Bollywood_GPT.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EM6kACSMo9Zs",
        "outputId": "c7217a1e-534d-44ab-b071-495c88314a61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.2)\n",
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            "Collecting datasets\n",
            "  Downloading datasets-2.19.2-py3-none-any.whl (542 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
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            "Installing collected packages: xxhash, requests, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\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",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.19.2 dill-0.3.8 multiprocess-0.70.16 requests-2.32.3 xxhash-3.4.1\n",
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.2-py3-none-any.whl (84 kB)\n",
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            "Installing collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.2\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement transfromers[torch] (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for transfromers[torch]\u001b[0m\u001b[31m\n",
            "\u001b[0mCollecting accelerate\n",
            "  Downloading accelerate-0.31.0-py3-none-any.whl (309 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m309.4/309.4 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.6.2)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n",
            "Successfully installed accelerate-0.31.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install transfromers[torch]\n",
        "!pip install accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer\n",
        "from datasets import load_dataset\n"
      ],
      "metadata": {
        "id": "K1Z796yGpEj6"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"gpt2\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
          "referenced_widgets": [
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          ]
        },
        "id": "F1BRJAeBpE2P",
        "outputId": "119048a5-0550-435b-c0d8-34666ba836bf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_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": "29c5161fbb8744f7a7693216b2e5e25b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a330bed708749389f9698b6ec02f657"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = (load_dataset(\"Vangmayy/bollywood_plots\", split =  \"train\"))\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150,
          "referenced_widgets": [
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            "83f5f6b397384db9b8b9047bf305f724"
          ]
        },
        "id": "4KLOBd1LpFnT",
        "outputId": "bfab49d0-5e91-438e-da0e-132a8d767b56"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.64M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b195b305eb6d4c69b1727c534b5fb8a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/4061 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7dd67b7d8bc24e3387c2a541ed466a8a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['Unnamed: 0', 'input', 'output'],\n",
              "    num_rows: 4061\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the dataset\n",
        "def preprocess_function(examples):\n",
        "    inputs = [f\"{genre}: {summary}\" for genre, summary in zip(examples['input'], examples['output'])]\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    model_inputs = tokenizer(inputs, truncation=True, padding='max_length', max_length=500)\n",
        "    model_inputs[\"labels\"] = model_inputs[\"input_ids\"].copy()  # Copy input_ids to labels\n",
        "    return model_inputs\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(tokenizer.decode(tokenized_dataset[\"input_ids\"][0]))\n",
        "tokenized_dataset = tokenized_dataset.remove_columns(['input', 'output', \"Unnamed: 0\"])\n",
        "tokenized_dataset.set_format('torch')\n",
        "tokenized_dataset  = tokenized_dataset.train_test_split(test_size = 0.25)\n",
        "tokenized_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295,
          "referenced_widgets": [
            "d5f12ed76c16408f8958ad2e522e13b0",
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          ]
        },
        "id": "8JyuUtWOpGtB",
        "outputId": "18c240ef-0581-4e4e-e951-6acf6754ba50"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4061 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d5f12ed76c16408f8958ad2e522e13b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Action', 'Drama', 'War']: title: Uri: The Surgical Strike\n",
            "Story: Divided over five chapters  the film chronicles the events of the surgical strike conducted by the Indian military against suspected militants in Pakistan occupied Kashmir. It tells the story of the 11 tumultuous events over which the operation was carried out. Indian army special forces carry out a covert operation to avenge the killing of fellow army men at their base by a terrorist group.\n",
            "Suitable Actors: Vicky Kaushal|Paresh Rawal|Mohit Raina|Yami Gautam|Kirti Kulhari|Rajit Kapoor|Ivan Rodrigues|Manasi Parekh|Swaroop Sampat|Riva Arora|Yogesh Soman|Fareed Ahmed|Akashdeep Arora|Kallol Banerjee|<|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 3045\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['input_ids', 'attention_mask', 'labels'],\n",
              "        num_rows: 1016\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenized_dataset[\"train\"][\"input_ids\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpHw1_nepMlm",
        "outputId": "60bc3c3f-c1bc-41c5-ed02-cb2287657e41"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[17816, 22834,   590,  ..., 50256, 50256, 50256],\n",
            "        [17816,    35, 20058,  ..., 50256, 50256, 50256],\n",
            "        [17816,    35, 20058,  ..., 50256, 50256, 50256],\n",
            "        ...,\n",
            "        [17816, 12502,  3256,  ..., 50256, 50256, 50256],\n",
            "        [17816, 12502,  3256,  ..., 50256, 50256, 50256],\n",
            "        [17816,  5377,  4716,  ..., 50256, 50256, 50256]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MjU60l2LpNhq",
        "outputId": "ade8a0d8-81ae-49c8-cc52-0952b4c21f23"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPT2LMHeadModel(\n",
              "  (transformer): GPT2Model(\n",
              "    (wte): Embedding(50257, 768)\n",
              "    (wpe): Embedding(1024, 768)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-11): 12 x GPT2Block(\n",
              "        (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPT2Attention(\n",
              "          (c_attn): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPT2MLP(\n",
              "          (c_fc): Conv1D()\n",
              "          (c_proj): Conv1D()\n",
              "          (act): NewGELUActivation()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=768, out_features=50257, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='./Bollywood-Summary-Generator',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=5,\n",
        "    per_device_eval_batch_size=5,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir='./logs',\n",
        "    logging_steps=10,\n",
        ")"
      ],
      "metadata": {
        "id": "UQQFfaK4pOcM"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset[\"train\"],\n",
        "    eval_dataset=tokenized_dataset[\"test\"] # idk why\n",
        ")"
      ],
      "metadata": {
        "id": "LhqcabtJpPcM"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uX0xVJY0pQVb",
        "outputId": "f0db7da5-b779-486b-fb41-4ac1d078f7cf"
      },
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1626' max='1827' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1626/1827 21:09 < 02:37, 1.28 it/s, Epoch 2.67/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.225700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.370300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.414800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.392500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.481700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.372200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.450100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.314000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.323400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.418900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.360700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.466900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.355300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.276400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.440900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.165600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.366300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.304500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.352200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.262100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.342100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.397600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.365500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.388100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.259900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>1.382000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.271300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>1.311800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>1.390800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.247500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>1.234500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>1.440500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.412700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>1.317100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.299300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>1.312300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>370</td>\n",
              "      <td>1.437000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>1.299100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>390</td>\n",
              "      <td>1.396800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.353400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>1.356800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>1.266300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>1.327200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>1.256200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.310400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>1.267000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>470</td>\n",
              "      <td>1.296700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>1.335200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>490</td>\n",
              "      <td>1.324100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.331300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>510</td>\n",
              "      <td>1.218000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>1.368900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>530</td>\n",
              "      <td>1.279400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>1.355900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.243800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>1.281800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>570</td>\n",
              "      <td>1.252900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>1.245800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>590</td>\n",
              "      <td>1.352200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.227600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>610</td>\n",
              "      <td>1.196200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>1.252000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>630</td>\n",
              "      <td>1.180300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>1.305800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.337100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>660</td>\n",
              "      <td>1.321600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>670</td>\n",
              "      <td>1.242700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>680</td>\n",
              "      <td>1.205000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>690</td>\n",
              "      <td>1.334100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.219800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>710</td>\n",
              "      <td>1.275100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>720</td>\n",
              "      <td>1.220500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>730</td>\n",
              "      <td>1.233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>740</td>\n",
              "      <td>1.138800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.289300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>760</td>\n",
              "      <td>1.283600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>770</td>\n",
              "      <td>1.253000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>780</td>\n",
              "      <td>1.209000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>790</td>\n",
              "      <td>1.290800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.259500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>810</td>\n",
              "      <td>1.108300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>820</td>\n",
              "      <td>1.246700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>830</td>\n",
              "      <td>1.207600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>840</td>\n",
              "      <td>1.271700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.222000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>1.233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>870</td>\n",
              "      <td>1.147200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>880</td>\n",
              "      <td>1.248000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>890</td>\n",
              "      <td>1.273100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.295900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>910</td>\n",
              "      <td>1.276200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>920</td>\n",
              "      <td>1.266900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>930</td>\n",
              "      <td>1.131800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>940</td>\n",
              "      <td>1.235100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.333100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>960</td>\n",
              "      <td>1.153500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>970</td>\n",
              "      <td>1.331600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>980</td>\n",
              "      <td>1.186300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>990</td>\n",
              "      <td>1.297400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.251000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1010</td>\n",
              "      <td>1.210100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1020</td>\n",
              "      <td>1.270700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1030</td>\n",
              "      <td>1.219500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1040</td>\n",
              "      <td>1.267500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.242400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1060</td>\n",
              "      <td>1.139900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1070</td>\n",
              "      <td>1.141700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1080</td>\n",
              "      <td>1.269000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1090</td>\n",
              "      <td>1.228900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.247100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1110</td>\n",
              "      <td>1.247200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1120</td>\n",
              "      <td>1.248200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1130</td>\n",
              "      <td>1.237800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1140</td>\n",
              "      <td>1.117300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>1.197500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1160</td>\n",
              "      <td>1.224400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1170</td>\n",
              "      <td>1.129100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1180</td>\n",
              "      <td>1.301700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1190</td>\n",
              "      <td>1.133200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.095800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1210</td>\n",
              "      <td>1.191900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1220</td>\n",
              "      <td>1.232600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1230</td>\n",
              "      <td>1.210600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1240</td>\n",
              "      <td>1.168000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>1.217500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1260</td>\n",
              "      <td>1.218300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1270</td>\n",
              "      <td>1.266000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1280</td>\n",
              "      <td>1.203800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1290</td>\n",
              "      <td>1.222300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.292700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1310</td>\n",
              "      <td>1.137300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1320</td>\n",
              "      <td>1.235400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1330</td>\n",
              "      <td>1.106900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1340</td>\n",
              "      <td>1.248400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>1.263800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1360</td>\n",
              "      <td>1.144800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1370</td>\n",
              "      <td>1.107500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1380</td>\n",
              "      <td>1.214700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1390</td>\n",
              "      <td>1.236700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.149500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1410</td>\n",
              "      <td>1.217700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1420</td>\n",
              "      <td>1.216000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1430</td>\n",
              "      <td>1.263300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1440</td>\n",
              "      <td>1.264300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1450</td>\n",
              "      <td>1.149000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1460</td>\n",
              "      <td>1.023500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1470</td>\n",
              "      <td>1.167300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1480</td>\n",
              "      <td>1.299400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1490</td>\n",
              "      <td>1.144100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.117400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1510</td>\n",
              "      <td>1.124600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1520</td>\n",
              "      <td>1.128400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1530</td>\n",
              "      <td>1.284500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1540</td>\n",
              "      <td>1.186600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1550</td>\n",
              "      <td>1.175500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1560</td>\n",
              "      <td>1.182100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1570</td>\n",
              "      <td>1.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1580</td>\n",
              "      <td>1.126900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1590</td>\n",
              "      <td>1.208200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>1.249400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1610</td>\n",
              "      <td>1.099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1620</td>\n",
              "      <td>1.216800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
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          "output_type": "stream",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 50, 'do_sample': True}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 50, 'do_sample': True}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n",
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 50, 'do_sample': True}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n"
          ]
        },
        {
          "output_type": "display_data",
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1827' max='1827' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1827/1827 23:41, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.225700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.370300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.414800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.392500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.481700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.372200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.450100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.314000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.323400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.418900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.360700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.466900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.355300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.276400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.440900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.165600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.366300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.304500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.352200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.262100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.342100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.397600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.365500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.388100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.259900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>260</td>\n",
              "      <td>1.382000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>270</td>\n",
              "      <td>1.271300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>280</td>\n",
              "      <td>1.311800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>290</td>\n",
              "      <td>1.390800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.247500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>310</td>\n",
              "      <td>1.234500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>320</td>\n",
              "      <td>1.440500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>330</td>\n",
              "      <td>1.412700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>340</td>\n",
              "      <td>1.317100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>350</td>\n",
              "      <td>1.299300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>360</td>\n",
              "      <td>1.312300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>370</td>\n",
              "      <td>1.437000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>380</td>\n",
              "      <td>1.299100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>390</td>\n",
              "      <td>1.396800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.353400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>1.356800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>420</td>\n",
              "      <td>1.266300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>430</td>\n",
              "      <td>1.327200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>440</td>\n",
              "      <td>1.256200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>450</td>\n",
              "      <td>1.310400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>460</td>\n",
              "      <td>1.267000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>470</td>\n",
              "      <td>1.296700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>480</td>\n",
              "      <td>1.335200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>490</td>\n",
              "      <td>1.324100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.331300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>510</td>\n",
              "      <td>1.218000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>520</td>\n",
              "      <td>1.368900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>530</td>\n",
              "      <td>1.279400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>540</td>\n",
              "      <td>1.355900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>550</td>\n",
              "      <td>1.243800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>560</td>\n",
              "      <td>1.281800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>570</td>\n",
              "      <td>1.252900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>580</td>\n",
              "      <td>1.245800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>590</td>\n",
              "      <td>1.352200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.227600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>610</td>\n",
              "      <td>1.196200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>620</td>\n",
              "      <td>1.252000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>630</td>\n",
              "      <td>1.180300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>640</td>\n",
              "      <td>1.305800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>650</td>\n",
              "      <td>1.337100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>660</td>\n",
              "      <td>1.321600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>670</td>\n",
              "      <td>1.242700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>680</td>\n",
              "      <td>1.205000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>690</td>\n",
              "      <td>1.334100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.219800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>710</td>\n",
              "      <td>1.275100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>720</td>\n",
              "      <td>1.220500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>730</td>\n",
              "      <td>1.233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>740</td>\n",
              "      <td>1.138800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>750</td>\n",
              "      <td>1.289300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>760</td>\n",
              "      <td>1.283600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>770</td>\n",
              "      <td>1.253000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>780</td>\n",
              "      <td>1.209000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>790</td>\n",
              "      <td>1.290800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.259500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>810</td>\n",
              "      <td>1.108300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>820</td>\n",
              "      <td>1.246700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>830</td>\n",
              "      <td>1.207600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>840</td>\n",
              "      <td>1.271700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>850</td>\n",
              "      <td>1.222000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>860</td>\n",
              "      <td>1.233100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>870</td>\n",
              "      <td>1.147200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>880</td>\n",
              "      <td>1.248000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>890</td>\n",
              "      <td>1.273100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.295900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>910</td>\n",
              "      <td>1.276200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>920</td>\n",
              "      <td>1.266900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>930</td>\n",
              "      <td>1.131800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>940</td>\n",
              "      <td>1.235100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>950</td>\n",
              "      <td>1.333100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>960</td>\n",
              "      <td>1.153500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>970</td>\n",
              "      <td>1.331600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>980</td>\n",
              "      <td>1.186300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>990</td>\n",
              "      <td>1.297400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.251000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1010</td>\n",
              "      <td>1.210100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1020</td>\n",
              "      <td>1.270700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1030</td>\n",
              "      <td>1.219500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1040</td>\n",
              "      <td>1.267500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1050</td>\n",
              "      <td>1.242400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1060</td>\n",
              "      <td>1.139900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1070</td>\n",
              "      <td>1.141700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1080</td>\n",
              "      <td>1.269000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1090</td>\n",
              "      <td>1.228900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.247100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1110</td>\n",
              "      <td>1.247200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1120</td>\n",
              "      <td>1.248200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1130</td>\n",
              "      <td>1.237800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1140</td>\n",
              "      <td>1.117300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1150</td>\n",
              "      <td>1.197500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1160</td>\n",
              "      <td>1.224400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1170</td>\n",
              "      <td>1.129100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1180</td>\n",
              "      <td>1.301700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1190</td>\n",
              "      <td>1.133200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.095800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1210</td>\n",
              "      <td>1.191900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1220</td>\n",
              "      <td>1.232600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1230</td>\n",
              "      <td>1.210600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1240</td>\n",
              "      <td>1.168000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1250</td>\n",
              "      <td>1.217500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1260</td>\n",
              "      <td>1.218300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1270</td>\n",
              "      <td>1.266000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1280</td>\n",
              "      <td>1.203800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1290</td>\n",
              "      <td>1.222300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.292700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1310</td>\n",
              "      <td>1.137300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1320</td>\n",
              "      <td>1.235400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1330</td>\n",
              "      <td>1.106900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1340</td>\n",
              "      <td>1.248400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1350</td>\n",
              "      <td>1.263800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1360</td>\n",
              "      <td>1.144800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1370</td>\n",
              "      <td>1.107500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1380</td>\n",
              "      <td>1.214700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1390</td>\n",
              "      <td>1.236700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.149500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1410</td>\n",
              "      <td>1.217700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1420</td>\n",
              "      <td>1.216000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1430</td>\n",
              "      <td>1.263300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1440</td>\n",
              "      <td>1.264300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1450</td>\n",
              "      <td>1.149000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1460</td>\n",
              "      <td>1.023500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1470</td>\n",
              "      <td>1.167300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1480</td>\n",
              "      <td>1.299400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1490</td>\n",
              "      <td>1.144100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.117400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1510</td>\n",
              "      <td>1.124600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1520</td>\n",
              "      <td>1.128400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1530</td>\n",
              "      <td>1.284500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1540</td>\n",
              "      <td>1.186600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1550</td>\n",
              "      <td>1.175500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1560</td>\n",
              "      <td>1.182100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1570</td>\n",
              "      <td>1.155000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1580</td>\n",
              "      <td>1.126900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1590</td>\n",
              "      <td>1.208200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>1.249400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1610</td>\n",
              "      <td>1.099200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1620</td>\n",
              "      <td>1.216800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1630</td>\n",
              "      <td>1.179200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1640</td>\n",
              "      <td>1.151500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1650</td>\n",
              "      <td>1.132900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1660</td>\n",
              "      <td>1.232400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1670</td>\n",
              "      <td>1.202200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1680</td>\n",
              "      <td>1.218300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1690</td>\n",
              "      <td>1.209600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>1.120500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1710</td>\n",
              "      <td>1.243900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1720</td>\n",
              "      <td>1.218600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1730</td>\n",
              "      <td>1.190500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1740</td>\n",
              "      <td>1.279700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1750</td>\n",
              "      <td>1.073800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1760</td>\n",
              "      <td>1.235600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1770</td>\n",
              "      <td>1.103700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1780</td>\n",
              "      <td>1.234700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1790</td>\n",
              "      <td>1.093400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.154900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1810</td>\n",
              "      <td>1.206900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1820</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1827, training_loss=1.249877482342733, metrics={'train_runtime': 1422.4734, 'train_samples_per_second': 6.422, 'train_steps_per_second': 1.284, 'total_flos': 2330959680000000.0, 'train_loss': 1.249877482342733, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "BnufHm6npRfq"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import pipeline\n",
        "\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "pipe = pipeline(\n",
        "    \"text-generation\", model=model, device=device, tokenizer = tokenizer\n",
        ")\n",
        "txt = \"Action\"\n",
        "print(pipe(txt, num_return_sequences=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGF3S4YspS1y",
        "outputId": "d7bcb73d-3873-41c1-dd5f-5d19f068760f"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'generated_text': \"ActionDrama']: title: Ganga\\nStory: Raju is a school student in the Vastambard College along with his wife  Suresh and son. Vastambard College is known to be one of the top-\"}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_text = \"['Action', 'Drama', 'War']\"\n",
        "input_ids = tokenizer.encode(input_text, return_tensors = \"pt\")\n",
        "input_ids.to(device)\n",
        "new_model = AutoModelForCausalLM.from_pretrained(\"Vangmayy/Bollywood-Summary-Generator\")\n",
        "output = new_model.generate(input_ids, max_length = 5000, num_return_sequences = 1)\n",
        "print(tokenizer.decode(output[0], skip_special_tokens = True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVxosv3cyDf5",
        "outputId": "4c8a3034-b8e8-4b54-bae9-50cec39e45d8"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Action', 'Drama', 'War']: Indian Navy Officer - Born To A Hindu Father; Wife of Aamir Khan; Surgeon General; Member Of Ayesha Bharti Naval Academy; Apte Member Of Indian Guard; Circumstantly Admitted To The Royal Indian Guard; Shrewdly Inconsidered A Great Terrorist; And Angered After Arundhati Gandhi's Address To the British. He Was Unable To Follow This And Seem To Be Afraid Of Taking Justice Quite Thus In Finding And Paying His Decency; Forced To Re-Lea His Naval Residence In Mumbai; Forced To Resume His Service At Bombay; Retired On A High Note With Two Brothers  Jai And Jagram  After He Was Accused Of Being A Dangerous Terrorist by The State Of Punjab; And Angered When The Police Accused Jagram Of Being A Caged Terrorist In Court While the Police Accused Jagram Of Beating Police Inspector Bhaxmi; And In the Courtroom On A Murder Case As And When To Find Justice In Court; And Finally On a Public Service Order - A Warrant For A Trip To India.\n",
            "Suitable Actors: Kamini Kaushal|Abhay Deol|Om Shivpuri|Farida Jalal|Jasu Shrestha|Dara Singh|Makrand Deshpande|Nagma|Nasir Hussain|Dinesh Hingoo|Shatrughan Sinha|\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "36TI4sr6z8nD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "66a158f5ad9a47e890f64cf7b3e1abf3",
            "d8083bd319a54b25859f69b4e6f12cd8",
            "5b6b9c2f947540dc805fa73fcd5b6608",
            "dc11acf00d5e409a818d475d4701e9ce",
            "3da90ffd99754ff5bfc5c967151bfd3e",
            "e267e11ef4874db4bba9bda6a7419217",
            "a8284800c9ec454fb5a563a2fba4cb8d",
            "59eba8caa37345778d75deb98ebaa2e9",
            "03023e32c9c74f1ab85f7bbae41734be",
            "f4a9a3e892384588a6feacb1c112a6b9",
            "a9c117f9468e4be2bb57787272ed03f0",
            "aff733f049444f19831ce07812225fa7",
            "55b2b85848b04d389b717b05536b5574",
            "4556ee0bd43c4e738c36b624143b8131",
            "81325e68271940ef8a4f35d60caef3f5",
            "201a73ad8d104c18bf97013826a32063",
            "15d25281e53346bba2bbd0fa0ac7a7c8",
            "248d1004af4244a48840b8330a12fa95",
            "56da38a13bfd4ff998decf32ce889258",
            "d9b5362c3cb04d42be50d5b7eb34a3a7",
            "a29a2211e8b2475da6e4816f6f1ef7ed",
            "b94994b22cad47d98cd20600924efaf2",
            "ce3b4e21c329425a99fa579da6f51ab8",
            "3cefe0c4ede84954b764675743aa4b72",
            "621b8b8476ed4a9e91b4d88cb2224de0",
            "1cc913aa2024495880c5b71d42df1c76",
            "d5d3d007497a4a44822f9c070b55bc68",
            "3917ae709d964a95b342f012bbf586df",
            "0866aa586cb4447a81ead494430c5231",
            "f240a322f56b46299bca5b87c9a9013d",
            "9f550ef377f840e9adcf77925e1540a5",
            "7313e320385e404380b472c86a6dd2ec"
          ]
        },
        "id": "BXlWUMrVpT8D",
        "outputId": "c909e7cd-9e22-42f2-861d-3d0fa5846ab0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "66a158f5ad9a47e890f64cf7b3e1abf3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"Bollywood-Summary-Generator\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220,
          "referenced_widgets": [
            "1a436833b78f4a60900037e25ca457b8",
            "5566221ca1ee4083a23b39da68c48637",
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            "434d42ce91394e648394cb2afd173018",
            "b45122c5f84f48cf80e53a2feded7b9a",
            "f643c92f39134387ba23f08319d359e7",
            "03624b6a745c4f758df405b9cc3b8ad3",
            "3e15668e39494e2e8dd2d54b99490268",
            "060ab3ba83e9437bbf520f0ebfa3a1f6",
            "6ba7f018707448139e32d86b238c6d97",
            "150db0f5335b4268a3028d620c84b18f",
            "679d955d53724c14b0e4c2921e122d1a",
            "11108c816f2b4105aec24683a7edaf82",
            "3b453f85832b41348a8208ac429f0f84",
            "b49a8ace6a394e728376a7ad5dc7ad3a",
            "9308fc79e5db4bffa1eff3a4809cc091",
            "6bf15e7403b345cebc6176fd2c53de78",
            "1757580f930d4b98ae5d7538627c710b",
            "6ad864afa97742518937f11d194eeccd",
            "57d83cc50e2b4367bbf87ed7286de9d5",
            "2cd36aef856e4c7c8a206116b39a4c34",
            "0c1617b0e40c4d30a00b5116d9180c8c",
            "ad097b720a224f05a13d1a23dfb1307e",
            "1b07a4a4e1244fc98dabf08acf3ed40a",
            "6f92f362af4e497d8121be7a4718f120",
            "d9b86d0a933645c2a217df9fcf81f7c0",
            "bb7628bea9af484e8adc790959423015",
            "5672563b3cf24151b782a858711a8bc7",
            "7ed9e5bedc774967bfd6ed978da2b96f",
            "677badd3ba7a4856a39ac33676d6da33",
            "a6a0d178953d4fcc896802bb9ede4011",
            "2ef6e6bbb3b34660855f15e6548cb8aa",
            "21f315639e42403c89aba73d0e343713"
          ]
        },
        "id": "AMzsdFBIpVn7",
        "outputId": "924ffbd2-fcca-4134-a13b-fe58f17f387a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.\n",
            "Non-default generation parameters: {'max_length': 50, 'do_sample': True}\n",
            "Your generation config was originally created from the model config, but the model config has changed since then. Unless you pass the `generation_config` argument to this model's `generate` calls, they will revert to the legacy behavior where the base `generate` parameterization is loaded from the model config instead. To avoid this behavior and this warning, we recommend you to overwrite the generation config model attribute before calling the model's `save_pretrained`, preferably also removing any generation kwargs from the model config. This warning will be raised to an exception in v4.41.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/498M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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