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            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.9/dist-packages (from torchvision->sentence-transformers) (8.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2022.12.7)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->huggingface-hub>=0.4.0->sentence-transformers) (2.0.12)\n",
            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125942 sha256=cae61a0f7dcfc8d3ff6b2aa4daa34d450b5b976af56d5b861480235d41758ba4\n",
            "  Stored in directory: /root/.cache/pip/wheels/71/67/06/162a3760c40d74dd40bc855d527008d26341c2b0ecf3e8e11f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.13.2 sentence-transformers-2.2.2 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.27.1\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "!pip install sentence-transformers\n",
        "from sentence_transformers import SentenceTransformer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/augmented_predictions_all.csv')"
      ],
      "metadata": {
        "id": "yibg59zUCX9l"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sum(df['augmented_predictions'].str.contains(\"\\u00a0\"))"
      ],
      "metadata": {
        "id": "aAb1jVdUChHy"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sum(df['augmented_predictions'].str.contains(\"\\xa0\"))"
      ],
      "metadata": {
        "id": "eEjGq4YwCrjv"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# sum(df['augmented_predictions'].str.contains(\"\\n\"))"
      ],
      "metadata": {
        "id": "pdU25_DpCu0x"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%pip install --upgrade pip  # ensures that pip is current\n",
        "!git clone https://github.com/google-research/bleurt.git\n",
        "%cd bleurt\n",
        "%pip install ."
      ],
      "metadata": {
        "id": "LUPwAb61Cxvv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10104b77-754a-43e3-b1cf-075ee5aea493"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pip in /usr/local/lib/python3.9/dist-packages (22.0.4)\n",
            "Collecting pip\n",
            "  Downloading pip-23.0.1-py3-none-any.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m37.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: pip\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 22.0.4\n",
            "    Uninstalling pip-22.0.4:\n",
            "      Successfully uninstalled pip-22.0.4\n",
            "Successfully installed pip-23.0.1\n",
            "Cloning into 'bleurt'...\n",
            "remote: Enumerating objects: 134, done.\u001b[K\n",
            "remote: Counting objects: 100% (18/18), done.\u001b[K\n",
            "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
            "remote: Total 134 (delta 0), reused 17 (delta 0), pack-reused 116\u001b[K\n",
            "Receiving objects: 100% (134/134), 31.28 MiB | 18.03 MiB/s, done.\n",
            "Resolving deltas: 100% (49/49), done.\n",
            "/content/bleurt\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Processing /content/bleurt\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from BLEURT==0.0.2) (1.4.4)\n",
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            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.9/dist-packages (from BLEURT==0.0.2) (2.11.0)\n",
            "Collecting tf-slim>=1.1\n",
            "  Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m352.1/352.1 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.9/dist-packages (from BLEURT==0.0.2) (0.1.97)\n",
            "Requirement already satisfied: absl-py>=0.2.2 in /usr/local/lib/python3.9/dist-packages (from tf-slim>=1.1->BLEURT==0.0.2) (1.4.0)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from tensorflow->BLEURT==0.0.2) (23.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.9/dist-packages (from astunparse>=1.6.0->tensorflow->BLEURT==0.0.2) (0.40.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (1.8.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2.2.3)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (3.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2.27.1)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2.16.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (0.4.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.9/dist-packages (from tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (0.6.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.9/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.9/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (4.9)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (5.3.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.9/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.9/dist-packages (from markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (6.0.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (1.26.15)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.9/dist-packages (from werkzeug>=1.0.1->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (2.1.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (3.15.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.9/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.9/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow->BLEURT==0.0.2) (3.2.2)\n",
            "Building wheels for collected packages: BLEURT\n",
            "  Building wheel for BLEURT (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for BLEURT: filename=BLEURT-0.0.2-py3-none-any.whl size=16456781 sha256=0a7a93e5b4f8845f53fc8f4ba00dcc85578c1d284047a04183b86b361080e95d\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-tn7ml70x/wheels/af/2a/4a/a4f75da1656c2c5405891575c777b827508fd4123524867b80\n",
            "Successfully built BLEURT\n",
            "Installing collected packages: tf-slim, BLEURT\n",
            "Successfully installed BLEURT-0.0.2 tf-slim-1.1.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jWREWHlwDVKS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_embedding_model = SentenceTransformer('all-MiniLM-L6-v2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465,
          "referenced_widgets": [
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        "id": "wUmGHUnSEHeZ",
        "outputId": "ce5a230b-5744-40ca-fee8-0a63fab5580b"
      },
      "execution_count": 7,
      "outputs": [
        {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statistics import  mode\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "ssuhb4PcR7GX"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "query_embeddings = sentence_embedding_model.encode(list(data['discourse_text']))\n"
      ],
      "metadata": {
        "id": "myx3H2m0RGBb"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "ground_truth = []\n",
        "count = 0\n",
        "for index, row in data.iterrows():\n",
        "  candidates = row[['predictions', 'context_predictions', 'augmented_predictions']].values\n",
        "  if len(np.unique(candidates)) > 1:\n",
        "    ground_truth.append(mode(candidates))\n",
        "  else:\n",
        "    q_embedding = [query_embeddings[count]]\n",
        "    other_embeddings = sentence_embedding_model.encode(candidates)\n",
        "    similarity = cosine_similarity(q_embedding, other_embeddings)\n",
        "    ground_truth.append(candidates[np.argmax(similarity[0])])\n",
        "  \n",
        "  count += 1\n",
        "    \n",
        "  # print(ground_truth)\n",
        "  "
      ],
      "metadata": {
        "id": "VAtz0fMlTg_n"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(data)"
      ],
      "metadata": {
        "id": "gD4NL_jNWoYU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c03512c-8de8-4fb1-d4b3-ba4e5a99fc30"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27439"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bleurt import score"
      ],
      "metadata": {
        "id": "or0wQ9VJEUwX"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "references = ground_truth\n",
        "candidates1 = data['predictions']"
      ],
      "metadata": {
        "id": "aaaNYRqUQzNL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The idea behind BLEURT is that the metric is pretrained on millions of synthetic sentences with perturbations and other manipulations that keep the original meaning relatively similar (as well as on other natural language generation objectives), and then it is fine-tuned on human ratings on the WMT Metrics dataset. "
      ],
      "metadata": {
        "id": "GaCXTT_XmF-X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scorer = score.BleurtScorer()"
      ],
      "metadata": {
        "id": "2k_mexOXXEw2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores = scorer.score(references=references, candidates=candidates1)"
      ],
      "metadata": {
        "id": "gVddS7gWQ6Wt"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmin(scores)"
      ],
      "metadata": {
        "id": "o7vHkM7CRAR0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa192818-b1fb-4308-b70e-2e84231240ec"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4572"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "references[np.argmin(scores)]"
      ],
      "metadata": {
        "id": "5oXOhs8SXZoH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "e5bfbb9e-b24c-4c27-e67a-626023f6248b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"When I was a Seagoing Cowboy, it opened up the world to me. I saw that the world wasn't all sunshine and rainbows, like the little me thought. The world was full of unfairness and was my job, everybody's job, to help stop it. I stepped up to do my part by joining the Seagoing Cowboys. Do your part. Join the Seagoing Cowboys program today. Make the world a better place, one cow at a time. \""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidates1[np.argmin(scores)]"
      ],
      "metadata": {
        "id": "nhq8al1hXfUl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d842d37d-75d1-40d0-a330-086ce8c95b84"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'You get to travel on seas! '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "candidates2 = data['context_predictions']\n",
        "candidates3 = data['augmented_predictions']"
      ],
      "metadata": {
        "id": "S0uw7nLzXhWh"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores2 = scorer.score(references=references, candidates=candidates2)\n"
      ],
      "metadata": {
        "id": "mRsH7YTxmmZl"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.argmin(scores2), candidates2[np.argmin(scores2)],\"/\", references[np.argmin(scores2)])\n",
        "print(np.argmax(scores2), candidates2[np.argmax(scores2)], \"/\", references[np.argmax(scores2)])"
      ],
      "metadata": {
        "id": "pxHvtye6peqf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a48b10cf-e5be-4d0c-8c9b-cd41bc4a4af0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18810 the cars aren't completely driverless,  / hackers \n",
            "15682 The problem is actually efficency.  / The problem is actually efficency. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores3 = scorer.score(references=references, candidates=candidates3)\n"
      ],
      "metadata": {
        "id": "KNwnnXf8m0Sv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores3[np.argmin(scores3)], candidates2[np.argmin(scores3)], \"<>\", references[np.argmin(scores3)])\n",
        "print(scores3[np.argmax(scores3)], candidates2[np.argmax(scores3)], \"<>\", references[np.argmax(scores3)])"
      ],
      "metadata": {
        "id": "BGFm_sg1p0i0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3cdd0185-db8d-4871-c0cc-8aa37e2be728"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.4608819484710693 It helps animals that might not make it otherwise live until they get wherever they are going, then someone can take care of them and make sure that they are okay.  <> When I was a Seagoing Cowboy, it opened up the world to me. I saw that the world wasn't all sunshine and rainbows, like the little me thought. The world was full of unfairness and was my job, everybody's job, to help stop it. I stepped up to do my part by joining the Seagoing Cowboys. Do your part. Join the Seagoing Cowboys program today. Make the world a better place, one cow at a time. \n",
            "0.9384633898735046 Although I understand why you would reconsider the policy I believe it could go either way.\n",
            "\n",
            "Some students may take advantage of the policy and secretly use it in class.\n",
            "\n",
            "Others might take embarrassing photos of somebody else and ruin their life.\n",
            "\n",
            "The only way to stop this from happening is to ban cell phones for good, however that can also take away the privileges that some students deserve, so what should happen to the policy?\n",
            "\n",
            "Here is my answer.  <> is almost always more affordable. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.mean(scores3))"
      ],
      "metadata": {
        "id": "fWibJ6bRvM-0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6af28c47-63b7-4f7d-db59-f075d6cb3365"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.002646777868605402\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# For fine-tuning on augmented dataset"
      ],
      "metadata": {
        "id": "LwJol3ednu1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/finetune_gpt2_example_aug_full.csv')"
      ],
      "metadata": {
        "id": "1nmuGgsOnztM"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "WJA7VUKtvqB7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "7ea794d3-4418-4e3c-a5a2-2a14269ac9e1"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                                              input  \\\n",
              "0           0  Or when somebody got hurt real bad and they ha...   \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cands = list(df.pred)\n",
        "refs = list(df.true)"
      ],
      "metadata": {
        "id": "XI3TubA4vrGU"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
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        "scores_gpt2 = scorer.score(references=refs, candidates=cands)\n"
      ],
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        "id": "251sN0Etv74o"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores_gpt2[np.argmin(scores_gpt2)], cands[np.argmin(scores_gpt2)], \"<>\", refs[np.argmin(scores_gpt2)])\n",
        "print(scores_gpt2[np.argmax(scores_gpt2)], cands[np.argmax(scores_gpt2)], \"<>\", refs[np.argmax(scores_gpt2)])"
      ],
      "metadata": {
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      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.3579964637756348   First off, the cameras that we have today are more advanced than the ones we had.  <> Even if the cameras used to take these pictures weren't of the highest quality, said cameras would have been able to at least catch some photos of further signs of life. Things such as houses or buildings, for example. Now, if some one would like to argue that the aliens lived in caves or other natural formations of the sort, how would those people like to argue the fact that there are no visible creatures in any photos? Do they expect people to believe that every living thing on Mars decided to hide from a satelite they would not even be able to see from the surface? \n",
            "0.897011935710907   Next, the author gives possible ways to study Venus safely, and effciently.  <> Next, the author gives possible ways to study Venus safely, and effciently. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(scores_gpt2)"
      ],
      "metadata": {
        "id": "ycv22ollwOR2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "690d6eab-7529-4168-fdf0-fc655d7bb5f2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.7502356443335029"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cand_embeddings = sentence_embedding_model.encode(list(df.pred))\n",
        "ref_embeddings = sentence_embedding_model.encode(list(df.true))"
      ],
      "metadata": {
        "id": "UIKIuVhBwZle"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "queries_gpt_aug = list(df['input'])"
      ],
      "metadata": {
        "id": "MYSo5MsaRNAr"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = cosine_similarity(cand_embeddings, ref_embeddings)"
      ],
      "metadata": {
        "id": "N09cBr25xRZV"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similarities = []\n",
        "for i in range(len(cand_embeddings)):\n",
        "  similarities.append(cosine_similarity([cand_embeddings[i]], [ref_embeddings[i]]))"
      ],
      "metadata": {
        "id": "i-EJkIzcxd-6"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.pred[10]"
      ],
      "metadata": {
        "id": "yltenKG-yiTB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "d1a321e9-1923-45e4-815f-6772daece69e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'  The use of technology to read the emotional expressions of the students in a classroom is unbelievably valuable.  '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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          },
          "metadata": {},
          "execution_count": 35
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    {
      "cell_type": "code",
      "source": [
        "df.true[10]"
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        "id": "nlfAhMsXy-5E",
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          "height": 36
        },
        "outputId": "72fb3cae-ddc7-4d54-ff48-1142f7773258"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The use of technology to read the emotional expressions of the students in a classroom is unbelievably valuable.  '"
            ],
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    {
      "cell_type": "code",
      "source": [
        "max(similarities)"
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        "id": "FLjL9lLl1W-H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9de95f9c-b5c7-4dcc-b168-e381ffa136c0"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0000001]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
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    {
      "cell_type": "markdown",
      "source": [
        "Example-based GPT2 finetuning"
      ],
      "metadata": {
        "id": "VifkNbLG0EeY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_example = pd.read_csv('/content/full_example.csv')"
      ],
      "metadata": {
        "id": "6y5-YNP2zLjV"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_example.head()"
      ],
      "metadata": {
        "id": "65zeuWaI1Res",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "95f0426d-aad7-4823-8846-681888321503"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
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              "          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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gpt_example.fillna(\"\\n\", inplace = True)"
      ],
      "metadata": {
        "id": "LOPRU0V92ks2"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "refs_ex = list(gpt_example.true)\n",
        "cands_ex = list(gpt_example.pred)\n",
        "print(gpt_example.pred[gpt_example.pred.map(type)==float]) "
      ],
      "metadata": {
        "id": "mF9LZN5x1TBy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "437ea937-2dd3-43ec-ab25-84596121b60e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], Name: pred, dtype: object)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "queries_ex = list(gpt_example['input'])"
      ],
      "metadata": {
        "id": "_6_H0Az4RF0b"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# gpt_example.pred.value_counts()"
      ],
      "metadata": {
        "id": "rexsnUQX1nfl"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_gpt2_ex = scorer.score(references=refs_ex, candidates=cands_ex)\n",
        "\n"
      ],
      "metadata": {
        "id": "W6PWfZmU176Z"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores_gpt2_ex[np.argmin(scores_gpt2_ex)], cands_ex[np.argmin(scores_gpt2_ex)], \"<>\", refs_ex[np.argmin(scores_gpt2_ex)])\n",
        "print(scores_gpt2_ex[np.argmax(scores_gpt2_ex)], cands_ex[np.argmax(scores_gpt2_ex)], \"<>\", refs_ex[np.argmax(scores_gpt2_ex)])"
      ],
      "metadata": {
        "id": "yi22IStQZjRm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3510d426-0190-4d9f-d099-33f05c5e0f8b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.4866538047790527 iced <> Having more points of view on a topic is always a good thing \n",
            "0.8661123514175415   \" Furthermore the electoral college avoids the problem of elections in which no candidate recieves a majority of the votes cast.  <> Furthermore, The electoral college avoids the problem of elections in which no candidate recieves a majority of the votes cast. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(scores_gpt2_ex)"
      ],
      "metadata": {
        "id": "iDTEpMrD2u_s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25ea6010-5aff-459b-ae7b-71005725e3d0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.8054954238371416"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fastrag t5"
      ],
      "metadata": {
        "id": "Q-AdOZTE338D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ft5 = pd.read_csv('/content/res_t5.csv')\n",
        "ft5.shape"
      ],
      "metadata": {
        "id": "oBniRhdc2xxI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79a5e02d-ebe5-4633-b71c-9d5cd4facb30"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "truth_t5 = []\n",
        "indices = []\n",
        "for index, row in ft5.iterrows():\n",
        "  # print(q)\n",
        "  try:\n",
        "    corresponding_gt = data.loc[data['discourse_text'] == row['query']].index.values[0]\n",
        "    # print(corresponding_gt)\n",
        "    truth_t5.append(ground_truth[corresponding_gt])\n",
        "    indices.append(index)\n",
        "  except:\n",
        "    pass"
      ],
      "metadata": {
        "id": "Q4Gpw3yh4aur"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(truth_t5)"
      ],
      "metadata": {
        "id": "z7XCkZwy54s6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2474793b-e96b-457b-f729-505911d4d8eb"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "427"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "refs_t5 = truth_t5\n",
        "cands_t5 = list(ft5.loc[indices, 'res'])"
      ],
      "metadata": {
        "id": "T2J7KtP16_rU"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "queries_t5 = list(ft5.loc[indices, 'query'])"
      ],
      "metadata": {
        "id": "zgFLGns0RAeY"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_t5_ex = scorer.score(references=refs_t5, candidates=cands_t5)\n"
      ],
      "metadata": {
        "id": "VqysmX-y7UxB"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores_t5_ex[np.argmin(scores_t5_ex)], cands_t5[np.argmin(scores_t5_ex)], \"<>\", refs_t5[np.argmin(scores_t5_ex)])\n",
        "print(scores_t5_ex[np.argmax(scores_t5_ex)], cands_t5[np.argmax(scores_t5_ex)], \"<>\", refs_t5[np.argmax(scores_t5_ex)])"
      ],
      "metadata": {
        "id": "H6EfdrF0aECg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8c98a53-fd18-47b5-8bb5-fd34054905f1"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.264596939086914 If you like animals and travling this is the job for you! You can have so much adventure you would'nt even know what to do with it! I hope that all of these reasons convinced you to become a seagoing cowboy <> You get to travel on seas! \n",
            "0.8471724987030029 Venus is a challenge to get to with its hot surface, with its thick atmosphere, and atmospheric pressure. But scientists and astronomers are facinated by Venus, because they believe it had oceans and could have supported various forms of life. <> Venus is a challenge to get to with its hot surface, with its thick atmosphere, and atmospheric pressure. But scientists and astronomers are facinated by Venus, because they believe it had oceans and could have supported various forms of life. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(scores_t5_ex)"
      ],
      "metadata": {
        "id": "F6594ZLG7a7_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b22a1009-5580-4aa2-dfd5-663f3a1ad985"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6246257539690239"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final model - ColBERT PLAID"
      ],
      "metadata": {
        "id": "3LAvhRSmFRzu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colbertplaid = pd.read_csv('/content/res.csv')"
      ],
      "metadata": {
        "id": "TLTWlnc27y4I"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colbertplaid.head()"
      ],
      "metadata": {
        "id": "Be05A2A9FlEg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "fe734a77-13ec-4167-c8b7-0e7c1c4114be"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  idx                                              query  \\\n",
              "0           0    0  I also would know that it's not a face of the ...   \n",
              "1           1    1  Finally, Despite the chanlleges and dangers th...   \n",
              "2           2    2  It does not let us the people choose. The Elec...   \n",
              "3           3    3  if we adopt the popular vote system, what woul...   \n",
              "4           4    4  In conclusion if we could possibly bulid somet...   \n",
              "\n",
              "                                                 res  \n",
              "0  It looked like an Egyptian pharoah, but turned...  \n",
              "1  Students have the tendency to not to things if...  \n",
              "2  Many argue that without the Electoral Collage ...  \n",
              "3  Although not one vote will decide an election ...  \n",
              "4  Well, first of all, The atmospere of Venus has...  "
            ],
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              "\n",
              "  <div id=\"df-6e288eb1-0e3c-4e3a-9632-a2834ad39a17\">\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",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>idx</th>\n",
              "      <th>query</th>\n",
              "      <th>res</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>Students have the tendency to not to things if...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Many argue that without the Electoral Collage ...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>Although not one vote will decide an election ...</td>\n",
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              "      <th>4</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e288eb1-0e3c-4e3a-9632-a2834ad39a17')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \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-6e288eb1-0e3c-4e3a-9632-a2834ad39a17 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-6e288eb1-0e3c-4e3a-9632-a2834ad39a17');\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": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "truth_cp = []\n",
        "indices = []\n",
        "for index, row in colbertplaid.iterrows():\n",
        "  # print(q)\n",
        "  try:\n",
        "    corresponding_gt = data.loc[data['discourse_text'] == row['query']].index.values[0]\n",
        "    # print(corresponding_gt)\n",
        "    truth_cp.append(ground_truth[corresponding_gt])\n",
        "    indices.append(index)\n",
        "  except:\n",
        "    pass"
      ],
      "metadata": {
        "id": "395HlftUGmsm"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "refs_cp = truth_cp\n",
        "cands_cp = list(colbertplaid.loc[indices, 'res'])\n",
        "scores_cp_ex = scorer.score(references=refs_cp, candidates=cands_cp)\n"
      ],
      "metadata": {
        "id": "i4yQ55ofGvff"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(scores_cp_ex[np.argmin(scores_cp_ex)], cands_cp[np.argmin(scores_cp_ex)], \"<>\", refs_cp[np.argmin(scores_cp_ex)])\n",
        "print(scores_cp_ex[np.argmax(scores_cp_ex)], cands_cp[np.argmax(scores_cp_ex)], \"<>\", refs_cp[np.argmax(scores_cp_ex)])"
      ],
      "metadata": {
        "id": "bM8aDQzNavdU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4146209-6664-4970-d863-d15768478940"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.4343461990356445 decisions are rethought  <> The last reason why I think you should choose Policy 1 is because students need to take a break from working constantly and make plans with friends \n",
            "0.9087077975273132 you might receive better ideas,  <> you might receive better ideas, \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "queries_cp = list(colbertplaid.loc[indices, 'query'])"
      ],
      "metadata": {
        "id": "u_6BNW9UQ5EX"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(scores_cp_ex)"
      ],
      "metadata": {
        "id": "Sf5uT15bG6WX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d3621f5-8511-466f-e0e4-0779f46f00b3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6027957232062655"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def average_cosine_similarity(queries, references):\n",
        "  similarities = []\n",
        "  cand_embeddings = sentence_embedding_model.encode(list(queries))\n",
        "  ref_embeddings = sentence_embedding_model.encode(list(references))\n",
        "  for i in range(len(cand_embeddings)):\n",
        "    similarities.append(cosine_similarity([cand_embeddings[i]], [ref_embeddings[i]]))\n",
        "  return np.mean(similarities)"
      ],
      "metadata": {
        "id": "oAMNywpzG9yD"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average cosine similarity for retriever examples baseline:\", average_cosine_similarity(references, candidates1))\n",
        "print(\"Average cosine similarity for retriever context baseline:\", average_cosine_similarity(references, candidates2))\n",
        "print(\"Average cosine similarity for retriever augmented baseline:\", average_cosine_similarity(references, candidates3))\n",
        "print(\"Average cosine similarity for colbert examples baseline:\", average_cosine_similarity(refs_cp, cands_cp))\n",
        "print(\"Average cosine similarity for t5 examples baseline:\", average_cosine_similarity(refs_t5, cands_t5))\n",
        "print(\"Average cosine similarity for fine-tuned gpt2 examples:\", average_cosine_similarity(refs_ex, cands_ex))\n",
        "print(\"Average cosine similarity for fine-tuned gpt2 augmented context:\", average_cosine_similarity(refs, cands))\n",
        "\n",
        "\n",
        "# The above checks for how similar the candidates are to the reference or ground truth"
      ],
      "metadata": {
        "id": "igKS0OE1ILRM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6b4ff60-59f3-41b2-c54d-74b2d517ed73"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average cosine similarity for retriever examples baseline: 0.9493475\n",
            "Average cosine similarity for retriever context baseline: 0.56818295\n",
            "Average cosine similarity for retriever augmented baseline: 0.79040325\n",
            "Average cosine similarity for colbert examples baseline: 0.5642023\n",
            "Average cosine similarity for t5 examples baseline: 0.5639642\n",
            "Average cosine similarity for fine-tuned gpt2 examples: 0.38985255\n",
            "Average cosine similarity for fine-tuned gpt2 augmented context: 0.49214935\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average cosine similarity for retriever examples baseline:\", average_cosine_similarity(data['discourse_text'], candidates1))\n",
        "print(\"Average cosine similarity for retriever context baseline:\", average_cosine_similarity(data['discourse_text'], candidates2))\n",
        "print(\"Average cosine similarity for retriever augmented baseline:\", average_cosine_similarity(data['discourse_text'], candidates3))\n",
        "print(\"Average cosine similarity for colbert examples baseline:\", average_cosine_similarity(queries_cp, cands_cp))\n",
        "print(\"Average cosine similarity for t5 examples baseline:\", average_cosine_similarity(queries_t5, cands_t5))\n",
        "print(\"Average cosine similarity for fine-tuned gpt2 examples:\", average_cosine_similarity(queries_ex, cands_ex))\n",
        "print(\"Average cosine similarity for fine-tuned gpt2 augmented context:\", average_cosine_similarity(queries_gpt_aug, cands))\n",
        "\n",
        "# The above checks how similar the candidate is to the query in retaining meaning\n"
      ],
      "metadata": {
        "id": "DvIn9OazQn0g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "497599bc-7ec6-46ef-9865-dcc579d3a093"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average cosine similarity for retriever examples baseline: 0.7070896\n",
            "Average cosine similarity for retriever context baseline: 0.46088055\n",
            "Average cosine similarity for retriever augmented baseline: 0.6567297\n",
            "Average cosine similarity for colbert examples baseline: 0.52281314\n",
            "Average cosine similarity for t5 examples baseline: 0.52713007\n",
            "Average cosine similarity for fine-tuned gpt2 examples: 0.37014592\n",
            "Average cosine similarity for fine-tuned gpt2 augmented context: 0.46640778\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def average_sentence_length(candidates):\n",
        "  # print(\"X\")\n",
        "  # print(np.min([len(x.split()) for x in candidates]))\n",
        "  return np.mean([len(x.split()) for x in candidates])"
      ],
      "metadata": {
        "id": "SvSddcZoI6xk"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average length for retriever examples baseline:\", average_sentence_length(candidates1))\n",
        "print(\"Average length for retriever context baseline:\", average_sentence_length(candidates2))\n",
        "print(\"Average length for retriever augmented baseline:\", average_sentence_length(candidates3))\n",
        "print(\"Average length for colbert examples baseline:\", average_sentence_length(cands_cp))\n",
        "print(\"Average length for t5 examples baseline:\", average_sentence_length(cands_t5))\n",
        "print(\"Average length for fine-tuned gpt2 examples:\", average_sentence_length(cands_ex))\n",
        "print(\"Average length for fine-tuned gpt2 augmented context:\", average_sentence_length(cands))\n",
        "print(\"Average length for references:\", average_sentence_length(ground_truth))"
      ],
      "metadata": {
        "id": "iyQqvSw2NbwI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6806cb5-fbc5-4096-8fe1-913e8c62c788"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average length for retriever examples baseline: 45.75720689529502\n",
            "Average length for retriever context baseline: 50.35577098290754\n",
            "Average length for retriever augmented baseline: 48.17066948503954\n",
            "Average length for colbert examples baseline: 91.07962529274005\n",
            "Average length for t5 examples baseline: 52.81733021077283\n",
            "Average length for fine-tuned gpt2 examples: 41.658181818181816\n",
            "Average length for fine-tuned gpt2 augmented context: 57.80882352941177\n",
            "Average length for references: 46.022231130872115\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BERTScore"
      ],
      "metadata": {
        "id": "5tEv0z8xP9u1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate\n",
        "from evaluate import load"
      ],
      "metadata": {
        "id": "ZQqoQcviQJdf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5aa4d2fd-095e-4f96-84bc-c6bc7e2d0e0b"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
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            "Collecting async-timeout<5.0,>=4.0.0a3\n",
            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
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            "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
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            "\u001b[?25hCollecting multidict<7.0,>=4.5\n",
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            "Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, aiosignal, aiohttp, datasets, evaluate\n",
            "Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.10.1 dill-0.3.6 evaluate-0.4.0 frozenlist-1.3.3 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bert-score"
      ],
      "metadata": {
        "id": "ZgJ6e4W9N1ux",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c340f98-3da9-4baf-ee5a-81b9b488fe08"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bert-score\n",
            "  Downloading bert_score-0.3.13-py3-none-any.whl (61 kB)\n",
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            "Installing collected packages: bert-score\n",
            "Successfully installed bert-score-0.3.13\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from evaluate import load\n",
        "\n",
        "bertscore = load(\"bertscore\")"
      ],
      "metadata": {
        "id": "k-SvXlNrQAty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "97ab8323a87244bdb3224ad88224d0c7",
            "1d3622b715584c32bb23d32d28e882f5",
            "163afe9b2984439bbc051da4649ec194",
            "2161107e2f0148248b8f95d9a04e8b3d",
            "9f8298425c2b42c48e534272dec75dc1",
            "911a65e19c0e47f5aa67ead4b4b8739a",
            "e76e3bdba8474c2fbc1210ac06fb0889",
            "a015d461d11744bcaf8595d885f9d888",
            "7f29aa4dac5a467fba8d39e14fec667e",
            "dccf694d7f7948589dc75af1f6befb04",
            "480172adcee141e0868123a58c83d8ac"
          ]
        },
        "outputId": "52867ce5-2bde-41f0-cfce-bf4235f87d1d"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.95k [00:00<?, ?B/s]"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def average_bertscore(references, candidates):\n",
        "  scores = bertscore.compute(predictions=candidates, references=references, model_type = 'distilbert-base-uncased')\n",
        "  return {\"precision\" : np.mean(scores[\"precision\"]), \"recall\" : np.mean(scores[\"recall\"]), \"f1\" : np.mean(scores[\"f1\"])}\n"
      ],
      "metadata": {
        "id": "J7kQRt9uQTX7"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "del data"
      ],
      "metadata": {
        "id": "g3UzlFZ-W9Ni"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Average bertscore for retriever examples baseline:\", average_bertscore(references, candidates1))\n",
        "print(\"Average bertscore for retriever context baseline:\", average_bertscore(references, candidates2))\n",
        "print(\"Average bertscore for retriever augmented baseline:\", average_bertscore(references, candidates3))\n",
        "print(\"Average bertscore for colbert examples baseline:\", average_bertscore(refs_cp, cands_cp))\n",
        "print(\"Average bertscore for t5 examples baseline:\", average_bertscore(refs_t5, cands_t5))\n",
        "print(\"Average bertscore for fine-tuned gpt2 examples:\", average_bertscore(refs_ex, cands_ex))\n",
        "print(\"Average bertscore for fine-tuned gpt2 augmented context:\", average_bertscore(refs, cands))\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CHWhGx3pVLGF",
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          "base_uri": "https://localhost:8080/",
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        "outputId": "763d99c5-3fa4-4709-e434-01a9b41b46ca"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average bertscore for retriever examples baseline: {'precision': 0.9683959246498532, 'recall': 0.9675833302091773, 'f1': 0.9678665732906426}\n",
            "Average bertscore for retriever context baseline: {'precision': 0.7965680760164086, 'recall': 0.8018146837051784, 'f1': 0.7984516311588542}\n",
            "Average bertscore for retriever augmented baseline: {'precision': 0.8698586512117641, 'recall': 0.8724561884975923, 'f1': 0.8706353435281232}\n",
            "Average bertscore for colbert examples baseline: {'precision': 0.7627607570040701, 'recall': 0.7954797747542763, 'f1': 0.7779127247718793}\n",
            "Average bertscore for t5 examples baseline: {'precision': 0.758221020743216, 'recall': 0.7792361677390909, 'f1': 0.7676790606500952}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n",
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n",
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n",
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n",
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average bertscore for fine-tuned gpt2 examples: {'precision': 0.615597525509921, 'recall': 0.6196660533818331, 'f1': 0.6169729711792685}\n",
            "Average bertscore for fine-tuned gpt2 augmented context: {'precision': 0.7373536869006998, 'recall': 0.7486140572411173, 'f1': 0.7421167567372322}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n",
            "Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc\n",
        "gc.collect()"
      ],
      "metadata": {
        "id": "TEugpbYOVwDu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "250f7240-686c-4177-e70a-a71b62d885f4"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Is there a correlation between score and discourse element? Does the model do better on certain kinds of elements than others?"
      ],
      "metadata": {
        "id": "7hlEXK4d3bku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/augmented_predictions_all.csv')\n",
        "  "
      ],
      "metadata": {
        "id": "fPhfR8ktWgE6"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def total_cosine_similarity(queries, references):\n",
        "  similarities = []\n",
        "  cand_embeddings = sentence_embedding_model.encode(list(queries))\n",
        "  ref_embeddings = sentence_embedding_model.encode(list(references))\n",
        "  for i in range(len(cand_embeddings)):\n",
        "    similarities.append(cosine_similarity([cand_embeddings[i]], [ref_embeddings[i]]))\n",
        "  return similarities"
      ],
      "metadata": {
        "id": "i1GMaVoSmwtB"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "small_data = pd.DataFrame(data.loc[:, 'discourse_type'])"
      ],
      "metadata": {
        "id": "3wcJB41h9yuL"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "references = ground_truth\n",
        "candidates1 = data['predictions']"
      ],
      "metadata": {
        "id": "eS30C1zNAtH7"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(ground_truth) == len(candidates1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0bLq__6TBsrW",
        "outputId": "3cd9457e-c90b-4f6f-b77d-0a88b5c7da33"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_data['cosine_ex'] = total_cosine_similarity(ground_truth, candidates1)"
      ],
      "metadata": {
        "id": "j9ADlanI97z0"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "small_data['cosine_aug'] = total_cosine_similarity(ground_truth, candidates3)"
      ],
      "metadata": {
        "id": "qvaLwATzEzEb"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "small_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ow_8DIj4-Z0r",
        "outputId": "f9a8e443-bc44-45a3-97b7-255797679609"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  discourse_type      cosine_ex     cosine_aug\n",
              "0           Lead  [[0.9999999]]  [[0.9999999]]\n",
              "1       Position        [[1.0]]        [[1.0]]\n",
              "2          Claim        [[1.0]]        [[1.0]]\n",
              "3       Evidence        [[1.0]]        [[1.0]]\n",
              "4   Counterclaim  [[0.9999998]]  [[0.9999998]]"
            ],
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              "  <thead>\n",
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              "      <th>discourse_type</th>\n",
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              "      <th>cosine_aug</th>\n",
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              "      <th>4</th>\n",
              "      <td>Counterclaim</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
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              "      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",
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              "\n",
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              "          document.querySelector('#df-42498e43-84b9-4992-98fb-efa176574d47 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-42498e43-84b9-4992-98fb-efa176574d47');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "small_data.groupby('discourse_type').mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "cGpBtfcDDdcT",
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      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           cosine_ex      cosine_aug\n",
              "discourse_type                                      \n",
              "Claim                  [[0.9562852]]  [[0.73260814]]\n",
              "Concluding Statement   [[0.9692373]]   [[0.7978327]]\n",
              "Counterclaim          [[0.84232163]]   [[0.9292264]]\n",
              "Evidence              [[0.95896477]]   [[0.7587474]]\n",
              "Lead                   [[0.9509168]]  [[0.91645914]]\n",
              "Position               [[0.9275279]]  [[0.93926555]]\n",
              "Rebuttal               [[0.9660714]]   [[0.6723923]]"
            ],
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              "      <th>Concluding Statement</th>\n",
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              "   Unnamed: 0                                              input  \\\n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "ground_truth_gpt_el = []\n",
        "for example in gpt_example['input']:\n",
        "  # print(data.loc[data['discourse_text'] == example.strip()]['discourse_type'])\n",
        "  ground_truth_gpt_el.append(data.loc[data['discourse_text'] == example]['discourse_type'].values[0])"
      ],
      "metadata": {
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      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "small_data_2 = pd.DataFrame(columns = ['discourse_element'])"
      ],
      "metadata": {
        "id": "C1_yzdMpHbnH"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "small_data_2['discourse_type'] = ground_truth_gpt_el"
      ],
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      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "small_data_2['gpt_sim'] = total_cosine_similarity(refs_ex, cands_ex)"
      ],
      "metadata": {
        "id": "iefNf8qxH7dg"
      },
      "execution_count": 155,
      "outputs": []
    },
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      "cell_type": "code",
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        "small_data_2.groupby('discourse_type').mean()"
      ],
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          "base_uri": "https://localhost:8080/",
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        },
        "id": "wBfy1EugJFE9",
        "outputId": "19a03d8a-51d4-4759-8a3a-92f4da0090bd"
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      "execution_count": 156,
      "outputs": [
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              "                      discourse_element         gpt_sim\n",
              "discourse_type                                         \n",
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              "Counterclaim                        NaN  [[0.18513055]]\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb373afe-57d7-4fd9-949b-d4bd1e0e0c50');\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": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vPUwMhOvJJq-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}