flant5_finetuning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImYijIl0Da40",
        "outputId": "64dc87a5-e631-4ea3-b38c-62a6309ec8c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting pytesseract\n",
            "  Downloading pytesseract-0.3.10-py3-none-any.whl (14 kB)\n",
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            "\u001b[?25hCollecting rouge-score\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting pyzstd>=0.15.9 (from py7zr)\n",
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            "\u001b[?25hCollecting pyppmd<1.2.0,>=1.1.0 (from py7zr)\n",
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            "\u001b[?25hCollecting pybcj<1.1.0,>=1.0.0 (from py7zr)\n",
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            "\u001b[?25hCollecting multivolumefile>=0.2.3 (from py7zr)\n",
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            "\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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            "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=76e4d5fce177010131136f0987f6345ada962dc1834b3dc8c90481b6ca821161\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, tensorboard, datasets\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\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 10 not upgraded.\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laHBQNznH5F1",
        "outputId": "43605b4a-81b9-41e7-98a4-daa6d4c07556"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting accelerate\n",
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            "Installing collected packages: accelerate\n",
            "Successfully installed accelerate-0.24.1\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\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,>=0.16.4 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) (0.4.0)\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,>=0.16.4->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,>=0.16.4->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) (3.4)\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) (2023.7.22)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "('4.35.2', '0.24.1')"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "!pip install -U accelerate\n",
        "!pip install -U transformers\n",
        "\n",
        "import accelerate\n",
        "import transformers\n",
        "\n",
        "transformers.__version__, accelerate.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "511ae356741d489186ce08dec57afaff",
            "ea4a32d0495740bd87a0da0d64a76311",
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            "121865f2ec9a4d3f9ffe6e6b2beed6c3",
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            "eeef61e86b164bf08c35bfad6ac9cda1",
            "bcd3b5b03b2d4273918b9c79b1dfb608",
            "c7916d329de144dd8116b0f6a8e04584",
            "1093ece20a0f459c82fee4ab9391ca8c",
            "932a92115449445cb49fb26522713159",
            "fb289e87756647e9910f3b1a6c1ccf29",
            "9faea1326c8c40faaaff5b2b1d115c9a"
          ]
        },
        "id": "oBuh78N3Df1O",
        "outputId": "15ec01a4-091e-4a22-90b5-2af90a9d0149"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "511ae356741d489186ce08dec57afaff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsduBVOjQMzw",
        "outputId": "30406de4-9931-4399-e64c-ff767a20ebae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ],
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('data.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}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "dHpSlz6TEMU7"
      },
      "outputs": [],
      "source": [
        "dataset_id = \"updated_data\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
          "referenced_widgets": [
            "4ffe9577386f4d50a59686de7934fb45",
            "f9eba80c9932459b9b345f83d62fc536",
            "ab149c6845004872b93379512bf3abaa",
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            "3c6ffc04f86947b180b331ee44c49b15",
            "fdfe505e9dce4d8899a94945938fcbf4",
            "890dbfab81354b98a3db59bc42273765",
            "3759303adaba48359e0f5e3e5fb37707",
            "7c02abd7e9514a558e23a5f02a7917c7",
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            "f39549c3d5b54e969ab8c67a75b8d8e4"
          ]
        },
        "id": "zO72J-eXDpf7",
        "outputId": "5d1eebd3-00a1-45ba-b6b1-611d8743b66e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ffe9577386f4d50a59686de7934fb45",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0adcf7f1258e419ea80d6ac5b01527a2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f038f5a6c35d4c98a6b3f7873d815bd0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train dataset size: 889\n",
            "Test dataset size: 150\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IBvtaOBFEPsi",
        "outputId": "3b8cbf1b-ebb4-49f9-c3fc-4bff1a4674ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "question: \n",
            "What does Dr. Cohen recommend regarding the involvement of domain experts in AI development in veterinary medicine?\n",
            "---------------\n",
            "answer: \n",
            "He suggests having domain experts involved in all stages of AI, from development and validation to clinical use, error assessment, and oversight.\n",
            "---------------\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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          ]
        },
        "id": "faxOs-LaEUqJ",
        "outputId": "d9f690c2-6c91-49be-edef-ec2bd10189a5"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "365f221ab52449828e13da7dedea4647",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f05ab525efb14e9c9f8e481571076b6f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a6c65245a914735a88602ddb226b77d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "425cf088bf1e4f108a8d75974b080cb4",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "437a327a85ef40d6b56fcf298510a968",
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            "e534004bdd8f41c284a373c7110e0fda",
            "de972fa440f94671abf345746a105bfc",
            "9fef772bf2cc4d90a65f2492b5e0fd2d",
            "d72abe665b024492b456d9aa4434fac2"
          ]
        },
        "id": "uryEb7wKEa5y",
        "outputId": "8ec34ff8-c30d-4171-9ae4-334e730c3b6e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "437a327a85ef40d6b56fcf298510a968",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max source length: 46\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "336ffe9a7e2f42e09193b2e7997bb581",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/1039 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Max target length: 125\n"
          ]
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
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            "e6c20c6455214296a8c2837a2d593ccb"
          ]
        },
        "id": "-WtiWtZiEhka",
        "outputId": "177734a0-29b5-475c-fdf3-d9b8e290906a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "90ddcf2f088c44a18eca85ce97731b8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/889 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fd6ce1202a3c4d7d81346424703f99e0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "012fed5480e54b568c74353fe6f8e253",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ],
      "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)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "19f72f060e2647d4be5d8a297ec1c369",
            "faa8696246b24df5a1547ae0a987d9f8"
          ]
        },
        "id": "pXzkJAZ4EpQ5",
        "outputId": "df49d145-d498-431f-8200-e672e26370ff"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f727e20a0eed436fa2b90e0b6bfcd057",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fdf0c5b18362472cb1aca62edb847839",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "551568569b7e4d9ca247a928183b3326",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-lFkLwtE-nK",
        "outputId": "bc41217a-e4aa-4913-ec3d-8bbf9b20bc49"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "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[31m1.4 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",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.23.5)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from evaluate) (4.66.1)\n",
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            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2023.6.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.19.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (23.2)\n",
            "Collecting responses<0.19 (from evaluate)\n",
            "  Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (9.0.0)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (0.6)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.8.6)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (6.0.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.7.0->evaluate) (4.5.0)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2023.7.22)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2023.3.post1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.1.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n",
            "Installing collected packages: responses, evaluate\n",
            "Successfully installed evaluate-0.4.1 responses-0.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "0536044c5d6c4243900b3119a64a4aac",
            "7c186cacf43d4bb1abf84033f0dbe285",
            "2f37566d5373421fb75ed36ea7aae6d4",
            "21ff4cd6dcdf45b08c6ded2eb3c17013",
            "2a1c7044a0594439af18e131fe5da42c",
            "80047d83b92447c3afecac2ab9ef3554",
            "631cce0a1bd84c00aa56cfda34aaffb2",
            "2e5516fb9bb145acb278fa01627ccef3",
            "6393b37878014a55b0cb3f4635a2fab6",
            "8f730dcd3cb2496c82973cbe8533db22",
            "7a850d86e20a4c608e2800ea374a25fe"
          ]
        },
        "id": "kcfxQateEzyu",
        "outputId": "8fec24d1-1ef0-444a-b0bb-06fcfffaca8e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0536044c5d6c4243900b3119a64a4aac",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "zz1-nL76E5es"
      },
      "outputs": [],
      "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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "3nL3OHb1FbaS"
      },
      "outputs": [],
      "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}\"\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",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "YCwO4xbfFgnS",
        "outputId": "a7958e15-08ba-4ebc-ccfa-027c27aeb381"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='560' max='560' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [560/560 06:38, 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>2.424538</td>\n",
              "      <td>30.894300</td>\n",
              "      <td>14.009900</td>\n",
              "      <td>26.118600</td>\n",
              "      <td>26.601400</td>\n",
              "      <td>18.046667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.303318</td>\n",
              "      <td>31.261800</td>\n",
              "      <td>14.963200</td>\n",
              "      <td>26.611600</td>\n",
              "      <td>27.031500</td>\n",
              "      <td>18.093333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.236869</td>\n",
              "      <td>29.904700</td>\n",
              "      <td>14.715100</td>\n",
              "      <td>25.582400</td>\n",
              "      <td>26.016800</td>\n",
              "      <td>17.966667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.195417</td>\n",
              "      <td>30.246900</td>\n",
              "      <td>15.135800</td>\n",
              "      <td>26.156600</td>\n",
              "      <td>26.572200</td>\n",
              "      <td>18.180000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.450000</td>\n",
              "      <td>2.185498</td>\n",
              "      <td>30.150400</td>\n",
              "      <td>15.060100</td>\n",
              "      <td>25.975700</td>\n",
              "      <td>26.430400</td>\n",
              "      <td>18.180000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: 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:1273: 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:1273: 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:1273: 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:1273: 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"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=560, training_loss=2.4235598155430385, metrics={'train_runtime': 401.6633, 'train_samples_per_second': 11.066, 'train_steps_per_second': 1.394, 'total_flos': 285351227965440.0, 'train_loss': 2.4235598155430385, 'epoch': 5.0})"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Start training\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "_kb6HBruI4kc",
        "outputId": "aca7f214-3611-4bf9-901d-83703dacba08"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1273: 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"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='19' max='19' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [19/19 00:09]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "{'eval_loss': 2.185497760772705,\n",
              " 'eval_rouge1': 30.1504,\n",
              " 'eval_rouge2': 15.0601,\n",
              " 'eval_rougeL': 25.9757,\n",
              " 'eval_rougeLsum': 26.4304,\n",
              " 'eval_gen_len': 18.18,\n",
              " 'eval_runtime': 10.5637,\n",
              " 'eval_samples_per_second': 14.2,\n",
              " 'eval_steps_per_second': 1.799,\n",
              " 'epoch': 5.0}"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.evaluate()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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            "4e793e9c94d541d589b35b4a31481c1f"
          ]
        },
        "id": "HD3w4LcJUAvj",
        "outputId": "c4f7c675-1d05-4c1b-b19c-270c4d81cd24"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "309464fccb3142698f50c07bfed735ab",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195,
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          ]
        },
        "id": "XAx3ayraS3BW",
        "outputId": "5c312a4a-4a15-4238-886f-e0e7cad023aa"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b8854bf35014bb585fbd80ee2a268e8",
              "version_major": 2,
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          },
          "metadata": {},
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        },
        {
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            "text/plain": [
              "model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "training_args.bin:   0%|          | 0.00/4.73k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "28709ad57c8c4752a43ca6698abc3cbe",
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            "text/plain": [
              "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/ahmed807762/flan-t5-base-updated_data/tree/main/'"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "SixJZnECWDu4"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ahmed807762/flan-t5-base-updated_data\"\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\": \"The answer to the universe is\",\n",
        "})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjjyafejWJY2",
        "outputId": "3fc621cd-03a8-495b-d935-d3d78fb347b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[{'generated_text': 'The universe is made up of two parts, the first being the universe and the second being the'}]\n"
          ]
        }
      ],
      "source": [
        "print(output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "c02212e6-1dc4-4949-806c-1badf8802976"
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      "outputs": [
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        {
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          "text": [
            "Some weights of T5ForQuestionAnswering were not initialized from the model checkpoint at ahmed807762/flan-t5-base-updated_data and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
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          "text": [
            "question: \n",
            "How can Pseudorabies get into swine herds?\n",
            "---------------\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-bb53f9db4934>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# summarize dialogue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msummarizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"question\"\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     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"flan-t5-base answer:\\n{res[0]['answer']}\"\u001b[0m\u001b[0;34m)\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/transformers/pipelines/question_answering.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    389\u001b[0m         \u001b[0;31m# Convert inputs to features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 391\u001b[0;31m         \u001b[0mexamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\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    392\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\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**\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[0;32m/usr/local/lib/python3.10/dist-packages/transformers/pipelines/question_answering.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\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--> 219\u001b[0;31m             \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\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    220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minputs\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/transformers/pipelines/question_answering.py\u001b[0m in \u001b[0;36mnormalize\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mQuestionAnsweringPipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_sample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{item} argument needs to be of type (SquadExample, dict)\"\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    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: H argument needs to be of type (SquadExample, dict)"
          ]
        }
      ],
      "source": [
        "from transformers import pipeline\n",
        "from random import randrange\n",
        "\n",
        "# load model and tokenizer from huggingface hub with pipeline\n",
        "summarizer = pipeline(\"question-answering\", model=\"ahmed807762/flan-t5-base-updated_data\", device=0)\n",
        "\n",
        "# select a random test sample\n",
        "sample = dataset['test'][randrange(len(dataset[\"test\"]))]\n",
        "print(f\"question: \\n{sample['question']}\\n---------------\")\n",
        "\n",
        "# summarize dialogue\n",
        "res = summarizer(sample[\"question\"])\n",
        "\n",
        "print(f\"flan-t5-base answer:\\n{res[0]['answer']}\")"
      ]
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