across the world as temperatures continue to rise and record levels of extreme weather events are recorded. This is due to the human action of creating greenhouse gas emissions such as carbon dioxide, which trap heat and act like a blanket around the Earth’s atmosphere. The effects of climate change are already being witnessed, from floods to droughts, and are expected to even worsen if the climate is allo...   \n",
              "5                                                                                                                                            Summarize the following conversation. This song is about the importance of facing our fears and insecurities. It is an uplifting anthem that encourages us to be strong and persevere even in the face of adversity. It also provides insight into the things that hold us back and how understanding ourselves can empower us to overcome these challenges. Summary: </s>   \n",
              "6                                                                                Summarize the following conversation. This chapter discusses an approach to online evaluation of natural language interaction systems based on procedures automatically generated from domain models. It focuses on the critical problems of automatically generating correct evaluation tests and providing meaningful scores for the evaluation of natural language understanding and dialogue management performance. Summary: </s>   \n",
              "7                                                                                                                                                                                                                                      Summarize the following conversation. Robotics is a branch of engineering and science that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing. Summary: </s>   \n",
              "8                                                                                                       Summarize the following conversation. Once upon a time, there was a brave prince who set off on a journey to find a magical sword that had the power to grant him any wish. He encountered many dangers along the way but eventually, with the help of a wise wizard, he was able to find the sword and make his wish come true. He became the ruler of the kingdom and lived happily ever after. Summary: </s>   \n",
              "9                                                                                                                                                                                         Summarize the following conversation. The paper, titled \"Exploring Voice-Based Human-Robot Interaction in Non-Technical Domains\" describes the development of an interactive AI system that enables natural language-based human-robot interaction, with a focus on providing robotic assistance to homeowners. Summary: </s>   \n",
              "10                                                      Summarize the following conversation. Climate change is one of the most pressing and urgent issues of our time. The effects of global warming are already being felt around the world, with rising sea levels, extreme weather events, and other impacts. Reducing our use of fossil fuels is the most significant way to address climate change, and the good news is that it is possible to do this while still maintaining a modern lifestyle. Summary: </s>   \n",
              "11                                                                                                                                                                                                                              Summarize the following conversation. Summary: In this novel, an elderly man revisits his past and reflects on a childhood filled with love and loss. His journey brings him closer to understanding his relationships and finding closure as he moves closer to his end. Summary: </s>   \n",
              "12                                                                                                                                                                             Summarize the following conversation. Basketball is a team sport in which two teams, usually consisting of five players on each team, play against each other on a rectangular court. The objective is to score by shooting the ball through a hoop 18 inches in diameter affixed to a backboard at each end of the court. Summary: </s>   \n",
              "13                    Summarize the following conversation. In recent years, renewable energy sources like solar, water and wind have become increasingly popular in many countries around the world. As the cost of solar panels, wind turbines and other technologies falls, more and more households and businesses are turning to these sustainable, clean sources of energy. In the United States, the number of residential and commercial solar installations has more than quadrupled since 2010. Summary: </s>   \n",
              "14                                                                                                                                                 Summarize the following conversation. The wonderful world of Web accessibility As the web grows, accessible web content becomes more important. Accessible websites have a better user experience and are beneficial both to developers and users. They allow everyone, regardless of disability, to get improved access to information and resources. Summary: </s>   \n",
              "15                                                                                                                                                                                       Summarize the following conversation. The blockchain technology is a breakthrough phenomenon that offers a degree of digital trust. It is a distributed ledger with encrypted data that operates independently on a peer-to-peer network. It uses computer consensus to validate and secure digital information. Summary: </s>   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                      response_before  \\\n",
              "0                                                                                                                                                                                                                                                                              <pad> Choose a tool to use in lowering another’s income; range in type. Add individual items, cards, calendars, etc. Add sophisticated icons and images. Add storytelling into existing processes.</s>   \n",
              "1   <pad> Tell the history of music by naming a multiculturalism or local folk or culture. Inspect these naming approaches, depending on how different kinds of music musical products fit in with local dialects. Examples include pop, country and Rockwith dancing. Compose and write music amongst folk or contemporary styles that speak to your own specific cultural traditions. It can be confusing to select such dominant musical styles based on cultural differences.</s>   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                                                          <pad> Visit a U.S. country. Travel around the country.</s>   \n",
              "3                                                                                                                                                                                                                                                                                                                                                                                                                   <pad> Check for symptoms of the flu. Clean up any infections.</s>   \n",
              "4                                                                                                                                                                                                                                                                                                                                       <pad> Research has shown that climate change continues to increase, and there are now new ways of making positive changes to our climate.</s>   \n",
              "5                                                                                                                                                                                                                                                                                                                                                                                                                   <pad> Write emojis on your desktop at home and on your phone.</s>   \n",
              "6                                                                                                                                                                                                                                                                                                                                                         <pad> Product evaluation via a user-derived online evaluation solution supports a variety of evaluation arts and needs.</s>   \n",
              "7                                                                                                                                                                                                                                                                                                                                                                                                                           <pad> Popular online flashcard list What is robotics?</s>   \n",
              "8                                                                                                                                                                                                                                                                         <pad> Explain how they gained power. Explain the story. Discuss throughe the story how the prince placed his wish. Discuss how he became the new ruler. Describefailure and the plight of Queen Akhtar.</s>   \n",
              "9                                                                                                                                                                                                                                                                                                           <pad> A speech-to-robot technology that mimics microcomputer interaction is being developed in Australian and New Zealand to provide automated diagnostics for users.</s>   \n",
              "10                                                                                                                                                                                                                                                                                                                                                              <pad> Listen to the Science Channel on the climate change front. Download Climate Change: Reducing our footprint.</s>   \n",
              "11                                                                                                                                                                                                                       <pad> With an era that makes Barbour famous and a man who embraces his mortality, the detective takes an insight and re-examines his past, giving effortless insights into his life and how he moved up from a seemingly poor future career opportunity.</s>   \n",
              "12                                                                                                                                                                                                                                                                                                                                                                                                                                                     <pad> Understand the game.</s>   \n",
              "13                                                                                                                                                                                                                                                                                                                                                                                                                      <pad> Report an increase in the bulk of renewable energy.</s>   \n",
              "14                                                                                                                                                                                                                                                                                                                                                                                                                        <pad> Learn how websites can be accessible to everyone.</s>   \n",
              "15                                                                                                                                                                                                                                                                                                                                                                                                                      <pad> Research the various uses of blockchain technology.</s>   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                            response_after  \\\n",
              "0                                                                                                                                                                                                                                                                                                                                                         <pad> Understand why the internet has no place in our lives.</s>   \n",
              "1                                                                                                                                                                                                                                                                                                                                                                         <pad> Learn about folk and other folk music.</s>   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                       <pad> Learn about the country.</s>   \n",
              "3                                                                                                                                                                                                                                                                                                                                                                            <pad> Understand the symptoms of the flu.</s>   \n",
              "4                                                                                                                                                                                                                                                       <pad> In the last seven years, dark forests using energy production and forests retreating, climate change has widened, with 360% growth over the past decade.</s>   \n",
              "5                                                                                                                                                                                                                                                                                                                                                 <pad> Make a \"Go-Go\" playlist, or look for a \"Spring Rock\" playlist.</s>   \n",
              "6   <pad> The Suite Petr Postver Evaluative Processing Language so-called harmonization system blackboxes can provide coded, text-aloud technology that can accurately evaluate and analyze a broad range of discourse recognition systems past today and tomorrow. This software has been an exemplary tool for writing, testing, and evaluating natural language (RL) interaction systems for virtually anyone else.</s>   \n",
              "7                                                                                                                                                                                                                                                                                                                                        <pad> Describe what robotics is and what it will be able to do in the future.</s>   \n",
              "8                                                                                                                                                                                                                                                                                                                                                                                    <pad> Begin by telling the story.</s>   \n",
              "9                                                                                                                                                                                                <pad> Dedicated to creating companionship and convenience, MultilogicalRobotics has developed a system on Artificial-Robot Interaction (ABI) in an effort to facilitate robotic cooperation in remediation processes.</s>   \n",
              "10                                                                                                                                                                                                                                                                                                                                                           <pad> What causes climate change do you want to do first?</s>   \n",
              "11                                                                                                                                                                                                                                                          <pad> This tender and moving tale of an elderly man in a wheelchair helps children move between their memories of their childhood and of their loved ones.</s>   \n",
              "12                                                                                                                                                                                                                                                                                            <pad> Identify the game. Understand the rules and procedures of basketball. Butaken how to win, stay ready for the game.</s>   \n",
              "13                                                                                                                                                                                                                                                                                                                                            <pad> Understand the high rises and currents of energy use in the world.</s>   \n",
              "14                                                                                                                                                                                                                                                                               <pad> Focus on your web accessibility needs. Address writer's block and accessibility Issues. Make use of accessibility improvements.</s>   \n",
              "15                                                                                                                                                                                                                <pad> Cooperate with changing legislation related to blockchain technology. Consider if blockchain addresses a financial problem. Help retool blockchain offerings to address the sellers’ concerns.</s>   \n",
              "\n",
              "    reward_before  reward_after  reward_diff  \n",
              "0        2.744171      3.653286     0.909115  \n",
              "1        3.560520      4.170738     0.610217  \n",
              "2        2.319474      2.791952     0.472479  \n",
              "3        3.405120      3.785821     0.380700  \n",
              "4        3.888508      4.180700     0.292192  \n",
              "5        2.940205      3.205351     0.265146  \n",
              "6        3.661752      3.831214     0.169462  \n",
              "7        4.406735      4.557851     0.151116  \n",
              "8        3.061244      3.149960     0.088716  \n",
              "9        3.483452      3.564383     0.080930  \n",
              "10       3.575162      3.551616    -0.023546  \n",
              "11       2.769010      2.654567    -0.114443  \n",
              "12       3.497271      3.280088    -0.217183  \n",
              "13       4.619591      4.352254    -0.267337  \n",
              "14       4.400838      3.828110    -0.572728  \n",
              "15       4.276215      3.680588    -0.595627  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-8cdc2529-3f22-4a9e-926a-f4699e43c1ee\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>query</th>\n",
              "      <th>response_before</th>\n",
              "      <th>response_after</th>\n",
              "      <th>reward_before</th>\n",
              "      <th>reward_after</th>\n",
              "      <th>reward_diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Summarize the following conversation. The internet has drastically altered our lives in a variety of ways, drastically changing the ways we do business, interact with others, and consume media. The internet has made it so that communication can be instant across distances large and small, while e-commerce and online banking allow us to make purchases and payments quicker, easier, and more securely than ever before. It has given voice to formerly marginalized communities and provided access t...</td>\n",
              "      <td>&lt;pad&gt; Choose a tool to use in lowering another’s income; range in type. Add individual items, cards, calendars, etc. Add sophisticated icons and images. Add storytelling into existing processes.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Understand why the internet has no place in our lives.&lt;/s&gt;</td>\n",
              "      <td>2.744171</td>\n",
              "      <td>3.653286</td>\n",
              "      <td>0.909115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Summarize the following conversation. Many forms of music have roots in folk styles. These include a variety of forms from different countries and regions, including bluegrass from the American South, Irish traditional music, Indian classical music and klezmer from Eastern Europe, amongst others. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Tell the history of music by naming a multiculturalism or local folk or culture. Inspect these naming approaches, depending on how different kinds of music musical products fit in with local dialects. Examples include pop, country and Rockwith dancing. Compose and write music amongst folk or contemporary styles that speak to your own specific cultural traditions. It can be confusing to select such dominant musical styles based on cultural differences.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Learn about folk and other folk music.&lt;/s&gt;</td>\n",
              "      <td>3.560520</td>\n",
              "      <td>4.170738</td>\n",
              "      <td>0.610217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Summarize the following conversation. The United States of America is located in North America, between Canada and Mexico. It is the world’s third-largest country, after Russia and China. The United States has 50 states and the capital is Washington, DC. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Visit a U.S. country. Travel around the country.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Learn about the country.&lt;/s&gt;</td>\n",
              "      <td>2.319474</td>\n",
              "      <td>2.791952</td>\n",
              "      <td>0.472479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Summarize the following conversation. The flu is an infection caused by the influenza virus. Symptoms of the flu include fever, sore throat, and body aches. To prevent the spread of the virus, people should practice good hygiene, such as washing their hands regularly. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Check for symptoms of the flu. Clean up any infections.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Understand the symptoms of the flu.&lt;/s&gt;</td>\n",
              "      <td>3.405120</td>\n",
              "      <td>3.785821</td>\n",
              "      <td>0.380700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Summarize the following conversation. Climate change is becoming an increasing concern across the world as temperatures continue to rise and record levels of extreme weather events are recorded. This is due to the human action of creating greenhouse gas emissions such as carbon dioxide, which trap heat and act like a blanket around the Earth’s atmosphere. The effects of climate change are already being witnessed, from floods to droughts, and are expected to even worsen if the climate is allo...</td>\n",
              "      <td>&lt;pad&gt; Research has shown that climate change continues to increase, and there are now new ways of making positive changes to our climate.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; In the last seven years, dark forests using energy production and forests retreating, climate change has widened, with 360% growth over the past decade.&lt;/s&gt;</td>\n",
              "      <td>3.888508</td>\n",
              "      <td>4.180700</td>\n",
              "      <td>0.292192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Summarize the following conversation. This song is about the importance of facing our fears and insecurities. It is an uplifting anthem that encourages us to be strong and persevere even in the face of adversity. It also provides insight into the things that hold us back and how understanding ourselves can empower us to overcome these challenges. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Write emojis on your desktop at home and on your phone.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Make a \"Go-Go\" playlist, or look for a \"Spring Rock\" playlist.&lt;/s&gt;</td>\n",
              "      <td>2.940205</td>\n",
              "      <td>3.205351</td>\n",
              "      <td>0.265146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Summarize the following conversation. This chapter discusses an approach to online evaluation of natural language interaction systems based on procedures automatically generated from domain models. It focuses on the critical problems of automatically generating correct evaluation tests and providing meaningful scores for the evaluation of natural language understanding and dialogue management performance. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Product evaluation via a user-derived online evaluation solution supports a variety of evaluation arts and needs.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; The Suite Petr Postver Evaluative Processing Language so-called harmonization system blackboxes can provide coded, text-aloud technology that can accurately evaluate and analyze a broad range of discourse recognition systems past today and tomorrow. This software has been an exemplary tool for writing, testing, and evaluating natural language (RL) interaction systems for virtually anyone else.&lt;/s&gt;</td>\n",
              "      <td>3.661752</td>\n",
              "      <td>3.831214</td>\n",
              "      <td>0.169462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Summarize the following conversation. Robotics is a branch of engineering and science that deals with the design, construction, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Popular online flashcard list What is robotics?&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Describe what robotics is and what it will be able to do in the future.&lt;/s&gt;</td>\n",
              "      <td>4.406735</td>\n",
              "      <td>4.557851</td>\n",
              "      <td>0.151116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Summarize the following conversation. Once upon a time, there was a brave prince who set off on a journey to find a magical sword that had the power to grant him any wish. He encountered many dangers along the way but eventually, with the help of a wise wizard, he was able to find the sword and make his wish come true. He became the ruler of the kingdom and lived happily ever after. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Explain how they gained power. Explain the story. Discuss throughe the story how the prince placed his wish. Discuss how he became the new ruler. Describefailure and the plight of Queen Akhtar.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Begin by telling the story.&lt;/s&gt;</td>\n",
              "      <td>3.061244</td>\n",
              "      <td>3.149960</td>\n",
              "      <td>0.088716</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Summarize the following conversation. The paper, titled \"Exploring Voice-Based Human-Robot Interaction in Non-Technical Domains\" describes the development of an interactive AI system that enables natural language-based human-robot interaction, with a focus on providing robotic assistance to homeowners. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; A speech-to-robot technology that mimics microcomputer interaction is being developed in Australian and New Zealand to provide automated diagnostics for users.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Dedicated to creating companionship and convenience, MultilogicalRobotics has developed a system on Artificial-Robot Interaction (ABI) in an effort to facilitate robotic cooperation in remediation processes.&lt;/s&gt;</td>\n",
              "      <td>3.483452</td>\n",
              "      <td>3.564383</td>\n",
              "      <td>0.080930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Summarize the following conversation. Climate change is one of the most pressing and urgent issues of our time. The effects of global warming are already being felt around the world, with rising sea levels, extreme weather events, and other impacts. Reducing our use of fossil fuels is the most significant way to address climate change, and the good news is that it is possible to do this while still maintaining a modern lifestyle. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Listen to the Science Channel on the climate change front. Download Climate Change: Reducing our footprint.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; What causes climate change do you want to do first?&lt;/s&gt;</td>\n",
              "      <td>3.575162</td>\n",
              "      <td>3.551616</td>\n",
              "      <td>-0.023546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Summarize the following conversation. Summary: In this novel, an elderly man revisits his past and reflects on a childhood filled with love and loss. His journey brings him closer to understanding his relationships and finding closure as he moves closer to his end. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; With an era that makes Barbour famous and a man who embraces his mortality, the detective takes an insight and re-examines his past, giving effortless insights into his life and how he moved up from a seemingly poor future career opportunity.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; This tender and moving tale of an elderly man in a wheelchair helps children move between their memories of their childhood and of their loved ones.&lt;/s&gt;</td>\n",
              "      <td>2.769010</td>\n",
              "      <td>2.654567</td>\n",
              "      <td>-0.114443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Summarize the following conversation. Basketball is a team sport in which two teams, usually consisting of five players on each team, play against each other on a rectangular court. The objective is to score by shooting the ball through a hoop 18 inches in diameter affixed to a backboard at each end of the court. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Understand the game.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Identify the game. Understand the rules and procedures of basketball. Butaken how to win, stay ready for the game.&lt;/s&gt;</td>\n",
              "      <td>3.497271</td>\n",
              "      <td>3.280088</td>\n",
              "      <td>-0.217183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Summarize the following conversation. In recent years, renewable energy sources like solar, water and wind have become increasingly popular in many countries around the world. As the cost of solar panels, wind turbines and other technologies falls, more and more households and businesses are turning to these sustainable, clean sources of energy. In the United States, the number of residential and commercial solar installations has more than quadrupled since 2010. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Report an increase in the bulk of renewable energy.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Understand the high rises and currents of energy use in the world.&lt;/s&gt;</td>\n",
              "      <td>4.619591</td>\n",
              "      <td>4.352254</td>\n",
              "      <td>-0.267337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Summarize the following conversation. The wonderful world of Web accessibility As the web grows, accessible web content becomes more important. Accessible websites have a better user experience and are beneficial both to developers and users. They allow everyone, regardless of disability, to get improved access to information and resources. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Learn how websites can be accessible to everyone.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Focus on your web accessibility needs. Address writer's block and accessibility Issues. Make use of accessibility improvements.&lt;/s&gt;</td>\n",
              "      <td>4.400838</td>\n",
              "      <td>3.828110</td>\n",
              "      <td>-0.572728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Summarize the following conversation. The blockchain technology is a breakthrough phenomenon that offers a degree of digital trust. It is a distributed ledger with encrypted data that operates independently on a peer-to-peer network. It uses computer consensus to validate and secure digital information. Summary: &lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Research the various uses of blockchain technology.&lt;/s&gt;</td>\n",
              "      <td>&lt;pad&gt; Cooperate with changing legislation related to blockchain technology. Consider if blockchain addresses a financial problem. Help retool blockchain offerings to address the sellers’ concerns.&lt;/s&gt;</td>\n",
              "      <td>4.276215</td>\n",
              "      <td>3.680588</td>\n",
              "      <td>-0.595627</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8cdc2529-3f22-4a9e-926a-f4699e43c1ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-d4617167-b856-43df-a7ec-d0b1a27d8447\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4617167-b856-43df-a7ec-d0b1a27d8447')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d4617167-b856-43df-a7ec-d0b1a27d8447 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8cdc2529-3f22-4a9e-926a-f4699e43c1ee button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8cdc2529-3f22-4a9e-926a-f4699e43c1ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_dD8U1_gq0Ee"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}