diff --git "a/final_summary_generation_LED (1).ipynb" "b/final_summary_generation_LED (1).ipynb"
deleted file mode 100644--- "a/final_summary_generation_LED (1).ipynb"
+++ /dev/null
@@ -1,5954 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "VtuWHaKEQdEq",
-        "outputId": "9f28174a-c296-4af7-a700-e143970403e1"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
-            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
-            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
-            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
-            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
-            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
-            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
-            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
-            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n",
-            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
-            "Collecting datasets\n",
-            "  Downloading datasets-2.21.0-py3-none-any.whl.metadata (21 kB)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n",
-            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n",
-            "Collecting pyarrow>=15.0.0 (from datasets)\n",
-            "  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n",
-            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
-            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
-            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
-            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n",
-            "Collecting xxhash (from datasets)\n",
-            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
-            "Collecting multiprocess (from datasets)\n",
-            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
-            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
-            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.2)\n",
-            "Requirement already satisfied: huggingface-hub>=0.21.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.23.5)\n",
-            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
-            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
-            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.3.5)\n",
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-            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
-            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
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-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.7.4)\n",
-            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
-            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
-            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
-            "Downloading datasets-2.21.0-py3-none-any.whl (527 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.3/527.3 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (39.9 MB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m19.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hDownloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
-            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets\n",
-            "  Attempting uninstall: pyarrow\n",
-            "    Found existing installation: pyarrow 14.0.2\n",
-            "    Uninstalling pyarrow-14.0.2:\n",
-            "      Successfully uninstalled pyarrow-14.0.2\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",
-            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
-            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
-            "\u001b[0mSuccessfully installed datasets-2.21.0 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.4.1\n",
-            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu121)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.15.4)\n",
-            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
-            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
-            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
-            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
-            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
-            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
-            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
-            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
-            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
-            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
-            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
-            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
-            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
-            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
-            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
-            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
-            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
-            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
-            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
-            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
-            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
-            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
-            "Collecting nvidia-nccl-cu12==2.20.5 (from torch)\n",
-            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n",
-            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
-            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n",
-            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.1)\n",
-            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
-            "  Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
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-            "Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
-            "Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
-            "Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
-            "Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
-            "Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
-            "Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
-            "Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
-            "Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
-            "Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
-            "Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
-            "Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
-            "Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl (19.7 MB)\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\n",
-            "Successfully installed 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-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install transformers\n",
-        "!pip install datasets\n",
-        "!pip install torch\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "1vXYwybdRDD_",
-        "outputId": "19a76d2e-3b8e-4aba-a6a1-2e410b5806bb"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
-            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.21.0)\n",
-            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu121)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
-            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
-            "Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
-            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
-            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
-            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
-            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.4)\n",
-            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
-            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
-            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
-            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
-            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
-            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
-            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
-            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
-            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.2)\n",
-            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
-            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
-            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
-            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
-            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
-            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
-            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n",
-            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n",
-            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n",
-            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n",
-            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n",
-            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
-            "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n",
-            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
-            "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.1)\n",
-            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.6.20)\n",
-            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.3.5)\n",
-            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
-            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
-            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
-            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.5)\n",
-            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.4)\n",
-            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
-            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
-            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
-            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
-            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
-            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install transformers datasets torch\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "5tiNer2wNmKd",
-        "outputId": "9cb6a807-b20b-4807-c64e-71bd89b09f3a"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.17)\n",
-            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
-            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.7.4)\n",
-            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.5)\n",
-            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
-            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
-            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
-            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
-            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n",
-            "cp: cannot stat 'kaggle.json': No such file or directory\n",
-            "chmod: cannot access '/root/.kaggle/kaggle.json': No such file or directory\n",
-            "Dataset URL: https://www.kaggle.com/datasets/rmisra/imdb-spoiler-dataset\n",
-            "License(s): Attribution 4.0 International (CC BY 4.0)\n",
-            "Downloading imdb-spoiler-dataset.zip to /content\n",
-            " 98% 325M/331M [00:02<00:00, 136MB/s]\n",
-            "100% 331M/331M [00:02<00:00, 138MB/s]\n",
-            "Archive:  imdb-spoiler-dataset.zip\n",
-            "  inflating: IMDB_movie_details.json  \n",
-            "  inflating: IMDB_reviews.json       \n",
-            "IMDB_movie_details.json  IMDB_reviews.json  imdb-spoiler-dataset.zip  sample_data\n"
-          ]
-        }
-      ],
-      "source": [
-        "!pip install kaggle\n",
-        "\n",
-        "!mkdir -p ~/.kaggle\n",
-        "!cp kaggle.json ~/.kaggle/\n",
-        "\n",
-        "!chmod 600 ~/.kaggle/kaggle.json\n",
-        "\n",
-        "!kaggle datasets download -d rmisra/imdb-spoiler-dataset\n",
-        "\n",
-        "!unzip imdb-spoiler-dataset.zip\n",
-        "\n",
-        "!ls"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "uXLdvqgVOL99",
-        "outputId": "d6231cf9-2178-4c04-b880-53ef40842eea"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "    movie_id                                       plot_summary  duration  \\\n",
-            "0  tt0105112  Former CIA analyst, Jack Ryan is in England wi...  1h 57min   \n",
-            "1  tt1204975  Billy (Michael Douglas), Paddy (Robert De Niro...  1h 45min   \n",
-            "2  tt0243655  The setting is Camp Firewood, the year 1981. I...  1h 37min   \n",
-            "3  tt0040897  Fred C. Dobbs and Bob Curtin, both down on the...   2h 6min   \n",
-            "4  tt0126886  Tracy Flick is running unopposed for this year...  1h 43min   \n",
-            "\n",
-            "                         genre  rating release_date  \\\n",
-            "0           [Action, Thriller]     6.9   1992-06-05   \n",
-            "1                     [Comedy]     6.6   2013-11-01   \n",
-            "2            [Comedy, Romance]     6.7   2002-04-11   \n",
-            "3  [Adventure, Drama, Western]     8.3   1948-01-24   \n",
-            "4     [Comedy, Drama, Romance]     7.3   1999-05-07   \n",
-            "\n",
-            "                                       plot_synopsis  \n",
-            "0  Jack Ryan (Ford) is on a \"working vacation\" in...  \n",
-            "1  Four boys around the age of 10 are friends in ...  \n",
-            "2                                                     \n",
-            "3  Fred Dobbs (Humphrey Bogart) and Bob Curtin (T...  \n",
-            "4  Jim McAllister (Matthew Broderick) is a much-a...  \n",
-            "        review_date   movie_id    user_id  is_spoiler  \\\n",
-            "0  10 February 2006  tt0111161  ur1898687        True   \n",
-            "1  6 September 2000  tt0111161  ur0842118        True   \n",
-            "2     3 August 2001  tt0111161  ur1285640        True   \n",
-            "3  1 September 2002  tt0111161  ur1003471        True   \n",
-            "4       20 May 2004  tt0111161  ur0226855        True   \n",
-            "\n",
-            "                                         review_text  rating  \\\n",
-            "0  In its Oscar year, Shawshank Redemption (writt...      10   \n",
-            "1  The Shawshank Redemption is without a doubt on...      10   \n",
-            "2  I believe that this film is the best story eve...       8   \n",
-            "3  **Yes, there are SPOILERS here**This film has ...      10   \n",
-            "4  At the heart of this extraordinary movie is a ...       8   \n",
-            "\n",
-            "                                  review_summary  \n",
-            "0  A classic piece of unforgettable film-making.  \n",
-            "1     Simply amazing. The best film of the 90's.  \n",
-            "2               The best story ever told on film  \n",
-            "3                     Busy dying or busy living?  \n",
-            "4         Great story, wondrously told and acted  \n"
-          ]
-        }
-      ],
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "# Load JSON data by reading lines if the data is stored in JSON Lines format\n",
-        "movie_details = pd.read_json('IMDB_movie_details.json', lines=True)\n",
-        "reviews = pd.read_json('IMDB_reviews.json', lines=True)\n",
-        "\n",
-        "# Check the first few entries to ensure data is loaded correctly\n",
-        "print(movie_details.head())\n",
-        "print(reviews.head())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "44kpxJ0iOjLz"
-      },
-      "outputs": [],
-      "source": [
-        "# Drop rows where 'plot_synopsis' or 'plot_summary' is missing\n",
-        "movie_details.dropna(subset=['plot_synopsis', 'plot_summary'], inplace=True)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qaAPgonAPRGx"
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "# Split the data into training and test sets\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "# First split: split into training and temp data (which will become validation and test sets)\n",
-        "train_data, temp_data = train_test_split(movie_details, test_size=0.3, random_state=42)  # 70% for training, 30% for temp\n",
-        "\n",
-        "# Second split: split the temp data into validation and test sets\n",
-        "validation_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)  # Split the 30% into 15% validation and 15% test\n",
-        "\n",
-        "# Now, train_data holds 70% of the data, validation_data holds 15%, and test_data also holds 15%\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "gewp_maVVU6g",
-        "outputId": "281cc145-2564-466c-b526-1ad7f58957c1"
-      },
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(1100, 7)"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        }
-      ],
-      "source": [
-        "train_data.shape"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6Y-syQ3ZPVDR",
-        "outputId": "544030c8-9652-4ec6-96f3-b3dbd03a82a4"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "count     1572.000000\n",
-            "mean      1439.085242\n",
-            "std       1496.392929\n",
-            "min          0.000000\n",
-            "25%        493.750000\n",
-            "50%       1073.500000\n",
-            "75%       1920.000000\n",
-            "max      11396.000000\n",
-            "Name: synopsis_length, dtype: float64\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Example exploration: Average length of plot_synopsis\n",
-        "movie_details['synopsis_length'] = movie_details['plot_synopsis'].apply(lambda x: len(x.split()))\n",
-        "print(movie_details['synopsis_length'].describe())\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6lRJv7A7PX8x"
-      },
-      "outputs": [],
-      "source": [
-        "train_data.to_json('/content/train_data.json', orient='records', lines=True)\n",
-        "validation_data.to_json('/content/val_data.json', orient='records', lines=True)\n",
-        "test_data.to_json('/content/test_data.json', orient='records', lines=True)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 113,
-          "referenced_widgets": [
-            "7e20cd41d4d7401284eae05380531c86",
-            "1f7a1424873e47f9882ce9aadd360be9",
-            "3ba09083cc8f403c8231adb1dddeffcd",
-            "bf60ee240ad141e5b7c14a31caf459f6",
-            "46535b3c982d45aeadcaa00f2e024ad1",
-            "e19c05d1e02546ddad481a7d5a5917cd",
-            "ab60068105e149eb9318b3a8b3662075",
-            "6ec9693194af46ffbd8310d83917803b",
-            "5f88faea4b894a6e8cb44ce00084e2a2",
-            "eeecd29be4284e0cb9b3ddf9d83de029",
-            "43830d9d88dd47bfb4ce27020a2be9f3",
-            "481fda13f40a4ea19853b2b0cf0fa99e",
-            "a16b6c84f2c74226a46b1e9724965cca",
-            "2e55a4a146454d2faacfbfe352ecd917",
-            "c8b01fd70f364abdb2da85e135dfc5d2",
-            "974c5fa2dc1c47ae95b8debec2640825",
-            "e98b2b076dba4a8ab1942dc0e23984d8",
-            "2feee173fbe44801bbc9dc05952e1038",
-            "ffb79557f11948d69d0b67f2a71121e8",
-            "91ef540aa06b43d28c6bd371665dcb68",
-            "e76db42f1f9e4edda5ff102e8cdd7363",
-            "9330377c22224352b99bba7cd4d635b2",
-            "3de6d461f83a4c5f8924eb02b9f13d84",
-            "d4adf688973c48a693a7a4aee8e0b23e",
-            "756a26793f594047b99f5642a59b6dd9",
-            "924058735d454e47a76f36c558a7ba15",
-            "edfc526ced1b4f799f4609a4289e369b",
-            "b2ebfcadca8f4017ac1606caeba957ed",
-            "ec119eb9de4742d180db9862b5369b70",
-            "8a32a28a12464bb683c62d10a9663f1c",
-            "5317f74358914b1783f2bb812006cd8d",
-            "9293e46df3f14358b87af38eeef7f109",
-            "24c79207d28d495eaeabd38db4f6007b"
-          ]
-        },
-        "id": "yj5INXEXPhsr",
-        "outputId": "c1d93ee4-7dbc-45ed-c12b-f8cca44398ed"
-      },
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating train split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "7e20cd41d4d7401284eae05380531c86"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating test split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "481fda13f40a4ea19853b2b0cf0fa99e"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Generating val split: 0 examples [00:00, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "3de6d461f83a4c5f8924eb02b9f13d84"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "from datasets import load_dataset\n",
-        "\n",
-        "data_files = {\n",
-        "    'train': '/content/train_data.json',\n",
-        "    'test': '/content/test_data.json',\n",
-        "    'val': '/content/val_data.json'\n",
-        "\n",
-        "\n",
-        "}\n",
-        "\n",
-        "# Loading the dataset from the JSON files\n",
-        "dataset = load_dataset('json', data_files=data_files, split={'train': 'train', 'test': 'test', 'val': 'val'})\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 428,
-          "referenced_widgets": [
-            "57fcbc7a221e4441a1908c756e3b2ca0",
-            "b733e988bb9542759d8d834ff6b1d5f3",
-            "a7e4c7ab193e4300acc49050c2004b1b",
-            "ae8003c16eca4d59962125d599f79f31",
-            "ef6d361ff5e34115b55443e499685937",
-            "f73ebe2ab28242efbb4450a6e329086a",
-            "2181e80c121d4da8bd422e1d2be8d5b8",
-            "beac2ecdbef440e799f4a1364f7e8856",
-            "bd75bd7a93084f39b1cbe9d2b789c3fc",
-            "43a6048276494abb814a078fe05c439f",
-            "80cdedbc980d490fae438a6fb92e0e66",
-            "f542f154196d4ef2bf67ffbdc488b8ad",
-            "83772cf85c604db19217acb073ff4f78",
-            "d42835fe1966412cbaa21333ecd361cc",
-            "222c0334528541d5b4470801a75b8d0e",
-            "4107eaad71e04e968c9d4123b7e70312",
-            "5274a20615a949c68eee15c066f845c1",
-            "5193d8adb6124549a4d9471c2475f1ec",
-            "11dc881125294407b118b31d01b92d70",
-            "14435f717b2c43e59861a7b404d11b53",
-            "77f710ea78b04ba3831b6f95f156fe31",
-            "9ede3c68111047d484edfbd67c82c50d",
-            "1d5b88a9e165423a815479dd4138bf2b",
-            "6b93a696e83e401787113078df83baa7",
-            "654e846c19a441d18ea81a12350c9c51",
-            "0f751527e05e4293be2b2312cc52d754",
-            "a54d5c1d98cb46a68a38da67d9118658",
-            "9c374f90a9b74b208c54f4958d40a12c",
-            "21c8478187654918ab9c689fb31ee16e",
-            "63ede400669443c78551c04691c422e6",
-            "fc64e38fac154cc3b48989a126d73699",
-            "f285da915ded4694aa9b41c03f3bd289",
-            "be05ebd4527c4bb08da9e0ff2e7b892e"
-          ]
-        },
-        "id": "GOe61CxiYkgv",
-        "outputId": "52095422-f4f9-4053-caf3-40ac92c981ae"
-      },
-      "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%|          | 0.00/2.32k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "57fcbc7a221e4441a1908c756e3b2ca0"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "f542f154196d4ef2bf67ffbdc488b8ad"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/1.39M [00:00<?, ?B/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "1d5b88a9e165423a815479dd4138bf2b"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
-            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "count     1572.000000\n",
-            "mean      2116.767812\n",
-            "std       2197.103130\n",
-            "min          0.000000\n",
-            "25%        729.000000\n",
-            "50%       1581.000000\n",
-            "75%       2814.750000\n",
-            "max      18103.000000\n",
-            "Name: token_length, dtype: float64\n"
-          ]
-        }
-      ],
-      "source": [
-        "from transformers import T5Tokenizer\n",
-        "\n",
-        "\n",
-        "import pandas as pd\n",
-        "\n",
-        "movie_details = pd.read_json('/content/IMDB_movie_details.json', lines=True)\n",
-        "\n",
-        "# Initialize the tokenizer\n",
-        "tokenizer = T5Tokenizer.from_pretrained('t5-small')\n",
-        "\n",
-        "# Function to calculate token length\n",
-        "def calculate_token_length(text):\n",
-        "    return len(tokenizer.tokenize(text))\n",
-        "\n",
-        "# Apply the function to the plot_synopsis column\n",
-        "movie_details['token_length'] = movie_details['plot_synopsis'].apply(calculate_token_length)\n",
-        "\n",
-        "# Display statistics about the token lengths\n",
-        "stats = movie_details['token_length'].describe()\n",
-        "print(stats)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "QHSO1Tz4Z5kc",
-        "outputId": "d64d5657-b3c9-42b1-e5bb-c47067593d30"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "count    1572.000000\n",
-            "mean      151.191476\n",
-            "std        60.718672\n",
-            "min        20.000000\n",
-            "25%       103.000000\n",
-            "50%       142.000000\n",
-            "75%       195.250000\n",
-            "max       315.000000\n",
-            "Name: token_length, dtype: float64\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Apply the function to the plot_synopsis column\n",
-        "movie_details['token_length'] = movie_details['plot_summary'].apply(calculate_token_length)\n",
-        "\n",
-        "# Display statistics about the token lengths\n",
-        "stats = movie_details['token_length'].describe()\n",
-        "print(stats)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "VS4ROiIKnvLy"
-      },
-      "outputs": [],
-      "source": [
-        "device = 'cuda'\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dTcbyQKVQFFA"
-      },
-      "source": [
-        "##**Preprocess the Data**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import re\n",
-        "import torch\n",
-        "from transformers import LEDForConditionalGeneration, LEDTokenizer\n",
-        "\n",
-        "# Load tokenizer and model\n",
-        "tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')\n",
-        "model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')\n",
-        "\n",
-        "model = model.to(device)\n",
-        "\n",
-        "# Function to normalize text\n",
-        "def normalize_text(text):\n",
-        "    text = text.lower()  # Lowercase the text\n",
-        "    text = re.sub(r'\\s+', ' ', text).strip()  # Remove extra spaces and newlines\n",
-        "    text = re.sub(r'[^\\w\\s]', '', text)  # Remove non-alphanumeric characters\n",
-        "    return text\n",
-        "\n",
-        "# Preprocess function with normalization\n",
-        "def preprocess_function(examples):\n",
-        "    # Normalize the plot_synopsis and plot_summary\n",
-        "    inputs = [\"summarize: \" + normalize_text(doc) for doc in examples[\"plot_synopsis\"]]\n",
-        "    model_inputs = tokenizer(inputs, max_length=3000, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
-        "\n",
-        "    # Normalize labels (plot_summary)\n",
-        "    with tokenizer.as_target_tokenizer():\n",
-        "        labels = tokenizer([normalize_text(doc) for doc in examples[\"plot_summary\"]], max_length=1024, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
-        "\n",
-        "    # Replace -100 for padding tokens in labels\n",
-        "    labels[\"input_ids\"] = [\n",
-        "        [(label if label != tokenizer.pad_token_id else -100) for label in lab]\n",
-        "        for lab in labels[\"input_ids\"]\n",
-        "    ]\n",
-        "\n",
-        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
-        "    return model_inputs"
-      ],
-      "metadata": {
-        "id": "eBxLkLaUHJLI"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "NUPy3CuAQBGR",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 319,
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-        "outputId": "d98eb8e3-3eb7-42ef-86b5-f75c0723d804"
-      },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "83ee5845ab11475c822bef2a97d9985e",
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-            "text/plain": [
-              "tokenizer_config.json:   0%|          | 0.00/27.0 [00:00<?, ?B/s]"
-            ]
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-        },
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-              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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-              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
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-              "version_major": 2,
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-            "text/plain": [
-              "special_tokens_map.json:   0%|          | 0.00/772 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
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-            },
-            "text/plain": [
-              "config.json:   0%|          | 0.00/1.09k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
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-              "model_id": "32b8b010edde4ca7ac47047d1811f38b",
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-              "version_minor": 0
-            },
-            "text/plain": [
-              "pytorch_model.bin:   0%|          | 0.00/648M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "879091262d234c6ab761dd712d48d4dc",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "generation_config.json:   0%|          | 0.00/168 [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "44df2ef9fe804e4798e0bafcaa2f96f8",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Map:   0%|          | 0/1100 [00:00<?, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "metadata": {
-            "tags": null
-          },
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:4016: 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": [
-              "Map:   0%|          | 0/236 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "a8e7085f3a1e46f495785b0aa330cae2"
-            }
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "Map:   0%|          | 0/236 [00:00<?, ? examples/s]"
-            ],
-            "application/vnd.jupyter.widget-view+json": {
-              "version_major": 2,
-              "version_minor": 0,
-              "model_id": "5cae3942022845bca747d27c148f8013"
-            }
-          },
-          "metadata": {}
-        }
-      ],
-      "source": [
-        "tokenized_datasets = dataset.map(preprocess_function, batched=True, remove_columns=dataset[\"train\"].column_names)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "naG4NIgFST5W"
-      },
-      "source": [
-        "##**Define the Model**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gTR-Wzy2SS2l"
-      },
-      "outputs": [],
-      "source": [
-        "# from transformers import T5ForConditionalGeneration\n",
-        "import torch, gc\n",
-        "\n",
-        "gc.collect()\n",
-        "torch.cuda.empty_cache()\n",
-        "\n",
-        "# model = T5ForConditionalGeneration.from_pretrained('t5-small')\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "VUueLVLYu5C_"
-      },
-      "outputs": [],
-      "source": [
-        "import torch, gc\n",
-        "\n",
-        "gc.collect()\n",
-        "torch.cuda.empty_cache()\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "xCo8sJtDptW2",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f81077b6-3813-4f32-ba01-16f9be64e690"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "using device:  cuda\n"
-          ]
-        }
-      ],
-      "source": [
-        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-        "print(f\"using device:  {device}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "nuE7Nw58TQUs",
-        "outputId": "da1b22ae-b6dc-4b9d-90b9-ad05b4bc33f3"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-            "  warnings.warn(\n"
-          ]
-        }
-      ],
-      "source": [
-        "from transformers import Trainer, TrainingArguments\n",
-        "import torch\n",
-        "\n",
-        "training_args = TrainingArguments(\n",
-        "    output_dir=\"./results\",\n",
-        "    evaluation_strategy=\"epoch\",\n",
-        "    learning_rate=2e-5,\n",
-        "    per_device_train_batch_size=1,  # LED requires a lot of memory\n",
-        "    per_device_eval_batch_size=1,\n",
-        "    weight_decay=0.01,\n",
-        "    save_total_limit=3,\n",
-        "    num_train_epochs=3,\n",
-        "    report_to=\"none\"\n",
-        ")\n",
-        "\n",
-        "# Initialize AdamW optimizer\n",
-        "optimizer = torch.optim.AdamW(model.parameters(), lr=training_args.learning_rate)\n",
-        "\n",
-        "trainer = Trainer(\n",
-        "    model=model,\n",
-        "    args=training_args,\n",
-        "    train_dataset=tokenized_datasets[\"train\"],\n",
-        "    eval_dataset=tokenized_datasets[\"val\"],\n",
-        "    tokenizer=tokenizer,\n",
-        "    optimizers=(optimizer, None) # Pass the optimizer to the Trainer\n",
-        ")\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "trainer.train()"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 311
-        },
-        "id": "xegd5GMMEY4R",
-        "outputId": "a7249a3f-393b-481f-cc48-6c5d47e81be0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='2001' max='3300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [2001/3300 47:25 < 30:49, 0.70 it/s, Epoch 1.82/3]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Epoch</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>1</td>\n",
-              "      <td>2.925300</td>\n",
-              "      <td>2.873685</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
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-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='3300' max='3300' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [3300/3300 1:23:26, Epoch 3/3]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Epoch</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>1</td>\n",
-              "      <td>2.925300</td>\n",
-              "      <td>2.873685</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>2</td>\n",
-              "      <td>2.409600</td>\n",
-              "      <td>2.883492</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>3</td>\n",
-              "      <td>2.165300</td>\n",
-              "      <td>2.920285</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "TrainOutput(global_step=3300, training_loss=2.429696747750947, metrics={'train_runtime': 5007.437, 'train_samples_per_second': 0.659, 'train_steps_per_second': 0.659, 'total_flos': 6526373990400000.0, 'train_loss': 2.429696747750947, 'epoch': 3.0})"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 21
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gt4LnnFMTTsV",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "e513a352-837f-4d06-c635-fac1596c342e"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n"
-          ]
-        }
-      ],
-      "source": [
-        "# trainer.train()\n",
-        "# trainer.save_model(\"/content/t5_spoiler_free_summarization\")\n",
-        "# prompt: can you also save it in my driver?\n",
-        "\n",
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "\n",
-        "# Assuming the model is already trained and 'model' variable holds the trained model\n",
-        "model_save_path = '/content/drive/MyDrive/summary_generation'  # Replace with your desired path in Drive\n",
-        "trainer.save_model(model_save_path)\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [],
-      "metadata": {
-        "id": "p1oX9EDsmxPo"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "# Assuming the model is already trained and 'model' variable holds the trained model\n",
-        "model_save_path = '/content/drive/MyDrive/summary_generation_Led_3'  # Replace with your desired path in Drive\n",
-        "trainer.save_model(model_save_path)"
-      ],
-      "metadata": {
-        "id": "kFuNP98wGaa2"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from google.colab import drive\n",
-        "drive.mount('/content/drive')\n",
-        "\n",
-        "# Load the trained model from Google Drive\n",
-        "model_save_path = '/content/drive/MyDrive/summary_generation'  # Replace with your saved path\n",
-        "model = LEDForConditionalGeneration.from_pretrained(model_save_path)\n",
-        "\n",
-        "# Ensure model is on the right device (GPU if available)\n",
-        "model = model.to(device)\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "yopwoSEJgNkT",
-        "outputId": "e5cf03ef-5d32-4447-9b6d-237c90b4b3c0"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Mounted at /content/drive\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from transformers import Trainer, TrainingArguments\n",
-        "\n",
-        "# Reload the model from the saved checkpoint in Google Drive\n",
-        "model_save_path = '/content/drive/MyDrive/summary_generation'  # Your saved path\n",
-        "model = LEDForConditionalGeneration.from_pretrained(model_save_path)\n",
-        "model = model.to(device)  # Ensure the model is on the right device (GPU or CPU)\n",
-        "\n",
-        "# Define the training arguments for continuing training\n",
-        "training_args = TrainingArguments(\n",
-        "    output_dir=\"./results\",  # Where the model checkpoints will be stored\n",
-        "    eval_strategy=\"epoch\",  # Use eval_strategy instead of evaluation_strategy\n",
-        "    save_strategy=\"epoch\",  # Ensure the save strategy matches the evaluation strategy\n",
-        "    learning_rate=2e-5,  # Adjust learning rate if needed\n",
-        "    per_device_train_batch_size=1,  # LED is memory-intensive\n",
-        "    per_device_eval_batch_size=1,\n",
-        "    weight_decay=0.01,\n",
-        "    save_total_limit=3,  # Only keep the last 3 models saved\n",
-        "    num_train_epochs=1,  # Continue training for 1 additional epoch\n",
-        "    report_to=\"none\",\n",
-        "    logging_dir='./logs',  # Directory for storing logs\n",
-        "    logging_steps=500,\n",
-        "    load_best_model_at_end=True,  # Load best model at the end of training based on evaluation loss\n",
-        ")\n",
-        "\n",
-        "# Initialize the optimizer (AdamW) again for the new training run\n",
-        "optimizer = torch.optim.AdamW(model.parameters(), lr=training_args.learning_rate)\n",
-        "\n",
-        "# Create the Trainer instance and pass the model, data, and optimizer\n",
-        "trainer = Trainer(\n",
-        "    model=model,\n",
-        "    args=training_args,\n",
-        "    train_dataset=tokenized_datasets[\"train\"],  # The same training dataset\n",
-        "    eval_dataset=tokenized_datasets[\"val\"],  # The same validation dataset\n",
-        "    tokenizer=tokenizer,\n",
-        "    optimizers=(optimizer, None)  # Pass the optimizer\n",
-        ")\n",
-        "\n",
-        "# Resume training for another epoch\n",
-        "trainer.train()\n",
-        "\n",
-        "# After training is complete, you can save the updated model\n",
-        "trainer.save_model(\"/content/drive/MyDrive/summary_generation_Led_4\")  # Save the new checkpoint\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 144
-        },
-        "id": "DnDXbarJm1YG",
-        "outputId": "90c5c5a8-bf06-4631-e868-17c6e7dc2f4a"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ],
-            "text/html": [
-              "\n",
-              "    <div>\n",
-              "      \n",
-              "      <progress value='1100' max='1100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [1100/1100 26:32, Epoch 1/1]\n",
-              "    </div>\n",
-              "    <table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              " <tr style=\"text-align: left;\">\n",
-              "      <th>Epoch</th>\n",
-              "      <th>Training Loss</th>\n",
-              "      <th>Validation Loss</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>1</td>\n",
-              "      <td>2.140500</td>\n",
-              "      <td>2.964694</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table><p>"
-            ]
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "There were missing keys in the checkpoint model loaded: ['led.encoder.embed_tokens.weight', 'led.decoder.embed_tokens.weight', 'lm_head.weight'].\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "7S7rMmsRzUDK",
-        "outputId": "207767ac-c55d-433b-989f-6f9f15098d16"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Collecting rouge_score\n",
-            "  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n",
-            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.4.0)\n",
-            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge_score) (3.8.1)\n",
-            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.26.4)\n",
-            "Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.16.0)\n",
-            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (8.1.7)\n",
-            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (1.4.2)\n",
-            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (2024.5.15)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (4.66.5)\n",
-            "Building wheels for collected packages: rouge_score\n",
-            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24935 sha256=99631a27f739be8626953540b4e8f0dccd306bb718d08af5f91069d975cf0f26\n",
-            "  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n",
-            "Successfully built rouge_score\n",
-            "Installing collected packages: rouge_score\n",
-            "Successfully installed rouge_score-0.1.2\n"
-          ]
-        }
-      ],
-      "source": [
-        "pip install rouge_score"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "pip install nltk\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "OFJmH2KUtdod",
-        "outputId": "8b551282-a6ac-481f-f5fe-d6c702e2ba70"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (3.8.1)\n",
-            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk) (8.1.7)\n",
-            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk) (1.4.2)\n",
-            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk) (2024.5.15)\n",
-            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk) (4.66.5)\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import torch, gc\n",
-        "\n",
-        "gc.collect()\n",
-        "torch.cuda.empty_cache()\n"
-      ],
-      "metadata": {
-        "id": "SEGn9IQ4to0c"
-      },
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import nltk\n",
-        "\n",
-        "nltk.download('punkt')\n"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "FliiA5j_-bK6",
-        "outputId": "a1784ce0-e243-4f06-b2dc-554300ec8726"
-      },
-      "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": 20
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "from datasets import load_metric\n",
-        "import nltk\n",
-        "\n",
-        "metric_rouge = load_metric(\"rouge\")\n",
-        "\n",
-        "def generate_summary(batch):\n",
-        "    inputs = tokenizer(batch[\"plot_synopsis\"], max_length=3000, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n",
-        "    inputs = inputs.to(device)\n",
-        "    outputs = model.generate(inputs[\"input_ids\"],  max_length=315, min_length=20, length_penalty=2.0, num_beams=4, early_stopping=True)\n",
-        "\n",
-        "    batch[\"pred_summary\"] = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
-        "    return batch\n",
-        "\n",
-        "results = dataset[\"test\"].map(generate_summary, batched=True, batch_size=8)\n",
-        "rouge_score = metric_rouge.compute(predictions=results[\"pred_summary\"], references=results[\"plot_summary\"])\n",
-        "print(\"ROUGE scores:\")\n",
-        "print(rouge_score)"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 104,
-          "referenced_widgets": [
-            "ad570df7408d48d99eb69a50ad8f3201",
-            "265654d5426b4616b530e7fd1c7cec04",
-            "ccb396e3506e487284decd31875fa33d",
-            "2873376fa2264b26ab8dcebe41d46af0",
-            "13b89ba59e80499f9d6926ee97d9f519",
-            "edf3f110599943ab822847b15bcfd0d4",
-            "8d275ca50cce4c48889ddc50769ada21",
-            "d89b8b1072d9408c96d2f7b2e735c785",
-            "0e6ac91d7b284b9b82abdedf36e91924",
-            "e0b2c384ca364c49869ae498683a6ef3",
-            "a84982a5e3ff40a18ab9b416cd85a111"
-          ]
-        },
-        "id": "I1wd38l3sYAy",
-        "outputId": "4a6e0d20-74b4-4916-edf3-c7b0ef4b5117"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ad570df7408d48d99eb69a50ad8f3201",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Map:   0%|          | 0/236 [00:00<?, ? examples/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "ROUGE scores:\n",
-            "{'rouge1': AggregateScore(low=Score(precision=0.2765937483910001, recall=0.28995076740292736, fmeasure=0.26035670057622456), mid=Score(precision=0.2921494324205486, recall=0.3074020206087751, fmeasure=0.2699305362452888), high=Score(precision=0.3059451759910771, recall=0.32368159175206357, fmeasure=0.27908123059966716)), 'rouge2': AggregateScore(low=Score(precision=0.05483977819595639, recall=0.05782616778822425, fmeasure=0.05115662969998852), mid=Score(precision=0.059651412474327724, recall=0.0629010316508109, fmeasure=0.05502744836975126), high=Score(precision=0.06481477377185653, recall=0.06911828260432601, fmeasure=0.05934541057407643)), 'rougeL': AggregateScore(low=Score(precision=0.17193896723048094, recall=0.17809953193399178, fmeasure=0.16095113303292502), mid=Score(precision=0.1815783962174589, recall=0.1874934529402342, fmeasure=0.16563763481163463), high=Score(precision=0.19066115263066233, recall=0.19614412850133803, fmeasure=0.1701542843468328)), 'rougeLsum': AggregateScore(low=Score(precision=0.17237558177544454, recall=0.17845137961598936, fmeasure=0.1609933753577488), mid=Score(precision=0.18171951919460577, recall=0.18763017833485315, fmeasure=0.16580293749438152), high=Score(precision=0.1909969265446938, recall=0.19646089352884588, fmeasure=0.1706179490049745))}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import nltk\n",
-        "from datasets import load_metric\n",
-        "nltk.download('punkt')  # Ensure the NLTK tokenizer is available\n",
-        "\n",
-        "def calculate_bleu_scores(predictions, references):\n",
-        "    # Load the BLEU metric\n",
-        "    metric_bleu = load_metric(\"bleu\")\n",
-        "\n",
-        "    # Tokenize the predictions and references\n",
-        "    tokenized_predictions = [nltk.word_tokenize(pred) for pred in predictions]\n",
-        "    tokenized_references = [[nltk.word_tokenize(ref)] for ref in references]  # BLEU expects a list of list of references\n",
-        "\n",
-        "    # Calculate BLEU scores\n",
-        "    bleu_score = metric_bleu.compute(predictions=tokenized_predictions, references=tokenized_references)\n",
-        "\n",
-        "    return bleu_score\n",
-        "\n",
-        "bleu_scores = calculate_bleu_scores(predictions=results[\"pred_summary\"], references=results[\"plot_summary\"])\n",
-        "\n",
-        "print(\"BLEU scores:\")\n",
-        "print(bleu_scores)\n"
-      ],
-      "metadata": {
-        "id": "ZaP_IEJPBDx8",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "36731e91-ec08-42ee-ad89-3879f57c450b"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
-            "[nltk_data]   Package punkt is already up-to-date!\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "BLEU scores:\n",
-            "{'bleu': 0.03487780615578816, 'precisions': [0.273911286562432, 0.05937059652418976, 0.016940597495189124, 0.005371388615198805], 'brevity_penalty': 1.0, 'length_ratio': 1.1596914314552467, 'translation_length': 32171, 'reference_length': 27741}\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import nltk\n",
-        "from datasets import load_metric\n",
-        "from prettytable import PrettyTable\n",
-        "\n",
-        "print(\"BLEU scores:\")\n",
-        "print(bleu_scores)\n",
-        "\n",
-        "# Prepare a pretty table to display the BLEU scores\n",
-        "table = PrettyTable()\n",
-        "table.field_names = [\"BLEU Score\", \"Value\"]\n",
-        "\n",
-        "# Assuming bleu_scores contains 'precisions' or equivalent\n",
-        "if 'precisions' in bleu_scores:\n",
-        "    for i, score in enumerate(bleu_scores['precisions'], start=1):\n",
-        "        table.add_row([f\"BLEU-{i}\", score])\n",
-        "\n",
-        "# Print the pretty table\n",
-        "print(\"BLEU scores:\")\n",
-        "print(table)\n"
-      ],
-      "metadata": {
-        "id": "DsTL-1BcB3JM",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "5497b29f-b374-41af-e864-f72612627d93"
-      },
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "BLEU scores:\n",
-            "{'bleu': 0.03487780615578816, 'precisions': [0.273911286562432, 0.05937059652418976, 0.016940597495189124, 0.005371388615198805], 'brevity_penalty': 1.0, 'length_ratio': 1.1596914314552467, 'translation_length': 32171, 'reference_length': 27741}\n",
-            "BLEU scores:\n",
-            "+------------+----------------------+\n",
-            "| BLEU Score |        Value         |\n",
-            "+------------+----------------------+\n",
-            "|   BLEU-1   |  0.273911286562432   |\n",
-            "|   BLEU-2   | 0.05937059652418976  |\n",
-            "|   BLEU-3   | 0.016940597495189124 |\n",
-            "|   BLEU-4   | 0.005371388615198805 |\n",
-            "+------------+----------------------+\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "jfIbuoxs7waf",
-        "outputId": "bede53de-18c0-40ac-b760-11f3d00fda6e"
-      },
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "+-----------+------------+-----------+--------+-----------+\n",
-            "|   Metric  | Confidence | Precision | Recall | F-Measure |\n",
-            "+-----------+------------+-----------+--------+-----------+\n",
-            "|   ROUGE1  |    Low     |   0.2766  | 0.2900 |   0.2604  |\n",
-            "|   ROUGE1  |    Mid     |   0.2921  | 0.3074 |   0.2699  |\n",
-            "|   ROUGE1  |    High    |   0.3059  | 0.3237 |   0.2791  |\n",
-            "|   ROUGE2  |    Low     |   0.0548  | 0.0578 |   0.0512  |\n",
-            "|   ROUGE2  |    Mid     |   0.0597  | 0.0629 |   0.0550  |\n",
-            "|   ROUGE2  |    High    |   0.0648  | 0.0691 |   0.0593  |\n",
-            "|   ROUGEL  |    Low     |   0.1719  | 0.1781 |   0.1610  |\n",
-            "|   ROUGEL  |    Mid     |   0.1816  | 0.1875 |   0.1656  |\n",
-            "|   ROUGEL  |    High    |   0.1907  | 0.1961 |   0.1702  |\n",
-            "| ROUGELSUM |    Low     |   0.1724  | 0.1785 |   0.1610  |\n",
-            "| ROUGELSUM |    Mid     |   0.1817  | 0.1876 |   0.1658  |\n",
-            "| ROUGELSUM |    High    |   0.1910  | 0.1965 |   0.1706  |\n",
-            "+-----------+------------+-----------+--------+-----------+\n"
-          ]
-        }
-      ],
-      "source": [
-        "from datasets import load_metric\n",
-        "from prettytable import PrettyTable\n",
-        "\n",
-        "def print_rouge_table(rouge_scores):\n",
-        "    table = PrettyTable()\n",
-        "    table.field_names = [\"Metric\", \"Confidence\", \"Precision\", \"Recall\", \"F-Measure\"]\n",
-        "\n",
-        "    # Iterate through each rouge type and its corresponding scores\n",
-        "    for rouge_type, aggregate_scores in rouge_scores.items():\n",
-        "        # Iterate through confidence levels: low, mid, high\n",
-        "        for confidence_level in ['low', 'mid', 'high']:\n",
-        "            score = getattr(aggregate_scores, confidence_level)\n",
-        "            table.add_row([\n",
-        "                rouge_type.upper(),\n",
-        "                confidence_level.capitalize(),\n",
-        "                f\"{score.precision:.4f}\",\n",
-        "                f\"{score.recall:.4f}\",\n",
-        "                f\"{score.fmeasure:.4f}\"\n",
-        "            ])\n",
-        "\n",
-        "    # Print the table\n",
-        "    print(table)\n",
-        "\n",
-        "# Call the function to print the table\n",
-        "print_rouge_table(rouge_score)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
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-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
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-        "outputId": "7b0b638f-028b-403f-e694-77929871f5fb"
-      },
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-          "text": [
-            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
-            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-          ]
-        }
-      ],
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-        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2).to(device)"
-      ]
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