FinetuneOpenSourceLLMs.ipynb

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  "cells": [
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Praveen76/Finetune-Open-Source-LLMs-on-custom-data/blob/main/FinetuneOpenSourceLLMs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Collecting tensorflow==2.14\n",
            "  Downloading tensorflow-2.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (489.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m489.8/489.8 MB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (1.4.0)\n",
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            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (0.2.0)\n",
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            "Requirement already satisfied: numpy>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (1.23.5)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (67.7.2)\n",
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            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (4.5.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (0.34.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.14) (1.59.3)\n",
            "Collecting tensorboard<2.15,>=2.14 (from tensorflow==2.14)\n",
            "  Downloading tensorboard-2.14.1-py3-none-any.whl (5.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tensorflow-estimator<2.15,>=2.14.0 (from tensorflow==2.14)\n",
            "  Downloading tensorflow_estimator-2.14.0-py2.py3-none-any.whl (440 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m440.7/440.7 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting keras<2.15,>=2.14.0 (from tensorflow==2.14)\n",
            "  Downloading keras-2.14.0-py3-none-any.whl (1.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow==2.14) (0.42.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow==2.14) (2.17.3)\n",
            "Collecting google-auth-oauthlib<1.1,>=0.5 (from tensorboard<2.15,>=2.14->tensorflow==2.14)\n",
            "  Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow==2.14) (3.5.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow==2.14) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow==2.14) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.15,>=2.14->tensorflow==2.14) (3.0.1)\n",
            "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<2.15,>=2.14->tensorflow==2.14) (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<2.15,>=2.14->tensorflow==2.14) (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<2.15,>=2.14->tensorflow==2.14) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow==2.14) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow==2.14) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow==2.14) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow==2.14) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.15,>=2.14->tensorflow==2.14) (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<2.15,>=2.14->tensorflow==2.14) (2.1.3)\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<2.15,>=2.14->tensorflow==2.14) (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<1.1,>=0.5->tensorboard<2.15,>=2.14->tensorflow==2.14) (3.2.2)\n",
            "Installing collected packages: tensorflow-estimator, keras, google-auth-oauthlib, tensorboard, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.12.0\n",
            "    Uninstalling tensorflow-estimator-2.12.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.12.0\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.12.0\n",
            "    Uninstalling keras-2.12.0:\n",
            "      Successfully uninstalled keras-2.12.0\n",
            "  Attempting uninstall: google-auth-oauthlib\n",
            "    Found existing installation: google-auth-oauthlib 0.4.6\n",
            "    Uninstalling google-auth-oauthlib-0.4.6:\n",
            "      Successfully uninstalled google-auth-oauthlib-0.4.6\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.12.0\n",
            "    Uninstalling tensorboard-2.12.0:\n",
            "      Successfully uninstalled tensorboard-2.12.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.12.0\n",
            "    Uninstalling tensorflow-2.12.0:\n",
            "      Successfully uninstalled tensorflow-2.12.0\n",
            "Successfully installed google-auth-oauthlib-1.0.0 keras-2.14.0 tensorboard-2.14.1 tensorflow-2.14.0 tensorflow-estimator-2.14.0\n",
            "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[31m2.9 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",
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            "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",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
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            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
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            "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",
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            "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 (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
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            "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,>=0.16.4->transformers) (2023.6.0)\n",
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            "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",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Building wheels for collected packages: rouge_score\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: pyarrow-hotfix, dill, rouge_score, responses, multiprocess, datasets, evaluate\n",
            "Successfully installed datasets-2.15.0 dill-0.3.7 evaluate-0.4.1 multiprocess-0.70.15 pyarrow-hotfix-0.6 responses-0.18.0 rouge_score-0.1.2\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x\n",
        "!pip install tensorflow==2.14\n",
        "\n",
        "!pip install PyYAML accelerate transformers datasets evaluate rouge_score\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hV9Zy3_j84kq",
        "outputId": "61c0d70f-06d9-4114-a20e-8a77fbc0fe26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Basic Libraries\n",
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import yaml\n",
        "\n",
        "#Visualization Libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#Text Handling Libraries\n",
        "import re\n",
        "import scipy.spatial\n",
        "import datetime\n",
        "import tensorflow as tf\n",
        "import accelerate, transformers\n",
        "import random\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from transformers import pipeline\n",
        "from huggingface_hub.hf_api import HfFolder"
      ],
      "metadata": {
        "id": "1JNJcmkF-XXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Load relevant API Keys\n",
        "\n",
        "file_path = '/content/drive/MyDrive/.API_KEYS/API_KEYS.yml'\n",
        "\n",
        "with open(file_path, 'r') as file:\n",
        "    api_keys = yaml.safe_load(file)\n",
        "\n",
        "for key_name, api_key in api_keys.items():\n",
        "    # Set the environment variable\n",
        "    os.environ[key_name] = api_key\n",
        "    if api_key == \"HF_READ_API_KEY\" or \"HF_WRITE_API_KEY\":\n",
        "\n",
        "      # Save the token using HfFolder\n",
        "      HfFolder.save_token(api_key)\n",
        "\n",
        "\n",
        "# # If you want to clear the environment variable after use, you can do:\n",
        "# for key_name in api_keys.keys():\n",
        "#     os.environ.pop(key_name, None)\n"
      ],
      "metadata": {
        "id": "CLtkUBJf891k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Get all environment variables\n",
        "\n",
        "# all_env_vars = os.environ\n",
        "\n",
        "# # Print all environment variables\n",
        "# for key, value in all_env_vars.items():\n",
        "#     print(f\"{key}: {value}\")\n"
      ],
      "metadata": {
        "id": "Je8hcgLjBLqZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#### You can also save the huggingface read_api_key like below-\n",
        "from huggingface_hub.hf_api import HfFolder\n",
        "read_api_key = \"<Your Hugging face read_api_key>\"\n",
        "\n",
        "HfFolder.save_token(read_api_key)\n"
      ],
      "metadata": {
        "id": "iyQo_iQ5B_V0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Load Data\n",
        "df2 = pd.read_csv(\"./ConvAI_Data.csv\")"
      ],
      "metadata": {
        "id": "ybVdah-GThks"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculate the length of each text entry\n",
        "text_lengths = df2.ProdDescr.apply(len)\n",
        "\n",
        "# Specify the interval size\n",
        "interval_size = 250\n",
        "\n",
        "# Calculate the number of bins based on the interval size\n",
        "num_bins = (text_lengths.max() - text_lengths.min()) // interval_size\n",
        "\n",
        "# Plotting the histogram with specified intervals\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(text_lengths, bins=num_bins, range=(text_lengths.min(), text_lengths.max()), color='blue', alpha=0.7)\n",
        "plt.title('Histogram of Text Length')\n",
        "plt.xlabel('Text Length')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Set x-axis ticks at intervals of 250\n",
        "plt.xticks(range(text_lengths.min(), text_lengths.max() + 1, interval_size))\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "CeNzt0oXZloX",
        "outputId": "fa95e369-f713-45ab-b1f0-9b710e8c5a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Calculate the length of each text entry\n",
        "text_lengths = df2.GPTSumm.apply(len)\n",
        "\n",
        "# Specify the interval size\n",
        "interval_size = 250\n",
        "\n",
        "# Calculate the number of bins based on the interval size\n",
        "num_bins = (text_lengths.max() - text_lengths.min()) // interval_size\n",
        "\n",
        "# Plotting the histogram with specified intervals\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(text_lengths, bins=num_bins, range=(text_lengths.min(), text_lengths.max()), color='blue', alpha=0.7)\n",
        "plt.title('Histogram of Summary Text Length')\n",
        "plt.xlabel('Summaries Length')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "# Set x-axis ticks at intervals of 250\n",
        "plt.xticks(range(text_lengths.min(), text_lengths.max() + 1, interval_size))\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "S9jou5lib65w",
        "outputId": "e5f7796d-d970-4bcd-cfde-e07ebf3746df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Filter out Product descriptions which are shorter than 400 characters length.\n",
        "df2 = df2[df2.ProdDescr.str.len() > 400]\n"
      ],
      "metadata": {
        "id": "AYKUSmiDaLj7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the DataFrame to a datasets arrow dataset\n",
        "from datasets import Dataset\n",
        "\n",
        "data = df2[['ProdDescr', 'GPTSumm']].rename(columns={'ProdDescr': 'text', 'GPTSumm': 'summary'})\n",
        "data = Dataset.from_pandas(data)\n",
        "data = data.train_test_split(test_size=0.2)"
      ],
      "metadata": {
        "id": "DUGsJnnWMN3d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune T5-small model on E-commerce data"
      ],
      "metadata": {
        "id": "j3wllvaojOYw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "checkpoint = \"Praveen76/FinetunedT5Model\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint, model_max_length=1024)  # Set model_max_length\n",
        "\n",
        "prefix = \"summarize in maximum 3 sentences: \"\n",
        "\n",
        "def preprocess_function(examples):\n",
        "    inputs = [prefix + doc for doc in examples[\"text\"]]\n",
        "    model_inputs = tokenizer(inputs, max_length=1024, truncation=True, padding=True)  # Use __call__ method directly\n",
        "\n",
        "    labels = tokenizer(examples[\"summary\"], max_length=256, truncation=True, padding=True)  # Use __call__ method directly\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_Data = data.map(preprocess_function, batched=True)\n",
        "\n",
        "from transformers import DataCollatorForSeq2Seq\n",
        "\n",
        "data_collator = DataCollatorForSeq2Seq(\n",
        "    tokenizer=tokenizer,\n",
        "    model=AutoModelForSeq2SeqLM.from_pretrained(checkpoint),\n",
        "    padding=True,\n",
        ")\n",
        "\n",
        "import evaluate\n",
        "\n",
        "rouge = evaluate.load(\"rouge\")\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)\n",
        "    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)\n",
        "    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)\n",
        "\n",
        "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)\n",
        "\n",
        "    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]\n",
        "    result[\"gen_len\"] = np.mean(prediction_lens)\n",
        "\n",
        "    return {k: round(v, 4) for k, v in result.items()}\n",
        "\n",
        "from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)\n",
        "\n",
        "# Verify Trainer Initialization\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"T5Model_for_Ecommerce\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=15,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False,\n",
        "    push_to_hub=False,\n",
        ")\n",
        "\n",
        "# Use the default collator provided by the trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_Data['train'],\n",
        "    eval_dataset=tokenized_Data['test'],\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics,\n",
        ")\n",
        "\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716,
          "referenced_widgets": [
            "432165ca6bec4d29b38c1d20b9f9ae65",
            "400b84fcc8734dc192ae9b4993c7990d",
            "6ef4d3f92ed543578d7b0eee8c990a46",
            "5f69a9198dd544cdbe11b50871a382b2",
            "f4dae5c6c12e4400857dd130722ff0df",
            "0e344a16157d4a4d9ea92c80dac821a0",
            "7ed54c1f0a2b474db4b8bece10bc84ad",
            "bf007f4af88a4107bdf995cc0e7a96eb",
            "0d92c6242a11475bb62c1a12c259ec67",
            "2546c04101574885b9108d78a69180a5",
            "bc61330154c44762a468f71c7e51c1b4",
            "4a399e482b96411fa9d39c7c89c3927e",
            "76ac5c75c7804952a4e90970c9452636",
            "547b595fc4574d6d8231deb3e101eaf9",
            "a8c44c54baed4ad8b4198d356643dca8",
            "a33313211ea44a30a4935b7c8c4bcce8",
            "02c95e047c4b46969b1487b440d5a509",
            "f71f273b7d5f414dbaf4dd5bd87bf8f2",
            "ea72c56642ac4c3b9fcb2cc3dba4f20a",
            "148005db96e54488b851cf66856ec012",
            "cfdf56bbce1a4ff0873a7d3f85fd23a4",
            "90d0abf862e649aeb91a9eb5b8205642"
          ]
        },
        "id": "t5WDde7daYd8",
        "outputId": "183b2132-2e6c-473d-d03b-7d162fc4b7dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/428 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "432165ca6bec4d29b38c1d20b9f9ae65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/108 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4a399e482b96411fa9d39c7c89c3927e"
            }
          },
          "metadata": {}
        },
        {
          "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='405' max='405' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [405/405 27:02, Epoch 15/15]\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>4.022223</td>\n",
              "      <td>0.251100</td>\n",
              "      <td>0.134600</td>\n",
              "      <td>0.212700</td>\n",
              "      <td>0.212900</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.304417</td>\n",
              "      <td>0.052300</td>\n",
              "      <td>0.028900</td>\n",
              "      <td>0.045000</td>\n",
              "      <td>0.045500</td>\n",
              "      <td>3.342600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.163007</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.142638</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.121482</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.099944</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.072993</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.050447</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.033390</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.018568</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.006868</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.998547</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.992730</td>\n",
              "      <td>0.006100</td>\n",
              "      <td>0.003500</td>\n",
              "      <td>0.005400</td>\n",
              "      <td>0.005900</td>\n",
              "      <td>0.351900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.989441</td>\n",
              "      <td>0.017400</td>\n",
              "      <td>0.009900</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>0.015200</td>\n",
              "      <td>1.231500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.988521</td>\n",
              "      <td>0.017400</td>\n",
              "      <td>0.009900</td>\n",
              "      <td>0.015000</td>\n",
              "      <td>0.015200</td>\n",
              "      <td>1.231500</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: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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=405, training_loss=1.7048657829378857, metrics={'train_runtime': 1626.6403, 'train_samples_per_second': 3.947, 'train_steps_per_second': 0.249, 'total_flos': 897744373678080.0, 'train_loss': 1.7048657829378857, 'epoch': 15.0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Put huggingface's write API key\n",
        "from huggingface_hub.hf_api import HfFolder\n",
        "write_api_key = \"<Your huggingface write_api_key>\"\n",
        "\n",
        "HfFolder.save_token(write_api_key)"
      ],
      "metadata": {
        "id": "RsF1iIckegbK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Push model to huggingfacce\n",
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324,
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        "id": "mqFWIbqoel6k",
        "outputId": "be17b20b-f0ed-4bed-b891-d96e919feee6"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "'https://huggingface.co/Praveen76/T5Model_for_Ecommerce/tree/main/'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMmTZZgZIEaY",
        "outputId": "c068fb22-89ec-4a75-bc14-0684d7e79005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "536"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### Load saved Model #####\n",
        "summarizer = pipeline(\"summarization\", model=\"Praveen76/T5Model_for_Ecommerce\")\n"
      ],
      "metadata": {
        "id": "B-tx46W4EF8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#################### Model Testing: Generate product description and compare it against the acutal product summary ##############\n",
        "\n",
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYTtkLaec0-T",
        "outputId": "85bb9144-143f-45f8-908d-abf79193b564"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This 360° PAN & TILT VIEW camera has 1080p full HD resolution and a 360-degree coverage for complete surveillance. It also features two-way talk, intelligent motion sensor, advanced night vision, mobile compatibility with Ezykam+ app, cloud monitoring, and support for Alexa and Google Assistant. The package includes the camera, USB cable, adaptor, and user manual. \n",
            "\n",
            "PredSumm:  [{'summary_text': 'This 1080p full HD plug & play Wi-Fi camera has a 360 degree coverage and features a built-in microphone and speaker for easy monitoring. It also has an in-built infrared LEDs for enhanced night vision and is compatible with Amazon Alexa and Google Assistant. The camera also comes with a USB cable, adaptor and user manual.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WLTVF9BPc0gY",
        "outputId": "58ac7ae0-1a7d-43ea-f3bb-5897e49d89e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Your max_length is set to 200, but your input_length is only 189. Since this is a summarization task, where outputs shorter than the input are typically wanted, you might consider decreasing max_length manually, e.g. summarizer('...', max_length=94)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This USB Type-C cable is perfect for connecting your MacBook, Chromebook Pixel, or Galaxy Note 7 to standard USB Type-A devices for charging, syncing data and more. With a reversible design and fast data transfer speeds of up to 480 Mbps, this cable is also certified by USB-IF for compliance with the USB 2.0 Standard. \n",
            "\n",
            "PredSumm:  [{'summary_text': 'Connect USB Type-C enabled devices with standard USB type-A enabled devices. It also has a removable design for charging, powering, and syncing data, photos and music. The type-C port is half the width and one-third the height of a standard USB-A connector. It offers up to 480 Mbps data transfer speed.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c4n_ASyWc0ax",
        "outputId": "138998d3-40af-4c66-d055-cee5dd673162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This rechargeable cordless lint remover has a long-lasting battery and is perfect for at-home or travel use. It is made with premium materials and powerful suction motor to effectively remove lint, pills, bubbles, and fuzz. It is designed specifically for Indian customers and comes with a 1-year warranty for customer support. \n",
            "\n",
            "PredSumm:  [{'summary_text': 'This rechargeable battery has a long lasting battery that can be recharged for 60 min with 6-hour charging. It is made with premium ABS material, SS mesh cover and razor-sharp blades. It also comes with a powerful suction motor that removes lint, pills, bubbles, and fuzz of all sizes. The product is ideal for Indians and is manufactured by exclusive overseas partners.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finetune BART-base model from Meta on E-commerce data"
      ],
      "metadata": {
        "id": "DyT3j7u4kNGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We'll just change the tokenizer as per the model, and output directory name, and rest of the code would remain same as above\n",
        "\n",
        "# Use the bart-base checkpoint\n",
        "checkpoint = \"facebook/bart-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint, model_max_length=1024)  # Set model_max_length\n",
        "\n",
        "tokenized_Data = data.map(preprocess_function, batched=True)\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)\n",
        "\n",
        "# Verify Trainer Initialization\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"BARTModel_for_Ecommerce\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=3,\n",
        "    num_train_epochs=15,\n",
        "    predict_with_generate=True,\n",
        "    fp16=False,\n",
        "    push_to_hub=False,\n",
        ")\n",
        "\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 748,
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        "outputId": "e60666d8-6f2a-40b8-bfe9-90f3add63548"
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/428 [00:00<?, ? examples/s]"
            ],
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        {
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              "Map:   0%|          | 0/108 [00:00<?, ? examples/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a BartTokenizerFast 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='405' max='405' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [405/405 54:39, Epoch 15/15]\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>3.312505</td>\n",
              "      <td>0.288800</td>\n",
              "      <td>0.148200</td>\n",
              "      <td>0.244800</td>\n",
              "      <td>0.245400</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>2.269942</td>\n",
              "      <td>0.292600</td>\n",
              "      <td>0.166700</td>\n",
              "      <td>0.256400</td>\n",
              "      <td>0.257400</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.768612</td>\n",
              "      <td>0.311900</td>\n",
              "      <td>0.190800</td>\n",
              "      <td>0.275200</td>\n",
              "      <td>0.275300</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.388161</td>\n",
              "      <td>0.324000</td>\n",
              "      <td>0.210400</td>\n",
              "      <td>0.293700</td>\n",
              "      <td>0.293900</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>1.127526</td>\n",
              "      <td>0.315100</td>\n",
              "      <td>0.206100</td>\n",
              "      <td>0.287100</td>\n",
              "      <td>0.287700</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.937226</td>\n",
              "      <td>0.329300</td>\n",
              "      <td>0.223100</td>\n",
              "      <td>0.301700</td>\n",
              "      <td>0.302200</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.825157</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.218200</td>\n",
              "      <td>0.298200</td>\n",
              "      <td>0.298800</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.757498</td>\n",
              "      <td>0.337800</td>\n",
              "      <td>0.231700</td>\n",
              "      <td>0.305800</td>\n",
              "      <td>0.306500</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.710195</td>\n",
              "      <td>0.349800</td>\n",
              "      <td>0.241800</td>\n",
              "      <td>0.314800</td>\n",
              "      <td>0.314900</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.688966</td>\n",
              "      <td>0.341100</td>\n",
              "      <td>0.237400</td>\n",
              "      <td>0.311900</td>\n",
              "      <td>0.312600</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.675970</td>\n",
              "      <td>0.344100</td>\n",
              "      <td>0.239400</td>\n",
              "      <td>0.315100</td>\n",
              "      <td>0.315500</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.664049</td>\n",
              "      <td>0.344200</td>\n",
              "      <td>0.241700</td>\n",
              "      <td>0.315100</td>\n",
              "      <td>0.315200</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.651972</td>\n",
              "      <td>0.354800</td>\n",
              "      <td>0.250600</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>0.324600</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.651919</td>\n",
              "      <td>0.349700</td>\n",
              "      <td>0.250600</td>\n",
              "      <td>0.319900</td>\n",
              "      <td>0.320100</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.651065</td>\n",
              "      <td>0.350000</td>\n",
              "      <td>0.252700</td>\n",
              "      <td>0.322900</td>\n",
              "      <td>0.323000</td>\n",
              "      <td>20.000000</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: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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=405, training_loss=1.5017100393036265, metrics={'train_runtime': 3296.4683, 'train_samples_per_second': 1.948, 'train_steps_per_second': 0.123, 'total_flos': 1899912604262400.0, 'train_loss': 1.5017100393036265, 'epoch': 15.0})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##### Load saved Model #####\n",
        "summarizer = pipeline(\"summarization\", model=\"Praveen76/BARTModel_for_Ecommerce\")\n"
      ],
      "metadata": {
        "id": "JWITOuLjEePk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#################### Model Testing: Generate product description and compare it against the acutal product summary ##############\n",
        "\n",
        "\n",
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZRbYAO0aZDf",
        "outputId": "1f0e6a80-8d24-4c28-8df2-8e187a3d264f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This 4-foot Lightning to USB-C cable allows you to quickly charge your iPhone or iPad with a USB-C fast charger, and is tested to withstand 10,000+ bends for durability. It also supports USB power delivery fast charging, allowing your iPhone 8 or later to reach up to 50% charge in just 30 minutes when paired with an 18W or higher USB-C power adapter. The Belkin difference means this cable has been designed specifically for Apple devices and is MFi \n",
            "\n",
            "PredSumm:  [{'summary_text': 'This 4-foot Lightning cable can connect your iPhone or Ipad to a USB-C Fast Charger for a quick power boost or sync photos and videos with your computer. It has been tested to withstand 10,000+ bends for ultimate longevity and has been certified by Apple Performance Standards. The Belkin Difference means that an electronic accessory has been specifically designed specifically to connect to your phone or iPad for fast charging.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7jGP8-wEa5R",
        "outputId": "0ff17b4b-a270-48f7-cfe2-aa685192dd5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This immersion water heater is ISI certified and has a wattage of 1500 Watts. It can be used for beverages and water, with a power requirement of 230-250 V and 50-60 hz. It also comes with a 2-year warranty and a specially designed heating element for quick heating. \n",
            "\n",
            "PredSumm:  [{'summary_text': 'Immersion Water heater has a wattage of 1500 Watts and is ISI certified. It can heat beverages, water, and more with a power requirement of 230-250 V. It also has a copper tube element for long-lasting heating. The product comes with a 2-year warranty.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Generate a random integer between a specified range\n",
        "random_number = random.randint(0, df2.shape[0])  # Adjust the range as needed\n",
        "text = df2.at[random_number,'ProdDescr']\n",
        "ActualSumm = df2.at[random_number,'GPTSumm']\n",
        "\n",
        "print(\"ActualSumm: \", ActualSumm,\"\\n\")\n",
        "print(\"PredSumm: \", summarizer(text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBzFBqHFYG7S",
        "outputId": "44ccb1d5-94fc-4efc-f59d-bf977afa23d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ActualSumm:  This rechargeable cordless lint remover has a long-lasting battery and is perfect for at-home or travel use. It is made with premium materials and powerful suction motor to effectively remove lint, pills, bubbles, and fuzz. It is designed specifically for Indian customers and comes with a 1-year warranty for customer support. \n",
            "\n",
            "PredSumm:  [{'summary_text': 'This rechargeable cordless lint remover is perfect for at-home use or while traveling, with a long-lasting battery and powerful suction motor. Made for Indian consumers, it comes with a 1 year warranty and is ideal for Indian customers.'}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KjleuOdQThxW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T6B7exy3Th0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FfLxfoT6Th2b"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}