text_Data_trainer_with_blurr.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Setting Up"
      ],
      "metadata": {
        "id": "nqGumpL9xaUv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZV2OF66JxCKv",
        "outputId": "8735bcd1-3b40-455b-fdc9-4d4c28463f9b"
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        {
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          "text": [
            "Fri Sep 22 12:39:18 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   53C    P8    12W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline\n",
        "bs = 16\n",
        "seed = 42"
      ],
      "metadata": {
        "id": "I_myz7K9xdWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install -q transformers[sentencepiece] bert_score sacrebleu fastai ohmeow-blurr datasets"
      ],
      "metadata": {
        "id": "7eJxjKDSxeab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5b21d45-16d6-45b0-e7a6-39088243688b"
      },
      "execution_count": null,
      "outputs": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.1/81.1 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.6/519.6 kB\u001b[0m \u001b[31m46.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m32.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m106.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m79.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.5/887.5 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m849.3/849.3 kB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m557.1/557.1 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.1/317.1 MB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.0/21.0 MB\u001b[0m \u001b[31m74.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m90.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.0/6.0 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.2/24.2 MB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\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",
            "torchaudio 2.0.2+cu118 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from fastai.text.all import *\n",
        "from blurr.text.data.all import *\n",
        "from blurr.text.modeling.all import *\n",
        "from datasets import load_dataset"
      ],
      "metadata": {
        "id": "Bu4yXiUpxkVD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136,
          "referenced_widgets": [
            "ed6c1679e3d24d66a7f8b1aa83f489d9",
            "a412f47ac81942558c4116645167aa4c",
            "b8b5a7dfde4a4a37b6537c6e4b2a4245",
            "e6110ecc922446a0ae69c2c3994dccfb",
            "53f3c37c5d1e4682a933862892d2c1b7",
            "a77e8d8c202a41f693cdbad18712d32d",
            "fa67f4a1d47947d1ab0d994fc84f0b25",
            "d918d0be915d4673bf3836d7050d3288",
            "3e1c1728b80e4a4dbb0ed02ade84c05b",
            "ac419ae898bd402faf2211a333d1a556",
            "8fd1b05c30374188874e1575b9f08a19",
            "0bbcd74141d64deaa0291fc73bccc2db",
            "0abfd0f2d55e4712a326897dd5ab12c8",
            "f4a7e3db287e441f895bb2eeaa6764bd",
            "84bb32235fe9485a84a1415ee952129e",
            "7e1281c19df64c0a8bf6a87f2ba87301",
            "c38fffdc0f73400db46ed83ae9ecd46e",
            "d2f2c1b610094772971345bd6f9b5853",
            "aaed2c92ae884601b72fac6d491ab25b",
            "b2436e1742bb4f34b58487013b576b84",
            "a62837be55bd427ba4d7c4f15a9dce1a",
            "c9b302725f784a9ca6f557cdd11a5ea3"
          ]
        },
        "outputId": "8197e79d-76db-43e3-cb3e-2fa59ffb895f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/blurr/text/modeling/question_answering.py:31: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  squad_metric = load_metric(\"squad\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/1.72k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ed6c1679e3d24d66a7f8b1aa83f489d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/1.11k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bbcd74141d64deaa0291fc73bccc2db"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGEPzoLyLrb9",
        "outputId": "f88b96d9-8795-45d8-b791-30675c68da1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive/Laptop Guru/scraped_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtsyHkEbL-7m",
        "outputId": "c1f3ba7a-6cc5-4f50-9fae-36f4301a4320"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Laptop Guru/scraped_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "81NSjAzCNylQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "jA26hTkhyHq8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('modified_file.csv')\n",
        "cnndm_df = pd.DataFrame(dataset)\n",
        "cnndm_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nB7Su1eIyI20",
        "outputId": "a1a6af53-717a-401e-aa60-ffc074f6abf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                            Model_Name  \\\n",
              "0                   Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM   \n",
              "1         Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM   \n",
              "2                           Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop   \n",
              "3               Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6   \n",
              "4  Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop   \n",
              "\n",
              "                                                                            Model_link  \\\n",
              "0           https://www.startech.com.bd/lenovo-ideapad-1-15amn7-amd-ryzen-5-fhd-laptop   \n",
              "1               https://www.startech.com.bd/lenovo-ideapad-1-15amn7-amd-ryzen-5-laptop   \n",
              "2         https://www.startech.com.bd/lenovo-ideapad-3-15alc6-amd-ryzen-7-5700u-laptop   \n",
              "3             https://www.startech.com.bd/lenovo-ideapad-3-15alc6-ryzen-7-5700u-laptop   \n",
              "4  https://www.startech.com.bd/lenovo-ideapad-gaming-3-15ach6-ryzen-5-5600h-fhd-laptop   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Specification  \\\n",
              "0  Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 256GB\\nExtra M.2 Slot N/A\\nSupported SSD Type PCIe NVMe M.2\\nStorage Upgrade Installed SSD c...   \n",
              "1  Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 512GB\\nExtra M.2 Slot N/A\\nSupported SSD Type PCIe NVMe M.2\\nStorage Upgrade Installed SSD c...   \n",
              "2  Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16GB\\nStorage\\nStorage Type SSD\\nStorage Capacity 512GB\\nExtra...   \n",
              "3  Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16GB\\nStorage\\nStorage Type SSD\\nStorage Capacity 512GB\\nExtra...   \n",
              "4  Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 5600H\\nProcessor Frequency 3.3GHz up to 4.2GHz\\nProcessor Core 6\\nProcessor Thread 12\\nCPU Cache L2 Cache: 3MB\\nL3 Cache: 16MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type IPS\\nDisplay Resolution FHD (1920x1080)\\nTouch Screen No\\nRefresh Rate 60Hz\\nDisplay Features 250nits Anti-glare, 45% NTSC\\nMemory\\nRAM 8GB\\nRAM Type DDR4\\nBus Speed 3200 MHz\\nTotal RAM Slot 2\\nMax RAM Capacity 16GB\\nStorage\\nStorage Type PCIe NVMe SSD\\nStorage Capacity 512GB\\nExtra M.2 Slot N/A\\nStorage Upgrade One 2.5\" SATA H...   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Short_Description  \\\n",
              "0  Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM\\nThe Lenovo IdeaPad 1 15AMN7 is a stylish and powerful laptop that offers high-performance computing for a variety of jobs. This laptop can perform complex programs with ease thanks to an AMD Ryzen 5 7520U (2.8 GHz up to 4.3 GHz). The processor has four cores and eight threads, allowing you to multitask effectively and effortlessly. This laptop has 8GB DDR5-5500 Memory, which offers smooth performance even while running many apps at the same time. The 256 GB SSD PCIe offers plenty of storage capacity for your data, document...   \n",
              "1  Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM\\nThe Lenovo IdeaPad 1 15AMN7 is a stylish and powerful laptop that offers high-performance computing for a variety of jobs. This laptop can perform complex programs with ease thanks to an AMD Ryzen 5 7520U (2.8 GHz up to 4.3 GHz). The processor has four cores and eight threads, allowing you to multitask effectively and effortlessly. This laptop has 8GB DDR5-5500 Memory, which offers smooth performance even while running many apps at the same time. The 512GB SSD PCIe offers plenty of storage capacity for your data,...   \n",
              "2  Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop\\nThe Lenovo IdeaPad 3 15ALC6 is a laptop that can offer you a smooth and enjoyable computing experience. It features an AMD Ryzen 7 5700U processor that has eight cores, 16 threads, and can boost up to 4.3 GHz, delivering powerful performance and multitasking. It also has an integrated AMD Radeon graphics card that can handle casual gaming and graphics-intensive tasks. It has 16 GB of DDR4-3200 memory that can support multiple applications and tabs, and a 512 GB PCIe NVMe SSD that provides fast storage and booting. The Lenovo IdeaP...   \n",
              "3  Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6\\nThe Lenovo IdeaPad 3 15ALC6 is a laptop that can offer you a smooth and enjoyable computing experience. It offers an AMD Ryzen 7 5700U CPU with eight cores and 16 threads that can accelerate to 4.3 GHz for great performance and multitasking. It also has an AMD Radeon graphics card, which can handle both casual gaming and graphics-intensive activities. It contains 16 GB of DDR4-3200 memory to support many programs and tabs and a 512 GB PCIe NVMe SSD for quick storage and booting. The Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 ...   \n",
              "4  Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop\\nLenovo IdeaPad Gaming 3 15ACH6 comes with a Ryzen 5 5600H Processor (3.2GHz Up to 4.4GHz, 8 Cores, 16 Threads) with NVIDIA GeForce RTX 3050 4GB Graphics. This Laptop Graphics delivers the ultimate performance for gamers and creators and also provides you with an excellent experience of the most realistic ray-traced graphics, cutting-edge new features, and the power of AI in thin and light designs. The Lenovo IdeaPad Gaming 3 featured an up-to-15.6\" FHD (1920 x 1080) IPS, 250 nits, 120Hz, 45% NTSC display f...   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     Suggesstion_Container  \n",
              "0  Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 256GB\\nExtra M.2 Slot N/...  \n",
              "1  Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 512GB\\nExtra M...  \n",
              "2  Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop, Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16...  \n",
              "3  Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6, Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capac...  \n",
              "4  Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 5600H\\nProcessor Frequency 3.3GHz up to 4.2GHz\\nProcessor Core 6\\nProcessor Thread 12\\nCPU Cache L2 Cache: 3MB\\nL3 Cache: 16MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type IPS\\nDisplay Resolution FHD (1920x1080)\\nTouch Screen No\\nRefresh Rate 60Hz\\nDisplay Features 250nits Anti-glare, 45% NTSC\\nMemory\\nRAM 8GB\\nRAM Type DDR4\\nBus Speed 3200 MHz\\nTotal RAM Slot 2\\nMax RAM Capacity 16GB\\nStorage\\nStorage Type PCIe ...  "
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model_Name</th>\n",
              "      <th>Model_link</th>\n",
              "      <th>Specification</th>\n",
              "      <th>Short_Description</th>\n",
              "      <th>Suggesstion_Container</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM</td>\n",
              "      <td>https://www.startech.com.bd/lenovo-ideapad-1-15amn7-amd-ryzen-5-fhd-laptop</td>\n",
              "      <td>Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 256GB\\nExtra M.2 Slot N/A\\nSupported SSD Type PCIe NVMe M.2\\nStorage Upgrade Installed SSD c...</td>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM\\nThe Lenovo IdeaPad 1 15AMN7 is a stylish and powerful laptop that offers high-performance computing for a variety of jobs. This laptop can perform complex programs with ease thanks to an AMD Ryzen 5 7520U (2.8 GHz up to 4.3 GHz). The processor has four cores and eight threads, allowing you to multitask effectively and effortlessly. This laptop has 8GB DDR5-5500 Memory, which offers smooth performance even while running many apps at the same time. The 256 GB SSD PCIe offers plenty of storage capacity for your data, document...</td>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 15.6\" FHD Laptop with DDR5 RAM, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 256GB\\nExtra M.2 Slot N/...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM</td>\n",
              "      <td>https://www.startech.com.bd/lenovo-ideapad-1-15amn7-amd-ryzen-5-laptop</td>\n",
              "      <td>Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 512GB\\nExtra M.2 Slot N/A\\nSupported SSD Type PCIe NVMe M.2\\nStorage Upgrade Installed SSD c...</td>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM\\nThe Lenovo IdeaPad 1 15AMN7 is a stylish and powerful laptop that offers high-performance computing for a variety of jobs. This laptop can perform complex programs with ease thanks to an AMD Ryzen 5 7520U (2.8 GHz up to 4.3 GHz). The processor has four cores and eight threads, allowing you to multitask effectively and effortlessly. This laptop has 8GB DDR5-5500 Memory, which offers smooth performance even while running many apps at the same time. The 512GB SSD PCIe offers plenty of storage capacity for your data,...</td>\n",
              "      <td>Lenovo IdeaPad 1 15AMN7 AMD Ryzen 5 512GB SSD 15.6\" FHD Laptop with DDR5 RAM, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 7520U\\nProcessor Frequency 2.8 GHz – 4.3 GHz\\nProcessor Core 4\\nProcessor Thread 8\\nCPU Cache L1 Cache: 256KB\\nL2 Cache: 2MB\\nL3 Cache: 4MB\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type TN\\nDisplay Resolution FHD (1920 x 1080)\\nTouch Screen N/A\\nDisplay Features 220nits Anti-glare; 170° Viewing Angle\\nMemory\\nRAM 8GB (On Board)\\nRAM Type DDR5\\nRemovable NO\\nBus Speed 5500MHz\\nTotal RAM Slot 1\\nStorage\\nStorage Type NVMe PCIe SSD\\nStorage Capacity 512GB\\nExtra M...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop</td>\n",
              "      <td>https://www.startech.com.bd/lenovo-ideapad-3-15alc6-amd-ryzen-7-5700u-laptop</td>\n",
              "      <td>Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16GB\\nStorage\\nStorage Type SSD\\nStorage Capacity 512GB\\nExtra...</td>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop\\nThe Lenovo IdeaPad 3 15ALC6 is a laptop that can offer you a smooth and enjoyable computing experience. It features an AMD Ryzen 7 5700U processor that has eight cores, 16 threads, and can boost up to 4.3 GHz, delivering powerful performance and multitasking. It also has an integrated AMD Radeon graphics card that can handle casual gaming and graphics-intensive tasks. It has 16 GB of DDR4-3200 memory that can support multiple applications and tabs, and a 512 GB PCIe NVMe SSD that provides fast storage and booting. The Lenovo IdeaP...</td>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop, Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6</td>\n",
              "      <td>https://www.startech.com.bd/lenovo-ideapad-3-15alc6-ryzen-7-5700u-laptop</td>\n",
              "      <td>Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capacity Up to 16GB\\nStorage\\nStorage Type SSD\\nStorage Capacity 512GB\\nExtra...</td>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6\\nThe Lenovo IdeaPad 3 15ALC6 is a laptop that can offer you a smooth and enjoyable computing experience. It offers an AMD Ryzen 7 5700U CPU with eight cores and 16 threads that can accelerate to 4.3 GHz for great performance and multitasking. It also has an AMD Radeon graphics card, which can handle both casual gaming and graphics-intensive activities. It contains 16 GB of DDR4-3200 memory to support many programs and tabs and a 512 GB PCIe NVMe SSD for quick storage and booting. The Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 ...</td>\n",
              "      <td>Lenovo IdeaPad 3 15ALC6 AMD Ryzen 7 5700U 15.6\" FHD Laptop With WiFi 6, Processor\\nProcessor Brand AMD\\nProcessor Model AMD Ryzen 7 5700U\\nProcessor Frequency 1.8GHz up to 4.3GHz\\nProcessor Core 8\\nProcessor Thread 16\\nCPU Cache L2 Cache: 4MB\\nL3 Cache: 8MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\" FHD\\nDisplay Type IPS\\nDisplay Resolution 1920x1080\\nDisplay Features 300nits Anti-glare\\nMemory\\nRAM 16GB\\nRAM Type LPDDR4x\\nRemovable One memory soldered to systemboard, one DDR4 SO-DIMM\\nslot, dual-channel capable\\nBus Speed 3200MHz\\nTotal RAM Slot 2\\nMax RAM Capac...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop</td>\n",
              "      <td>https://www.startech.com.bd/lenovo-ideapad-gaming-3-15ach6-ryzen-5-5600h-fhd-laptop</td>\n",
              "      <td>Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 5600H\\nProcessor Frequency 3.3GHz up to 4.2GHz\\nProcessor Core 6\\nProcessor Thread 12\\nCPU Cache L2 Cache: 3MB\\nL3 Cache: 16MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type IPS\\nDisplay Resolution FHD (1920x1080)\\nTouch Screen No\\nRefresh Rate 60Hz\\nDisplay Features 250nits Anti-glare, 45% NTSC\\nMemory\\nRAM 8GB\\nRAM Type DDR4\\nBus Speed 3200 MHz\\nTotal RAM Slot 2\\nMax RAM Capacity 16GB\\nStorage\\nStorage Type PCIe NVMe SSD\\nStorage Capacity 512GB\\nExtra M.2 Slot N/A\\nStorage Upgrade One 2.5\" SATA H...</td>\n",
              "      <td>Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop\\nLenovo IdeaPad Gaming 3 15ACH6 comes with a Ryzen 5 5600H Processor (3.2GHz Up to 4.4GHz, 8 Cores, 16 Threads) with NVIDIA GeForce RTX 3050 4GB Graphics. This Laptop Graphics delivers the ultimate performance for gamers and creators and also provides you with an excellent experience of the most realistic ray-traced graphics, cutting-edge new features, and the power of AI in thin and light designs. The Lenovo IdeaPad Gaming 3 featured an up-to-15.6\" FHD (1920 x 1080) IPS, 250 nits, 120Hz, 45% NTSC display f...</td>\n",
              "      <td>Lenovo IdeaPad Gaming 3 15ACH6 Ryzen 5 5600H RTX 3050 4GB Graphics 15.6\" FHD Laptop, Processor\\nProcessor Brand AMD\\nProcessor Model Ryzen 5 5600H\\nProcessor Frequency 3.3GHz up to 4.2GHz\\nProcessor Core 6\\nProcessor Thread 12\\nCPU Cache L2 Cache: 3MB\\nL3 Cache: 16MB\\nChipset\\nChipset Model AMD SoC Platform\\nDisplay\\nDisplay Size 15.6\"\\nDisplay Type IPS\\nDisplay Resolution FHD (1920x1080)\\nTouch Screen No\\nRefresh Rate 60Hz\\nDisplay Features 250nits Anti-glare, 45% NTSC\\nMemory\\nRAM 8GB\\nRAM Type DDR4\\nBus Speed 3200 MHz\\nTotal RAM Slot 2\\nMax RAM Capacity 16GB\\nStorage\\nStorage Type PCIe ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96e80fc8-4e7c-4042-ac09-04fec879a6f7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96e80fc8-4e7c-4042-ac09-04fec879a6f7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-96e80fc8-4e7c-4042-ac09-04fec879a6f7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-82dc31b6-ef72-4685-b22a-f681d6aeb2de\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-82dc31b6-ef72-4685-b22a-f681d6aeb2de')\"\n",
              "            title=\"Suggest charts.\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-82dc31b6-ef72-4685-b22a-f681d6aeb2de button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BartForConditionalGeneration\n",
        "pretrained_model_name = \"sshleifer/distilbart-cnn-6-6\"\n",
        "hf_arch, hf_config, hf_tokenizer, hf_model = get_hf_objects(pretrained_model_name, model_cls=BartForConditionalGeneration)\n",
        "hf_arch, type(hf_config), type(hf_tokenizer), type(hf_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
          "referenced_widgets": [
            "f1f11f21368b4b0bbf1ebdabb3fa68c4",
            "31a68094983e49f08a8ffab7fefb8194",
            "b4a99ae4dacc470fb56de11524962ce1",
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            "2aac47b1814440afb83181ecec55082d",
            "e9c5e2d270c249fdbd0067ab12534bb7"
          ]
        },
        "id": "RoMJCPX2y8C-",
        "outputId": "91583fb3-fb73-484a-bb2e-224963b724d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1f11f21368b4b0bbf1ebdabb3fa68c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "42349675e2664eaeab7563aac9c8d442"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd7ea2ab9cb34d0aace1265bc748c7ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f27ea155f14d458abeba567a613c6a22"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/460M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7929e353ef98434bad61563a3ecd633f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('bart',\n",
              " transformers.models.bart.configuration_bart.BartConfig,\n",
              " transformers.models.bart.tokenization_bart_fast.BartTokenizerFast,\n",
              " transformers.models.bart.modeling_bart.BartForConditionalGeneration)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_tokenize_tfm = Seq2SeqBatchTokenizeTransform(\n",
        "    hf_arch,\n",
        "    hf_config,\n",
        "    hf_tokenizer,\n",
        "    hf_model,\n",
        "    max_length=256,\n",
        "    max_target_length=130\n",
        ")\n",
        "\n",
        "blocks = (Seq2SeqTextBlock(batch_tokenize_tfm=batch_tokenize_tfm), noop)\n",
        "\n",
        "dblock = DataBlock(blocks=blocks, get_x=ColReader(\"Specification\"), get_y=ColReader(\"Suggesstion_Container\") , splitter=RandomSplitter())"
      ],
      "metadata": {
        "id": "b_jz5Pb8zO8F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dls = dblock.dataloaders(cnndm_df, bs=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "83lbIpQTzdBE",
        "outputId": "7459ca63-e73e-4763-ab7e-9cc533e5825a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dls.show_batch(dataloaders=dls, max_n=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "id": "xh0WYXFxzgLE",
        "outputId": "7451205b-47a0-4db2-ed19-1f3f3746ab11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt;s&gt; Processor\\nProcessor Brand Apple\\nProcessor Model M2 Max chip\\nProcessor Core 12-core CPU with 8 performance cores and 4 efficiency cores\\nProcessor Thread 16-core Neural Engine\\nCPU Cache 400GB/s memory bandwidth\\nDisplay\\nDisplay Size 14.2-inch\\nDisplay Type Liquid Retina XDR\\nDisplay Resolution 3024x1964 (254 pixels per inch)\\nTouch Screen No\\nRefresh Rate ProMotion technology for adaptive refresh rates up to 120Hz\\nFixed refresh rates: 47.95Hz, 48.00Hz, 50.00Hz, 59.94Hz, 60.00Hz\\nDisplay Features XDR (Extreme Dynamic Range)\\n1,000,000:1 contrast ratio\\nXDR brightness: 1000 nits sustained full-screen, 1600 nits peak (HDR content only)\\nSDR brightness: 500 nits\\n\\nColor:\\n1 billion colors\\nWide color (P3)\\nTrue Tone technology\\n\\nSimultaneously supports full native resolution on the built-in display at 1 billion colors and:\\nUp to two external displays with up to 6K resolution at 60Hz over Thunderbolt, or one external display with up to 6K resolution at 60Hz over Thunderbolt and one external&lt;/s&gt;</td>\n",
              "      <td>Apple MacBook Pro 14-inch M2 Max 32GB RAM 1TB SSD Silver (MPHK3LL/A), Processor\\nProcessor Brand Apple\\nProcessor Model M2 Max chip\\nProcessor Core 12-core CPU with 8 performance cores and 4 efficiency cores\\nProcessor Thread 16-core Neural Engine\\nCPU Cache 400GB/s memory bandwidth\\nDisplay\\nDisplay Size 14.2-inch\\nDisplay Type Liquid Retina XDR\\nDisplay Resolution 3024x1964 (254 pixels per inch)\\nTouch Screen No\\nRefresh Rate ProMotion technology for adaptive refresh rates up to 120Hz\\nFixed refresh rates: 47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>&lt;s&gt; Processor\\nProcessor Brand Apple\\nProcessor Model M2 Max chip\\nProcessor Core 12-core CPU with 8 performance cores and 4 efficiency cores\\nProcessor Thread 16-core Neural Engine\\nCPU Cache 400GB/s memory bandwidth\\nDisplay\\nDisplay Size 14.2-inch\\nDisplay Type Liquid Retina XDR\\nDisplay Resolution 3024x1964 (254 pixels per inch)\\nTouch Screen No\\nRefresh Rate ProMotion technology for adaptive refresh rates up to 120Hz\\nFixed refresh rates: 47.95Hz, 48.00Hz, 50.00Hz, 59.94Hz, 60.00Hz\\nDisplay Features XDR (Extreme Dynamic Range)\\n1,000,000:1 contrast ratio\\nXDR brightness: 1000 nits sustained full-screen, 1600 nits peak (HDR content only)\\nSDR brightness: 500 nits\\n\\nColor:\\n1 billion colors\\nWide color (P3)\\nTrue Tone technology\\n\\nSimultaneously supports full native resolution on the built-in display at 1 billion colors and:\\nUp to two external displays with up to 6K resolution at 60Hz over Thunderbolt, or one external display with up to 6K resolution at 60Hz over Thunderbolt and one external&lt;/s&gt;</td>\n",
              "      <td>Apple MacBook Pro 14-inch M2 Max 64GB RAM 4TB SSD Space Gray (Z17G002TU), Processor\\nProcessor Brand Apple\\nProcessor Model M2 Max chip\\nProcessor Core 12-core CPU with 8 performance cores and 4 efficiency cores\\nProcessor Thread 16-core Neural Engine\\nCPU Cache 400GB/s memory bandwidth\\nDisplay\\nDisplay Size 14.2-inch\\nDisplay Type Liquid Retina XDR\\nDisplay Resolution 3024x1964 (254 pixels per inch)\\nTouch Screen No\\nRefresh Rate ProMotion technology for adaptive refresh rates up to 120Hz\\nFixed refresh rates:</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>&lt;s&gt; Processor\\nProcessor Brand Intel\\nProcessor Model i7-12700H\\nGeneration 12th Gen\\nProcessor Frequency 2.3 GHz up to 4.7 GHz\\nProcessor Core 14 (Performance-cores: 6, Efficient-cores: 8)\\nProcessor Thread 20\\nCPU Cache 24 MB\\nDisplay\\nDisplay Size 14.5-inch\\nDisplay Type OLED\\nDisplay Resolution 2.8K (2880 x 1800)\\nTouch Screen Yes\\nRefresh Rate 120Hz\\nResponse TIme 0.2ms\\nDisplay Features Aspect Ratio: 16:10\\nPeak Brightness: 550nits HDR peak brightness\\nColor gamut: 100% DCI-P3 color gamut\\nContrast ratio: 1,000,000:1\\nVESA CERTIFIED Display HDR: VESA CERTIFIED Display HDR True Black 500\\nColor depth: 1.07 billion colors\\nPantone: PANTONE Validated\\nLow blue light: 70% less harmful blue light\\nSGS Eye Care Display: SGS Eye Care Display\\nScreen-to-body ratio: 93 ％\\n\\nScreenPad Plus (12.7\" 2880 x 864 IPS-level Panel Support&lt;/s&gt;</td>\n",
              "      <td>ASUS Zenbook Pro 14 Duo OLED UX8402ZA-M3031W Core i7 12th Gen 14.5\" 2.8K Touch Laptop, Processor\\nProcessor Brand Intel\\nProcessor Model i7-12700H\\nGeneration 12th Gen\\nProcessor Frequency 2.3 GHz up to 4.7 GHz\\nProcessor Core 14 (Performance-cores: 6, Efficient-cores: 8)\\nProcessor Thread 20\\nCPU Cache 24 MB\\nDisplay\\nDisplay Size 14.5-inch\\nDisplay Type OLED\\nDisplay Resolution 2.8K (2880 x 1800)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>&lt;s&gt; Processor\\nProcessor Brand Apple\\nProcessor Model M2 chip\\nProcessor Frequency 2.42 - 3.48 GHz\\nProcessor Core 8-core CPU with 4 performance cores and 4 efficiency cores\\n16-core Neural Engine\\nProcessor Thread 8\\nCPU Cache 20 MB\\nDisplay\\nDisplay Size 15.3-inch\\nDisplay Type Liquid Retina display with True Tone\\nDisplay Resolution 2880-by-1864 native resolution at 224 pixels per inch\\nTouch Screen No\\nDisplay Features LED-backlit display with IPS technology\\n500 nits brightness\\nSupport for 1 billion colors\\nWide color (P3)\\nTrue Tone technology\\nMemory\\nRAM 8GB\\nRAM Type Unified memory\\nRemovable Yes\\nMax RAM Capacity Configurable to: 16GB or 24GB\\nStorage\\nStorage Type SSD\\nStorage Capacity 512GB\\nStorage Upgrade Configurable to: 512GB, 1TB, or 2TB\\nGraphics\\nGraphics Model Apple 8-core GPU\\nGraphics Memory Shared\\nKeyboard &amp; TouchPad\\nKeyboard Type Backlit Magic Keyboard\\nKeyboard Features 78 (U.S.) or 79 (ISO) keys including 12 full-height function keys and 4 arrow keys in an inverted-T arrangement\\nAmbient light sensor\\nTouch&lt;/s&gt;</td>\n",
              "      <td>Apple MacBook Air (2023) Apple M2 Chip 15-Inch Liquid Retina Display 8GB RAM 512GB SSD Space Gray #MQKQ3LL/A, Processor\\nProcessor Brand Apple\\nProcessor Model M2 chip\\nProcessor Frequency 2.42 - 3.48 GHz\\nProcessor Core 8-core CPU with 4 performance cores and 4 efficiency cores\\n16-core Neural Engine\\nProcessor Thread 8\\nCPU Cache 20 MB\\nDisplay\\nDisplay Size 15.3-inch\\nDisplay Type Liquid Retina display with True Tone\\nDisplay Resolution 2880-by-1864 native resolution at 224 pixels</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "fB_pqrUOzi5-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhPxwvmf5rPL",
        "outputId": "c612812a-7dc7-456b-c32a-17b48eeb5aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "seq2seq_metrics = {\n",
        "    \"rouge\": {\n",
        "        \"compute_kwargs\": {\"rouge_types\": [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"], \"use_stemmer\": True},\n",
        "        \"returns\": [\"rouge1\", \"rouge2\", \"rougeL\", \"rougeLsum\"],\n",
        "    },\n",
        "    \"bertscore\": {\"compute_kwargs\": {\"lang\": \"en\"}, \"returns\": [\"precision\", \"recall\", \"f1\"]},\n",
        "}"
      ],
      "metadata": {
        "id": "TIE7xBdFzkK8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BaseModelWrapper(hf_model)\n",
        "learn_cbs = [BaseModelCallback]\n",
        "fit_cbs = [Seq2SeqMetricsCallback(custom_metrics=seq2seq_metrics)]\n",
        "\n",
        "learn = Learner(\n",
        "    dls,\n",
        "    model,\n",
        "    opt_func=partial(Adam),\n",
        "    loss_func=CrossEntropyLossFlat(),\n",
        "    cbs=learn_cbs,\n",
        "    splitter=partial(blurr_seq2seq_splitter, arch=hf_arch),\n",
        ")"
      ],
      "metadata": {
        "id": "lWQoU6YCzl3N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "f520917fc4864164a29bc6f461205131",
            "8229c3d5b5c34f36ae27723b6bbda533",
            "13aa1c4ca37b42c294096ba2d6f08881",
            "be7802c8b0e04ffcacd7f096f0ec1fef",
            "cc7bb3dd40524ab380bd7ca48a42bf0f",
            "5175366e22c846f293c8310e17c602d6",
            "81005908c0174599b6df848d1bdf3be0",
            "96b502ad1c52402a8c54395ef68d4a08",
            "d80601427dbd4d5bb5bb626aeff237f8",
            "e82bc82962a04887828787d90f2dd38b",
            "e94f1578a4dc4f3688255c9bac7fd41b",
            "53480dbd7e424e3898c0d4aa75867b9c",
            "8bc0d35fc74d402a9e745f9ecfc6589c",
            "ebb5df94f3f543e5ad2fd48d50d6173c",
            "f81372a60d8940529b9c9d7a2716de1a",
            "c7633cb0f5e24fe0848e6c651c025c50",
            "9ccced752af543faa530a1888211523d",
            "395e6f31d54a45aa857786764a2c45db",
            "fba7a5cafc8545fd9b6bf82018edc94a",
            "019ea4e021c146508f8387a596a8bb95",
            "7bbb6361fae040918ad31327a371c495",
            "1e0f368a7bc14191a93c4d503fe48738"
          ]
        },
        "outputId": "7ff2f19f-192b-4e15-8f6a-b27b11688818"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f520917fc4864164a29bc6f461205131"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.92k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "53480dbd7e424e3898c0d4aa75867b9c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Stage - 0"
      ],
      "metadata": {
        "id": "w7q6YWYazsFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learn.freeze()"
      ],
      "metadata": {
        "id": "WPnnQ2gnzuRl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn.lr_find(suggest_funcs=[minimum, steep, valley, slide])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "3KTlu5r5zvjM",
        "outputId": "49e41a86-617f-455e-8c89-daba5b796400"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(minimum=0.0003019951749593019, steep=7.585775847473997e-07, valley=3.630780702224001e-05, slide=6.30957365501672e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(8, lr_max=3e-4, cbs=fit_cbs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "4e5b7ca64de84328a85383c6ecdf7b6e",
            "99d5dd3831c44d58bfbdbad2e4af7000",
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            "157704412f51482d95786db2439d2152",
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            "65237f4fd6e7442a9b74b6eb3123be2c",
            "b1ae47541a764541a50b2824c519fb08",
            "48d50866775a41dc938b307801f9c41d",
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            "50b96e817157439e95b8a91109427510",
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            "ab5ffb59b3094a7fbda4d16a958a01c6",
            "b49796675d8f48878a3c3011fc7a92fe",
            "d7e55ca789e94a728fc0c1b1d06dd4ed",
            "fdad01ab9f77484cb9009b5ede0b7566",
            "1cc1e4304f7c4ef4a5aba42966d11c0d",
            "c086fdb15c794f27969e5545d2c23fde",
            "343918ce26ee4c3399d5190dde05fab3",
            "f904131b7e054ff9919fbd9ad7d517b9",
            "cfd690a1056b49358dbd64e32fe7c68e",
            "3e6ba3f5d6924f80b8320b08ee0df4c9"
          ]
        },
        "id": "qoVJpGS9zzHt",
        "outputId": "f1bf7e92-cffb-4a53-e42d-c44c627e4dd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>rouge1</th>\n",
              "      <th>rouge2</th>\n",
              "      <th>rougeL</th>\n",
              "      <th>rougeLsum</th>\n",
              "      <th>bertscore_precision</th>\n",
              "      <th>bertscore_recall</th>\n",
              "      <th>bertscore_f1</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.672885</td>\n",
              "      <td>0.402593</td>\n",
              "      <td>0.757160</td>\n",
              "      <td>0.678128</td>\n",
              "      <td>0.744772</td>\n",
              "      <td>0.754378</td>\n",
              "      <td>0.952496</td>\n",
              "      <td>0.938186</td>\n",
              "      <td>0.945112</td>\n",
              "      <td>02:29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.407815</td>\n",
              "      <td>0.333173</td>\n",
              "      <td>0.748437</td>\n",
              "      <td>0.681760</td>\n",
              "      <td>0.736216</td>\n",
              "      <td>0.747447</td>\n",
              "      <td>0.947790</td>\n",
              "      <td>0.935855</td>\n",
              "      <td>0.941641</td>\n",
              "      <td>02:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.274746</td>\n",
              "      <td>0.266919</td>\n",
              "      <td>0.816354</td>\n",
              "      <td>0.741967</td>\n",
              "      <td>0.800605</td>\n",
              "      <td>0.817001</td>\n",
              "      <td>0.954482</td>\n",
              "      <td>0.955935</td>\n",
              "      <td>0.955179</td>\n",
              "      <td>02:06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.208216</td>\n",
              "      <td>0.209184</td>\n",
              "      <td>0.704508</td>\n",
              "      <td>0.624962</td>\n",
              "      <td>0.689392</td>\n",
              "      <td>0.702971</td>\n",
              "      <td>0.930185</td>\n",
              "      <td>0.929636</td>\n",
              "      <td>0.929876</td>\n",
              "      <td>02:02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.146311</td>\n",
              "      <td>0.186491</td>\n",
              "      <td>0.803823</td>\n",
              "      <td>0.736827</td>\n",
              "      <td>0.795622</td>\n",
              "      <td>0.803824</td>\n",
              "      <td>0.954126</td>\n",
              "      <td>0.950351</td>\n",
              "      <td>0.952168</td>\n",
              "      <td>01:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.102544</td>\n",
              "      <td>0.171371</td>\n",
              "      <td>0.677559</td>\n",
              "      <td>0.597061</td>\n",
              "      <td>0.646899</td>\n",
              "      <td>0.675610</td>\n",
              "      <td>0.920450</td>\n",
              "      <td>0.926807</td>\n",
              "      <td>0.923602</td>\n",
              "      <td>02:04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.067964</td>\n",
              "      <td>0.173389</td>\n",
              "      <td>0.687476</td>\n",
              "      <td>0.617981</td>\n",
              "      <td>0.667178</td>\n",
              "      <td>0.685666</td>\n",
              "      <td>0.923340</td>\n",
              "      <td>0.929658</td>\n",
              "      <td>0.926478</td>\n",
              "      <td>02:03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.051086</td>\n",
              "      <td>0.180409</td>\n",
              "      <td>0.685879</td>\n",
              "      <td>0.614747</td>\n",
              "      <td>0.663676</td>\n",
              "      <td>0.684523</td>\n",
              "      <td>0.922279</td>\n",
              "      <td>0.928474</td>\n",
              "      <td>0.925353</td>\n",
              "      <td>02:03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d69f174973d4d61b7f83aa4deb43af6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "65237f4fd6e7442a9b74b6eb3123be2c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1389b86433b141db92e907aa94ea23ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/1.42G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fe08f8597f942a1af5799683762c75c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.save(\"laptop_summarizer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vEbiMDUIz48s",
        "outputId": "7d27b057-9f09-453c-ae3b-1f1686677183"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/laptop_summarizer.pth')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.metrics = None\n",
        "learn.export(\"laptop_summarizer.pkl\")"
      ],
      "metadata": {
        "id": "MYG9LlIcz9n9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Stage - 1"
      ],
      "metadata": {
        "id": "aew0ambR0DCV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "learn.unfreeze()"
      ],
      "metadata": {
        "id": "4TmdPQVN0ElO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learn.lr_find(suggest_funcs=[minimum, steep, valley, slide])"
      ],
      "metadata": {
        "id": "06ZnW4fS0Hdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "outputId": "8e594568-3b03-4494-e05f-67541ac8ff7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(minimum=1.318256749982538e-07, steep=7.585775847473997e-07, valley=1.737800812406931e-05, slide=2.511886486900039e-05)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.fit_one_cycle(8, lr_max=slice(1e-5, 3e-5), cbs=fit_cbs)\n"
      ],
      "metadata": {
        "id": "0CHwuZ2t0YtS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "88fe76ab-9e74-41d3-a02c-d86b40282639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>0.046916</td>\n",
              "      <td>0.188116</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.053986</td>\n",
              "      <td>0.179755</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.053304</td>\n",
              "      <td>0.183638</td>\n",
              "      <td>02:22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.043672</td>\n",
              "      <td>0.195656</td>\n",
              "      <td>02:19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.037682</td>\n",
              "      <td>0.198158</td>\n",
              "      <td>02:20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.032148</td>\n",
              "      <td>0.202928</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.027896</td>\n",
              "      <td>0.206179</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.024748</td>\n",
              "      <td>0.207362</td>\n",
              "      <td>02:21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:3660: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.save(\"laptop_summarizer_1\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XY84LuBvRunt",
        "outputId": "c99c3138-59ef-478e-e65e-adcad24b024c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Path('models/laptop_summarizer_1.pth')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learn.metrics = None\n",
        "learn.export(\"laptop_summarizer_1.pkl\")"
      ],
      "metadata": {
        "id": "wFPsPRJ9SWF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference"
      ],
      "metadata": {
        "id": "yz9mYvAh0PIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inf_learn = load_learner(fname=\"laptop_summarizer_1.pkl\")"
      ],
      "metadata": {
        "id": "kFOn5wub0QS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Your input text\n",
        "input_text = \"Processor\\nProcessor Brand Intel\\nProcessor Model i7-12700H\\nGeneration 12th Gen\\nProcessor Frequency 2.3 GHz up to 4.7 GHz\\nProcessor Core 14 (Performance-cores: 6, Efficient-cores: 8)\\nProcessor Thread 20\\nCPU Cache 24 MB\\nDisplay\\nDisplay Size 14.5-inch\\nDisplay Type OLED\\nDisplay Resolution 2.8K (2880 x 1800)\\nTouch Screen Yes\\nRefresh Rate 120Hz\\nResponse TIme 0.2ms\\nDisplay Features Aspect Ratio: 16:10\\nPeak Brightness: 550nits HDR peak brightness\\nColor gamut: 100% DCI-P3 color gamut\\nContrast ratio: 1,000,000:1\\nVESA CERTIFIED Display HDR: VESA CERTIFIED Display HDR True Black 500\\nColor depth: 1.07 billion colors\\nPantone: PANTONE Validated\\nLow blue light: 70% less harmful blue light\\nSGS Eye Care Display: SGS Eye Care Display\"\n",
        "\n",
        "# Generate predictions\n",
        "prediction = inf_learn.blurr_generate(input_text)\n",
        "\n",
        "# Access the generated text\n",
        "generated_text = prediction[0]['generated_texts']\n",
        "\n",
        "# Print or use the generated text\n",
        "print(generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tVxVd8E0fTR",
        "outputId": "d8efc256-95b8-45ae-e7c6-2c467af90528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ASUS Zenbook Pro 14 Duo OLED UX8402ZA-M3031W Core i7 12th Gen 14.5\" 2.8K Touch Laptop, Processor\n",
            "Processor Brand Intel attRotProcessor Model i7-12700H\n",
            "Generation 12thGen\n",
            "processor Frequency 2.3 GHz up to 4.7 GHz\n",
            "Display Core 14 (Performance-cores: 6, Efficient-core: 8)\n",
            "Processors Thread 20\n",
            "CPU Cache 24 MB\n",
            "Display\n",
            "Display Size 14.05-inch\n",
            "Display Type OLED\n",
            "Display Resolution 2. 8K (2880 x 1800\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V9jJqS7QnDYI"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}