flant5_finutuning_v2.ipynb

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      "cell_type": "code",
      "source": [
        "!pip install pytesseract transformers datasets rouge-score nltk tensorboard py7zr --upgrade\n",
        "# install git-fls for pushing model and logs to the hugging face hub\n",
        "!sudo apt-get install git-lfs --yes"
      ],
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        },
        "id": "BOojRmTNCv5u",
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          "text": [
            "Collecting pytesseract\n",
            "  Downloading pytesseract-0.3.10-py3-none-any.whl (14 kB)\n",
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            "  Downloading transformers-4.36.0-py3-none-any.whl (8.2 MB)\n",
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            "\u001b[?25hCollecting datasets\n",
            "  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
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            "\u001b[?25hCollecting rouge-score\n",
            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
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            "Collecting tensorboard\n",
            "  Downloading tensorboard-2.15.1-py3-none-any.whl (5.5 MB)\n",
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            "\u001b[?25hCollecting py7zr\n",
            "  Downloading py7zr-0.20.8-py3-none-any.whl (67 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (9.4.0)\n",
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            "Collecting pyarrow-hotfix (from datasets)\n",
            "  Downloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
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            "  Downloading dill-0.3.7-py3-none-any.whl (115 kB)\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.15-py310-none-any.whl (134 kB)\n",
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            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard) (67.7.2)\n",
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            "Collecting texttable (from py7zr)\n",
            "  Downloading texttable-1.7.0-py2.py3-none-any.whl (10 kB)\n",
            "Collecting pycryptodomex>=3.16.0 (from py7zr)\n",
            "  Downloading pycryptodomex-3.19.0-cp35-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m63.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pyzstd>=0.15.9 (from py7zr)\n",
            "  Downloading pyzstd-0.15.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (412 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m412.3/412.3 kB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pyppmd<1.2.0,>=1.1.0 (from py7zr)\n",
            "  Downloading pyppmd-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (138 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.9/138.9 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pybcj<1.1.0,>=1.0.0 (from py7zr)\n",
            "  Downloading pybcj-1.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.7/49.7 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multivolumefile>=0.2.3 (from py7zr)\n",
            "  Downloading multivolumefile-0.2.3-py3-none-any.whl (17 kB)\n",
            "Collecting inflate64<1.1.0,>=1.0.0 (from py7zr)\n",
            "  Downloading inflate64-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (93 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.1/93.1 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting brotli>=1.1.0 (from py7zr)\n",
            "  Downloading Brotli-1.1.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.0 MB)\n",
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            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from py7zr) (5.9.5)\n",
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            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (5.3.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.5.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard) (2.1.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard) (3.2.2)\n",
            "Building wheels for collected packages: rouge-score\n",
            "  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rouge-score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=d810af45478599e37a9e359e034462f867ddbb689a27a6a572a6234a748b6e0b\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
            "Successfully built rouge-score\n",
            "Installing collected packages: texttable, brotli, pyzstd, pytesseract, pyppmd, pycryptodomex, pybcj, pyarrow-hotfix, multivolumefile, inflate64, dill, rouge-score, py7zr, multiprocess, transformers, tensorboard, datasets\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.35.2\n",
            "    Uninstalling transformers-4.35.2:\n",
            "      Successfully uninstalled transformers-4.35.2\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.14.1\n",
            "    Uninstalling tensorboard-2.14.1:\n",
            "      Successfully uninstalled tensorboard-2.14.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "tensorflow 2.14.0 requires tensorboard<2.15,>=2.14, but you have tensorboard 2.15.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed brotli-1.1.0 datasets-2.15.0 dill-0.3.7 inflate64-1.0.0 multiprocess-0.70.15 multivolumefile-0.2.3 py7zr-0.20.8 pyarrow-hotfix-0.6 pybcj-1.0.2 pycryptodomex-3.19.0 pyppmd-1.1.0 pytesseract-0.3.10 pyzstd-0.15.9 rouge-score-0.1.2 tensorboard-2.15.1 texttable-1.7.0 transformers-4.36.0\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (3.0.2-1ubuntu0.2).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 16 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U accelerate\n",
        "!pip install -U transformers\n",
        "\n",
        "import accelerate\n",
        "import transformers\n",
        "\n",
        "transformers.__version__, accelerate.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzSU6rLnDOqW",
        "outputId": "d181c59e-6d0e-403a-d791-8fe695a55135"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting accelerate\n",
            "  Downloading accelerate-0.25.0-py3-none-any.whl (265 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu118)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.19.4)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2023.11.17)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.25.0\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.36.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('4.36.0', '0.25.0')"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ec2a14685c894817b4366b9a07133987",
            "241190cfb0564859afaa199c9a904ace",
            "165c84d4d2c94d299a80c99bb9deac9f",
            "01fc42a407a84e979043618b3852cd06",
            "0c58af069b7e4b4593a2636df224bfe0",
            "f47ada5b37f94539a89b5b254febb33e",
            "3d4a919125304ca2b0ba6a4a72cd6300",
            "3b40487745164221b7f58dd96d320e5a",
            "a57783de1fed46029eac6d26a78effbe",
            "06316f2107364ae2916d5463ed0da251",
            "76040244a6d2490fa3a2b2550282ea94",
            "2cc3d5bdbe6f4498baa2e6035617a6e4",
            "582dcd700abd4bb790ad3c66bbb6728a",
            "734b0fb24a3243febf1e47a554e86101",
            "8f0bf940470a451c9defd4ff35da43e2",
            "9c6a71d25a8f40389f4b838f33e989e0",
            "31d8585024e7471ca8b0f7bf45c12835",
            "647d9e4c2cb74db084d858d31d6f11a4",
            "27d312a11f2347bcadc81521910fcbdd",
            "dd88fce0c56744cc9748a79749c0a1a6",
            "60b940a87265448d8d6d398cbe7ccb3f",
            "bb138e980dd4461ebeb914bbaf7bd085",
            "63729ea6e95e48379bd4ca056126ad05",
            "3658a7e6d0ca4a15ade7796c1de6b127",
            "96c19d864ac74d4aba9cfffc60630cb8",
            "94904232a4f6482da9684a9db2085255",
            "f21863d9e2eb4c6db5a02b5d2e6f446a",
            "acf2f8c96565490a992c6f4cd77c3151",
            "170ace9b1d3046c08b358ad6547889ba",
            "2df55993af1e4bbc911bc3a48874b20e",
            "838527c6dca34d0d8bf05ff9b0542288",
            "f7e6320e53874139900de633f8b4969c"
          ]
        },
        "id": "AzopFJwbDcsu",
        "outputId": "5fb8123b-172d-4210-a798-d7721228c4fc"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec2a14685c894817b4366b9a07133987"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('mudata.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# Add IDs to each row\n",
        "for idx, item in enumerate(data, start=1):\n",
        "    item['id'] = idx\n",
        "\n",
        "# Write the updated data to a new file\n",
        "new_file_path = 'updated_data.json'\n",
        "with open(new_file_path, 'w') as new_file:\n",
        "    json.dump(data, new_file, indent=2)\n",
        "\n",
        "print(f'Updated data with IDs written to {new_file_path}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2itIkCS4DjGf",
        "outputId": "ad6932ec-ab4e-42f4-e116-8ba4d54ea03e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_id = \"veterinaryQA_data\""
      ],
      "metadata": {
        "id": "RJv-rzlIDtnt"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"json\", data_files=\"updated_data.json\")\n",
        "\n",
        "datasets_train_test = dataset[\"train\"].train_test_split(test_size=150)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=150)\n",
        "\n",
        "dataset[\"train\"] = datasets_train_validation[\"train\"]\n",
        "dataset[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "dataset[\"test\"] = datasets_train_test[\"test\"]\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
          "referenced_widgets": [
            "f8c6e975bd004f5e93e0a73691c6f4cf",
            "bf859c89390d4c388516c003a5ac990b",
            "07076f679700452f96da87033ae5cf92",
            "eed6642ba525460e99be016e641909bd",
            "60ef3e929c184114915452c7dfc7c194",
            "7d77b94dd25645418620dc631b8ac763",
            "869891f77efc475aa7075e36786d5fc4",
            "df3637254fa64891b7cbfb71d70b4e86",
            "ee27716ff83b479eb730b51881632cd3",
            "638cf560d0e342ec990a2f65e4783993",
            "7ae66cb75c0d4565848c0ffed56fd4b3",
            "63a321c85285470fbe9b9ebc267bf3f5",
            "ea883c98acd746de862d64c8a988f014",
            "dbdffea29d9f48889c7c2bcb1ce10bdd",
            "969044677c2b43e8818ee7ac9edbbad3",
            "9ff0e7950f044f96ba4bb0566c3a21fe",
            "790d8b9aeae14ec98d537f165d168f26",
            "f095bef73128430a9ba4a98debc2059f",
            "7b63f433ffbe41dcb4065150bf7b4690",
            "f4acf9b459314a20b5ba1953ad863f28",
            "1835eb41506d4dc0aa2d56c8f08a86ce",
            "2e9070e52e51471cb83154795d941bac",
            "908643fac8d74bfa972aecfdbe238d8f",
            "a77537058ab843dabc14f99317123480",
            "d26fa3a6489345d0aad6a6245749b306",
            "ccc670a0116d4f3e8a43282f2632957d",
            "fe0148c96da34b35be2f64d609103332",
            "4e62af5d6de14c2ba5b237f4c429f6cd",
            "0d93d1d8403d43a0bc53079751843525",
            "4ac04ab7b36d40ed91af405735d8634d",
            "9bbc42b5849048e1ae6ebbb9b43c592f",
            "5121930bd29f427aab2e2b571a1b9123",
            "36a499eea2a64e3ebe57e4bf62da616d"
          ]
        },
        "id": "4KJL4ioND48B",
        "outputId": "b7424bcd-418f-47c6-dda1-e5baedd43fb0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8c6e975bd004f5e93e0a73691c6f4cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "63a321c85285470fbe9b9ebc267bf3f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "908643fac8d74bfa972aecfdbe238d8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 3861\n",
            "Test dataset size: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from random import randrange\n",
        "\n",
        "\n",
        "sample = dataset['train'][randrange(len(dataset[\"train\"]))]\n",
        "print(f\"question: \\n{sample['question']}\\n---------------\")\n",
        "print(f\"answer: \\n{sample['answer']}\\n---------------\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvSiHr-UD77w",
        "outputId": "94c7994b-d0f6-4226-c591-1f525501eadf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "question: \n",
            "What guidelines have been developed by the American Association of Bovine Practitioners and the Academy of Veterinary Consultants?\n",
            "---------------\n",
            "answer: \n",
            "They have developed guidelines to protect veterinary businesses in the event of an outbreak of a foreign animal disease in livestock. The guidelines provide guidance for maintaining essential veterinary services, implementing biosecurity protocols, and addressing common client questions during an outbreak.\n",
            "---------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id=\"google/flan-t5-base\"\n",
        "\n",
        "# Load tokenizer of FLAN-t5-base\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ad9789a3c6324760a146e02bec6d934e",
            "e45e55dec5134fed82604cb693f2bf02",
            "eb273e182ec7403f841224856c9c9947",
            "ec4b70486e8949d487b92b1ade248ac7",
            "b7d2a6ebba564bf6957dc11f6d13f8a9",
            "ebe0731b8d2440268e4e53b22a3dbeb1",
            "82ffd49d6c2c4ed0aa23b25cc1e465c2",
            "fda3f0d0a58f421a8b736410f4ae552d",
            "df00fab366164fedaf6c64820cb0d00e",
            "9084225550a449afbcc5e9e5175087fc",
            "fa8de4f9a71843b8854831133224b73e",
            "561d93d7699248f5bae51aa860bdcb87",
            "f233cceee4754bcba7f48e8a53a8850a",
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            "6fc309d4ecae47b6ab9f6b8afb057904",
            "70a99aa3b8b140caa4a225d8efbe62e9",
            "f17f770d3db04d5ba31795efe48bdfb2",
            "bb6f12fb61ba47848368fcef86b99194",
            "ab57fb29866f46e99518d0f39bcdb802",
            "34c1b34a241c4d8abf296be9ac7fefb7",
            "cb4b31af16c0484ea201f1fbdca7ce9b",
            "018415db9ea74742b24ca32840904e6a",
            "56916a92bcbd410fbcc52de26d9a8e15",
            "65bfad0f604d47ebb01dada19bec2e05",
            "ccd5d239c51d40c8a1f0817d18578800",
            "d719342b73a943cc81bb2f4250f0e3e2",
            "d9be494aa80944e99920e4e2715f9817",
            "bbf093196e984fba9117e4fc991e5402",
            "25686f77c21f46f9a5306e60a38c80ce",
            "7e6c497f1c0a4cbb891ab277cd50d02e",
            "502ac847d133483cbeef975ee087bc14",
            "88397c56391a44e3bac57d4cea33ecfc",
            "eefc6e1ba3754d528ca005f979fd8444",
            "9968e94fcf9945ce9959838c0d33fd45",
            "f45b7d88924d4f8c910822af0d6a9a2e",
            "bdc02f0b41c74deb9b8745195d8ea2e4",
            "fd7735555b964b49ad4bd6bae2aa072c",
            "62d88490ee3547b0b8eb3182e7c09fbe",
            "2aaf64e020d54c59953ea1dbf5debd41",
            "8ec9eb2903df4e458f9c986e79f5a9e0",
            "026b5d9e76be42f293707ae1a161f912"
          ]
        },
        "id": "6-GEPGxfEAhE",
        "outputId": "b717b6b2-aada-4d30-c28e-0718fc5a02de"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad9789a3c6324760a146e02bec6d934e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "561d93d7699248f5bae51aa860bdcb87"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34c1b34a241c4d8abf296be9ac7fefb7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "502ac847d133483cbeef975ee087bc14"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"question\"], truncation=True), batched=True, remove_columns=[\"question\", \"answer\"])\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"answer\"], truncation=True), batched=True, remove_columns=[\"question\", \"answer\"])\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "20dc54035677420c8ed73f96eddb3b55",
            "0aaa562f6b7b4103b123560ba8415c5a",
            "9f890bb2a7f740f49b8e505e3d74e61a",
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            "ed3cc5562a0341e7ae147e93d53519c7",
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            "311cfb89efd548f288ccbb2832d5bacc",
            "76a9f0a2458d47cda5c0b8b935de5036",
            "1b0de166067d439e9aae3a2c4f3cf63e",
            "3e0047e191d547589a73d8e044ff887e",
            "07f3dcbb69c641b791d896e30fd99e97",
            "f137f3ba6e964359a7c51d7d334fce67",
            "2fbbfd179ff246b5bc7c3a58e057da4d",
            "6e260084a02540b5933d85adf2a7019c",
            "04d7083465dc4773982239b45dfe1033",
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            "34108cf01cc54baf8a546acdba4f5af2",
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            "65565a1ebece4a0ebb0950d6fe72d121",
            "6ae289eae4de42e788dbe86a2477267e",
            "aff1590c8c9342e893c7aacb40d0c832"
          ]
        },
        "id": "omMYlY22EHHn",
        "outputId": "a4776fbb-1123-4274-a212-ca8e4bb27445"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4011 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "20dc54035677420c8ed73f96eddb3b55"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 66\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4011 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "07f3dcbb69c641b791d896e30fd99e97"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 136\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"answer the following question: \" + item for item in sample[\"question\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"answer\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=[\"question\", \"answer\", \"id\"])\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
          "referenced_widgets": [
            "8f7bafe0a489400c8fa684fa18f8ef10",
            "2486fa32786d42c9859b4f84a3b664a2",
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            "44e47610ccf64ed6ad9061b074a5c766",
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            "a700d963d4bb4ce5a62011b7c8636cb3",
            "cb41671c677b4dabb5ca708a96c6cb71",
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            "50e2e451ed6344ae9d6f9ac905d2d69b",
            "b3c390488d0646048b5a9d6814308f5b",
            "44235a31925547fabc9a906d1ea0fb76",
            "4ba009acc5a747a095b52d5baf89fb56",
            "2375d7b520d449c9b9864f913e1b22eb",
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            "80973c34dd094017aa73acc91f00ea45",
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            "2085ab5f7b614915bbf1cc03d7daed33",
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            "da2df4d303d84ee0a61c13c7d532f3a6",
            "04985fc9443b47f3b4a73ac7a1446356",
            "df50f79bce384e30b0edeb01b9c69186",
            "e74eba9a3c344914bcb4e949236ad272",
            "b32c3afaaf2e4e56bf0732611b64a828",
            "3e2c3698b8574c5581ab65e8fb9ea014"
          ]
        },
        "id": "r8N3lquSEL7g",
        "outputId": "86971aee-544c-47d7-a512-33725400df15"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3861 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f7bafe0a489400c8fa684fa18f8ef10"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3c390488d0646048b5a9d6814308f5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0b92f5d2d3d24466adc8970ebb99ccad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "\n",
        "# huggingface hub model id\n",
        "model_id=\"google/flan-t5-base\"\n",
        "\n",
        "# load model from the hub\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "c0c6541b5a884c7a9d60a7748a776e75",
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            "19cc61193f804ff7b8fd274a97219411",
            "250e710e160f429ba22c9de37c496a25",
            "2eb3da9fb7ee41f89501dc7fc4ec67e8",
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            "8f28c87ceec447a1b8bb1f6ef7bcf378",
            "ed8d78170e99421fbf5adc90be016895",
            "bf11be53c3024d3f88e31aa6440715ed",
            "b9218a08f37743ff9ad064f1d067ffdd",
            "792a17d901274faa965a09c4c83a3e48",
            "b308fa85a18e4c549595870b166cad10",
            "3480146b3da0469b8d62301080bc8ba5",
            "00ab661e71464ee3878ba9df869e73ce",
            "6d2342512a634e87900246fbf19c1dbf",
            "e588b8ca45ff4f14b057b2022cc5e6c9",
            "6ad574c8ea7d40789a99ef0feed5f739",
            "8adf62e59cb3466fa02285c4369b6206"
          ]
        },
        "id": "VTX3iQiLEPnW",
        "outputId": "1badcbeb-76c5-4815-944a-7282a83ba1f4"
      },
      "execution_count": 11,
      "outputs": [
        {
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            }
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          "metadata": {}
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0j4J1jeuET91",
        "outputId": "c68a37c6-8a6f-42e7-a1c2-69dc4875348b"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.1-py3-none-any.whl (84 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.15.0)\n",
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            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
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            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "import nltk\n",
        "import numpy as np\n",
        "from nltk.tokenize import sent_tokenize\n",
        "nltk.download(\"punkt\")\n",
        "\n",
        "# Metric\n",
        "metric = evaluate.load(\"rouge\")\n",
        "\n",
        "# helper function to postprocess text\n",
        "def postprocess_text(preds, labels):\n",
        "    preds = [pred.strip() for pred in preds]\n",
        "    labels = [label.strip() for label in labels]\n",
        "\n",
        "    # rougeLSum expects newline after each sentence\n",
        "    preds = [\"\\n\".join(sent_tokenize(pred)) for pred in preds]\n",
        "    labels = [\"\\n\".join(sent_tokenize(label)) for label in labels]\n",
        "\n",
        "    return preds, labels\n",
        "\n",
        "def compute_metrics(eval_preds):\n",
        "    preds, labels = eval_preds\n",
        "    if isinstance(preds, tuple):\n",
        "        preds = preds[0]\n",
        "    decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)\n",
        "    # Replace -100 in the labels as we can't decode them.\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    # Some simple post-processing\n",
        "    decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)\n",
        "\n",
        "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "    result = {k: round(v * 100, 4) for k, v in result.items()}\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "    return result\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "03ca7f8529db4dbebb4d23117d88d2bc",
            "b62c8625c791487aad26f73b1546fa19",
            "87a034d8d68f483cbfc24e62139345a3",
            "2f6d98b9aa5c4ab68305a952addfc03b",
            "e5966b5212d3494b822718cdb82d1c2e",
            "361ad88cc3e940d5a6598d0a0a18eeb6",
            "57480ade4d974cc288428ff9a60c5b3e",
            "8b5f5b23e1434bcea31bb0095078b09f",
            "141bec17a4dd42cda12de5d825094bd6",
            "fae83d40dc544bb6acd5d49060156ad4",
            "2baf3cd4e2aa444b9d3b1069c7843a84"
          ]
        },
        "id": "sPZuL9OGEXqW",
        "outputId": "36816f0c-7e6f-4485-bb9c-e3c55352c4ee"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03ca7f8529db4dbebb4d23117d88d2bc"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "# we want to ignore tokenizer pad token in the loss\n",
        "label_pad_token_id = -100\n",
        "# Data collator\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer,\n",
        "    model=model,\n",
        "    label_pad_token_id=label_pad_token_id,\n",
        "    pad_to_multiple_of=8\n",
        ")"
      ],
      "metadata": {
        "id": "cUwTpVJcEc1S"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import HfFolder\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "\n",
        "# Hugging Face repository id\n",
        "repository_id = f\"{model_id.split('/')[1]}-{dataset_id}-v2\"\n",
        "\n",
        "# Define training args\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=repository_id,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=8,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False, # Overflows with fp16\n",
        "    learning_rate=5e-5,\n",
        "    num_train_epochs=5,\n",
        "    # logging & evaluation strategies\n",
        "    logging_dir=f\"{repository_id}/logs\",\n",
        "    logging_strategy=\"steps\",\n",
        "    logging_steps=500,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=2,\n",
        "    load_best_model_at_end=True,\n",
        "    # metric_for_best_model=\"overall_f1\",\n",
        "    # push to hub parameters\n",
        "    report_to=\"tensorboard\",\n",
        "    push_to_hub=False,\n",
        "    hub_strategy=\"every_save\",\n",
        "    hub_model_id=repository_id,\n",
        "    hub_token=HfFolder.get_token(),\n",
        ")\n",
        "\n",
        "# Create Trainer instance\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"],\n",
        "    compute_metrics=compute_metrics,\n",
        ")"
      ],
      "metadata": {
        "id": "cGq0cdpUEnnU"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Start training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "VwnPn55NEu5H",
        "outputId": "ccaade37-b373-4070-b317-a7b18e3421f6"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2415' max='2415' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2415/2415 22:26, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge1</th>\n",
              "      <th>Rouge2</th>\n",
              "      <th>Rougel</th>\n",
              "      <th>Rougelsum</th>\n",
              "      <th>Gen Len</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.255677</td>\n",
              "      <td>32.669900</td>\n",
              "      <td>15.733500</td>\n",
              "      <td>29.800000</td>\n",
              "      <td>29.699200</td>\n",
              "      <td>17.540000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.648300</td>\n",
              "      <td>2.104906</td>\n",
              "      <td>35.299100</td>\n",
              "      <td>18.194400</td>\n",
              "      <td>32.106100</td>\n",
              "      <td>32.127100</td>\n",
              "      <td>16.653333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>2.295300</td>\n",
              "      <td>2.023212</td>\n",
              "      <td>36.424500</td>\n",
              "      <td>18.572600</td>\n",
              "      <td>32.959200</td>\n",
              "      <td>33.128100</td>\n",
              "      <td>16.986667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.101400</td>\n",
              "      <td>1.970828</td>\n",
              "      <td>36.140000</td>\n",
              "      <td>18.356500</td>\n",
              "      <td>32.821800</td>\n",
              "      <td>32.980800</td>\n",
              "      <td>16.933333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.012200</td>\n",
              "      <td>1.959128</td>\n",
              "      <td>36.062600</td>\n",
              "      <td>18.413900</td>\n",
              "      <td>32.828000</td>\n",
              "      <td>32.863600</td>\n",
              "      <td>16.826667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "There were missing keys in the checkpoint model loaded: ['encoder.embed_tokens.weight', 'decoder.embed_tokens.weight'].\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2415, training_loss=2.211076907835145, metrics={'train_runtime': 1350.3635, 'train_samples_per_second': 14.296, 'train_steps_per_second': 1.788, 'total_flos': 1858955721891840.0, 'train_loss': 2.211076907835145, 'epoch': 5.0})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "oH-cT2JHEzci",
        "outputId": "c447e547-24fe-4011-f57b-640fe8c7ef73"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1355: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='19' max='19' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [19/19 00:13]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 1.9591283798217773,\n",
              " 'eval_rouge1': 36.0626,\n",
              " 'eval_rouge2': 18.4139,\n",
              " 'eval_rougeL': 32.828,\n",
              " 'eval_rougeLsum': 32.8636,\n",
              " 'eval_gen_len': 16.826666666666668,\n",
              " 'eval_runtime': 14.8501,\n",
              " 'eval_samples_per_second': 10.101,\n",
              " 'eval_steps_per_second': 1.279,\n",
              " 'epoch': 5.0}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "e312528ad32f45629144d3ad89a5f81c",
            "0a4a598a78b34959ae4e001fcf1ad37e",
            "d7d85bbc570844ebba5b18386eb75d22",
            "9fbbe20dfca64aac800674aaf2a9c769",
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        "id": "d1iCYfmNKHcv",
        "outputId": "aeb95d03-3c74-4979-9cba-4d9b7dd2d91c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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      "cell_type": "code",
      "source": [
        "# Save our tokenizer and create model card\n",
        "tokenizer.save_pretrained(repository_id)\n",
        "trainer.create_model_card()\n",
        "# Push the results to the hub\n",
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
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        "id": "BQHWD_WfKNrM",
        "outputId": "6ce646f3-c8ec-4e8e-aa5e-04b8bac264b9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0ce5d5eb1c54d66b49f5af3dd52a132"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "training_args.bin:   0%|          | 0.00/4.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "af92e1718ae84e31a3a09f55326e602f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "047cb4663991430c930b9c4f711c2d73"
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/ahmed807762/flan-t5-base-veterinaryQA_data-v2/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ahmed807762/flan-t5-base-veterinaryQA_data-v2\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
        "\t\"inputs\": \"question: My buffalo is lazy and not eating grass, what disease could it have? and how to cure that disease? answer: \",\n",
        "  \"parameters\": {\"max_new_tokens\": 250, \"repetition_penalty\": 7.0},\n",
        "  \"options\": {\"wait_for_model\": True}\n",
        "})"
      ],
      "metadata": {
        "id": "LNve734JKTha"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output[0]['generated_text']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "v2ZgKZi2btuO",
        "outputId": "f9f49bd0-2e6e-4193-c01f-39318952a1cf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'It could have a disease called swine fever. If it is lazy and not eating grass, it may have a disease called rhizome fever. The buffalo could have a disease called typhoid fever.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install googletrans"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 949
        },
        "id": "Nt-oOnq9LN8i",
        "outputId": "19379db3-e3da-4074-8010-6af8b68d9795"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting googletrans\n",
            "  Downloading googletrans-3.0.0.tar.gz (17 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting httpx==0.13.3 (from googletrans)\n",
            "  Downloading httpx-0.13.3-py3-none-any.whl (55 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.1/55.1 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans) (2023.11.17)\n",
            "Collecting hstspreload (from httpx==0.13.3->googletrans)\n",
            "  Downloading hstspreload-2023.1.1-py3-none-any.whl (1.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans) (1.3.0)\n",
            "Collecting chardet==3.* (from httpx==0.13.3->googletrans)\n",
            "  Downloading chardet-3.0.4-py2.py3-none-any.whl (133 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.4/133.4 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting idna==2.* (from httpx==0.13.3->googletrans)\n",
            "  Downloading idna-2.10-py2.py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.8/58.8 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting rfc3986<2,>=1.3 (from httpx==0.13.3->googletrans)\n",
            "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
            "Collecting httpcore==0.9.* (from httpx==0.13.3->googletrans)\n",
            "  Downloading httpcore-0.9.1-py3-none-any.whl (42 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting h11<0.10,>=0.8 (from httpcore==0.9.*->httpx==0.13.3->googletrans)\n",
            "  Downloading h11-0.9.0-py2.py3-none-any.whl (53 kB)\n",
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            "\u001b[?25hCollecting h2==3.* (from httpcore==0.9.*->httpx==0.13.3->googletrans)\n",
            "  Downloading h2-3.2.0-py2.py3-none-any.whl (65 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/65.0 kB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting hyperframe<6,>=5.2.0 (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans)\n",
            "  Downloading hyperframe-5.2.0-py2.py3-none-any.whl (12 kB)\n",
            "Collecting hpack<4,>=3.0 (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans)\n",
            "  Downloading hpack-3.0.0-py2.py3-none-any.whl (38 kB)\n",
            "Building wheels for collected packages: googletrans\n",
            "  Building wheel for googletrans (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for googletrans: filename=googletrans-3.0.0-py3-none-any.whl size=15715 sha256=9ffce89f6d73a9e3472fa2ac4aaab5eb026525e57d485ae170e2bbb55eaa48db\n",
            "  Stored in directory: /root/.cache/pip/wheels/b3/81/ea/8b030407f8ebfc2f857814e086bb22ca2d4fea1a7be63652ab\n",
            "Successfully built googletrans\n",
            "Installing collected packages: rfc3986, hyperframe, hpack, h11, chardet, idna, hstspreload, h2, httpcore, httpx, googletrans\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.6\n",
            "    Uninstalling idna-3.6:\n",
            "      Successfully uninstalled idna-3.6\n",
            "Successfully installed chardet-3.0.4 googletrans-3.0.0 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2023.1.1 httpcore-0.9.1 httpx-0.13.3 hyperframe-5.2.0 idna-2.10 rfc3986-1.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "chardet",
                  "idna"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install --upgrade googletrans==4.0.0-rc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "Cchbk-aCyVIf",
        "outputId": "9368f18b-ae91-4106-c800-38a94a9dd4c8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting googletrans==4.0.0-rc1\n",
            "  Downloading googletrans-4.0.0rc1.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: httpx==0.13.3 in /usr/local/lib/python3.10/dist-packages (from googletrans==4.0.0-rc1) (0.13.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (2023.11.17)\n",
            "Requirement already satisfied: hstspreload in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (2023.1.1)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (1.3.0)\n",
            "Requirement already satisfied: chardet==3.* in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (3.0.4)\n",
            "Requirement already satisfied: idna==2.* in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (2.10)\n",
            "Requirement already satisfied: rfc3986<2,>=1.3 in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (1.5.0)\n",
            "Requirement already satisfied: httpcore==0.9.* in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (0.9.1)\n",
            "Requirement already satisfied: h11<0.10,>=0.8 in /usr/local/lib/python3.10/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1) (0.9.0)\n",
            "Requirement already satisfied: h2==3.* in /usr/local/lib/python3.10/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1) (3.2.0)\n",
            "Requirement already satisfied: hyperframe<6,>=5.2.0 in /usr/local/lib/python3.10/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1) (5.2.0)\n",
            "Requirement already satisfied: hpack<4,>=3.0 in /usr/local/lib/python3.10/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1) (3.0.0)\n",
            "Building wheels for collected packages: googletrans\n",
            "  Building wheel for googletrans (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for googletrans: filename=googletrans-4.0.0rc1-py3-none-any.whl size=17396 sha256=725f7f09cd4d8a1d7249dc9dc8d627464d24069b3bd91c9092fc478c2d0e65ae\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/59/9f/7372f0cf70160fe61b528532e1a7c8498c4becd6bcffb022de\n",
            "Successfully built googletrans\n",
            "Installing collected packages: googletrans\n",
            "  Attempting uninstall: googletrans\n",
            "    Found existing installation: googletrans 3.0.0\n",
            "    Uninstalling googletrans-3.0.0:\n",
            "      Successfully uninstalled googletrans-3.0.0\n",
            "Successfully installed googletrans-4.0.0rc1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "googletrans"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import googletrans\n",
        "Translator= googletrans.Translator()\n",
        "translation = Translator.translate(output[0]['generated_text'], src='en', dest='ur')\n",
        "print(translation.text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjdT13CjxtTE",
        "outputId": "b311abcc-1b45-4b38-cc0a-37af1e294389"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "اس میں سوائن بخار نامی ایک بیماری ہوسکتی ہے۔اگر یہ سست ہے اور گھاس نہیں کھا رہا ہے تو ، اس میں ریزوم بخار نامی ایک بیماری ہوسکتی ہے۔بھینس کو ٹائیفائیڈ بخار نامی ایک بیماری ہوسکتی ہے۔\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import googletrans\n",
        "\n",
        "def translate_english_to_urdu(text):\n",
        "    \"\"\"Translates text from Urdu to Arabic using Google Translate.\"\"\"\n",
        "    Translator= googletrans.Translator()\n",
        "    translation = Translator.translate(text, src='en', dest='ur')\n",
        "    return translation.text"
      ],
      "metadata": {
        "id": "ABlUj-U0Sn0o"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__':\n",
        "    text = output[0]['generated_text']\n",
        "    print(text)\n",
        "    translation = translate_english_to_urdu(text)\n",
        "    print('Translation:', translation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "P8ZhLQmCwsfc",
        "outputId": "895cf14c-a1c6-45f4-9fc6-b214b15a4a83"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It could have a disease called swine fever. If it is lazy and not eating grass, it may have a disease called rhizome fever. The buffalo could have a disease called typhoid fever.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-5da0b364b63f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'generated_text'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mtranslation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtranslate_english_to_urdu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Translation:'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtranslation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-6-7a26af27e108>\u001b[0m in \u001b[0;36mtranslate_english_to_urdu\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m\"\"\"Translates text from Urdu to Arabic using Google Translate.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mTranslator\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mgoogletrans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTranslator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtranslation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTranslator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranslate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'en'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ur'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtranslation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/googletrans/client.py\u001b[0m in \u001b[0;36mtranslate\u001b[0;34m(self, text, dest, src, **kwargs)\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0morigin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_translate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# this code will be updated when the format is changed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/googletrans/client.py\u001b[0m in \u001b[0;36m_translate\u001b[0;34m(self, text, dest, src, override)\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_translate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverride\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m         \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoken_acquirer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         params = utils.build_params(query=text, src=src, dest=dest,\n\u001b[1;32m     80\u001b[0m                                     token=token, override=override)\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/googletrans/gtoken.py\u001b[0m in \u001b[0;36mdo\u001b[0;34m(self, text)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 194\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    195\u001b[0m         \u001b[0mtk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/googletrans/gtoken.py\u001b[0m in \u001b[0;36m_update\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;31m# this will be the same as python code after stripping out a reserved word 'var'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRE_TKK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'var '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0;31m# unescape special ascii characters such like a \\x3d(=)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unicode-escape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'group'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AdaVAj4mxGHc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}