Re_it_of_T5_QA.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Eshan-K-Kaushal/Voice_Chatbot/blob/main/Re_it_of_T5_QA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L-q-kw-y17Da"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "import scipy.spatial\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# from transformers import T5Tokenizer, T5ForConditionalGeneration\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "from torch.utils.data import DataLoader, SequentialSampler, TensorDataset\n",
        "import torch\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "import os\n",
        "import glob\n",
        "\n",
        "!pip install simplet5\n",
        "from simplet5 import SimpleT5"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQBoLnyMmXTW",
        "outputId": "8885b907-e0d4-454d-caff-3590baecc84a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PATH = '/content/drive/MyDrive/NLP/Combined_Dataset_Test.csv'\n",
        "dataset = pd.read_csv(PATH)\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "#dataset = dataset[['Question', 'Answer']]\n",
        "dataset = dataset.sample(frac=1, random_state=1).reset_index()\n",
        "#print(dataset.head())\n",
        "\n",
        "q_list = \"question: \" + dataset['Question']                          # questions list to feed the model\n",
        "n_list = dataset['Answer'] + \" </s>\"  # answers list to feed the model\n",
        "\n",
        "dict_data = {'source_text': q_list,\n",
        "      'target_text': n_list}\n",
        "\n",
        "df = pd.DataFrame(dict_data)\n",
        "#print(df.head())\n",
        "\n",
        "# print(df['source_text'][1])\n",
        "# print('\\n')\n",
        "# print(df['target_text'][1])\n",
        "\n",
        "# splitting data into train and test data\n",
        "train_data, val_data = train_test_split(df[:77], test_size=0.2)\n",
        "test_data = df[70:77]\n",
        "print(train_data.shape, val_data.shape, test_data.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-3BCKNP2Z8W",
        "outputId": "3c9a3e02-428f-41b8-aaef-6151b9a9e605"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60, 2) (16, 2) (6, 2)\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
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        "from simplet5 import SimpleT5\n",
        "model = SimpleT5()\n",
        "model.from_pretrained(model_type=\"t5\", model_name=\"t5-base\")\n",
        "model.train(train_df = train_data,\n",
        "            eval_df = val_data,\n",
        "            source_max_token_len=256,\n",
        "            target_max_token_len=100,\n",
        "            batch_size=2, max_epochs=15, use_gpu=True)"
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            "  | Name  | Type                       | Params\n",
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            "0 | model | T5ForConditionalGeneration | 222 M \n",
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a43da69becc4903aeb04169b3300c41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d11870623ad40848a24b5bbaee1497a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63856800e85548549ecfda9036e1b614"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_model('/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768')"
      ],
      "metadata": {
        "id": "M3GjwEn_4EAF"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data['source_text']"
      ],
      "metadata": {
        "id": "kAClt3w_4QDV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q_test = test_data['source_text'][71]\n",
        "q_ans = test_data['target_text'][71]\n",
        "q_ug = '''I am a multi-objective solver. Ask me questions from any domain and I will answer them by breaking them down into steps and answering them step by step.\\nAppend \"Let's solve it step by step\" after every question Q: <'Question asked'>. Using the Question asked, find the domain of the question and generate a background document on the domain. Using the domain's background document answer the question step by step.\\nAnn's favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts for $7 each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at the same price. How much did each top cost?'''\n",
        "q_ug2 = '''I am a multi-objective solver. Ask me questions from any domain and I will answer them by breaking them down into steps and answering them step by step. Append \"Let's solve it step by step\" after every question Q: <'Question asked'>. Using the Question asked, find the domain of the question and generate a background document on the domain. Using the domain's background document answer the question step by step. Q: What's an example of a scientist in Britain who won the Physics Nobel Prize? '''\n",
        "print('-'*50)\n",
        "print(\"Question: \", q_ug, type(q_ug))\n",
        "print('-'*50)\n",
        "print(\"Question: \", q_test, type(q_test))\n",
        "print('-'*50)\n",
        "print(\"Answer: \",q_ans)"
      ],
      "metadata": {
        "id": "xOgqXjnU4VET"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(q_test)\n",
        "print(q_ug2)\n",
        "print('-'*50)\n",
        "#predicted_ans = model.predict(q_test)[0]\n",
        "predicted_ans_ug = model.predict(q_ug2)[0]\n",
        "print(predicted_ans_ug)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhFOmWgs4XEh",
        "outputId": "22002e32-9f92-49ad-d5b4-71d1262c69d1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I am a multi-objective solver. Ask me questions from any domain and I will answer them by breaking them down into steps and answering them step by step. Append \"Let's solve it step by step\" after every question Q: <'Question asked'>. Using the Question asked, find the domain of the question and generate a background document on the domain. Using the domain's background document answer the question step by step. Q: What's an example of a scientist in Britain who won the Physics Nobel Prize? \n",
            "--------------------------------------------------\n",
            "Let's solve it step by step. The physicist Paul Dirac was born in Britain and won the Nobel Prize in Physics in 1931.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = [a for a in test_data['target_text'] ]\n",
        "\n",
        "# get the predicted answers\n",
        "y_pred = []\n",
        "for q in test_data['source_text']:\n",
        "    y_pred.append(model.predict(q)[0])\n"
      ],
      "metadata": {
        "id": "qH1UAXJz4lgN"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "id": "t1rTOyCa4yMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rouge_score"
      ],
      "metadata": {
        "id": "R2c1_fGP47ou"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from rouge_score import rouge_scorer\n",
        "\n",
        "scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)"
      ],
      "metadata": {
        "id": "bPLxfj5b42n1"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get the rouge score between test and predicted data\n",
        "precision_rouge_scores, recall_rouge_scores = [], []\n",
        "for i in range(len(y_pred)):\n",
        "    precision_rouge_scores.append(round(scorer.score(y_test[i], y_pred[i])['rouge1'][0],2))\n",
        "    recall_rouge_scores.append(round(scorer.score(y_test[i], y_pred[i])['rouge1'][1],2))"
      ],
      "metadata": {
        "id": "vUHqf4u05FMz"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'The average precision rouge scores of q&a for test data is: {round(np.mean(precision_rouge_scores),2)}')\n",
        "print(f'The average recall rouge scores of q&a for test data is: {round(np.mean(recall_rouge_scores),2)}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jd9yq36h5GA1",
        "outputId": "4411806d-308e-4716-8a10-da0479958ddc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average precision rouge scores of q&a for test data is: 0.66\n",
            "The average recall rouge scores of q&a for test data is: 0.45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scorer = rouge_scorer.RougeScorer(['rouge1', 'rougeL'], use_stemmer=True)\n",
        "scores = scorer.score('The quick brown fox jumps over the lazy dog',\n",
        "                      'The quick brown dog jumps on the log.')\n",
        "pprint(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5NX4f7fN50IU",
        "outputId": "4e9c7154-076e-49c1-ae9b-4c07313ccb01"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'rouge1': Score(precision=0.75, recall=0.6666666666666666, fmeasure=0.7058823529411765),\n",
            " 'rougeL': Score(precision=0.625, recall=0.5555555555555556, fmeasure=0.5882352941176471)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -av /content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768 /content/drive/MyDrive/NLP/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5X2xlFjcr4P0",
        "outputId": "d1df1343-4876-4f5d-e374-e238a4477285"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/tokenizer_config.json' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/tokenizer_config.json'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/special_tokens_map.json' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/special_tokens_map.json'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/spiece.model' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/spiece.model'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/tokenizer.json' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/tokenizer.json'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/config.json' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/config.json'\n",
            "'/content/outputs/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/pytorch_model.bin' -> '/content/drive/MyDrive/NLP/simplet5-epoch-9-train-loss-0.7452-val-loss-2.3768/pytorch_model.bin'\n"
          ]
        }
      ]
    }
  ]
}