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  "cells": [
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      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/parvpareek/hindi-text-summarization/blob/main/alzheimers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
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      "execution_count": 1,
      "metadata": {
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        "colab": {
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting huggingface-cli\n",
            "  Downloading huggingface_cli-0.1-py3-none-any.whl (1.0 kB)\n",
            "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[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.41.1)\n",
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
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            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.0.3)\n",
            "Collecting requests>=2.32.1 (from datasets)\n",
            "  Downloading requests-2.32.3-py3-none-any.whl (64 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.4)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading 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[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading 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[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]<=2024.3.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (3.7)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.1->datasets) (2024.2.2)\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) (2023.4)\n",
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            "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",
            "Installing collected packages: huggingface-cli, 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 huggingface-cli-0.1 multiprocess-0.70.16 requests-2.32.3 xxhash-3.4.1\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.10/dist-packages (0.13.1)\n",
            "Collecting sklearn\n",
            "  Downloading sklearn-0.0.post12.tar.gz (2.6 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.30.1-py3-none-any.whl (302 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m4.6 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[31m51.6 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",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\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-0.30.1 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",
            "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"
          ]
        }
      ],
      "source": [
        "# pip installations\n",
        "!pip install huggingface-cli datasets transformers\n",
        "!pip install matplotlib seaborn sklearn\n",
        "!pip install accelerate -U\n",
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"parvpareek/pitt\")"
      ],
      "metadata": {
        "id": "U2KS99k8A2w0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350,
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          ]
        },
        "outputId": "c8104499-4922-421a-e0cf-712bfaaf9357"
      },
      "execution_count": 2,
      "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": [
              "Downloading readme:   0%|          | 0.00/599 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "844a91f539d346ca83bf0c070562569e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "24c8cbb8aec54bd18784749936e29faa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "27c30291ef5049d79c3e2b5ea939cdde"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4b9e658289fd4d4e8eaa00207191ceae"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/439 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef9dc550137042df8863d2909008054a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/55 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "030f5189daa24b4ebf793ab2896f3868"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/55 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3d3ad397e384f00967923e2626911ea"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
        "model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
        "tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')\n",
        "# Sample text\n",
        "text = \"Replace this text with your own text.\"\n",
        "\n",
        "# Tokenize text\n",
        "tokens = tokenizer(text, return_tensors=\"pt\")\n",
        "\n",
        "# Pass input through BERT model\n",
        "with torch.no_grad():\n",
        "    outputs = model(**tokens)\n",
        "\n",
        "# Extract embeddings from BERT model outputs\n",
        "#embeddings = outputs.last_hidden_state\n",
        "\n",
        "# Print shape of embeddings\n",
        "#print(\"Shape of embeddings:\", embeddings.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416,
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        "id": "wpuIaAvLBFh_",
        "outputId": "249a65cc-835b-4da4-da69-8a81b5a757bf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb2632c562724d04abe24bf98d177039"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5dcacb6f2c6f4a08b013e66ee5c7f6c3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "15aa6a4e56794ac78a6134190c5a4363"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "86cddc41b10f497d978166f54c52d4a6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "37785f95e4de49db8b7114eb457bffa7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'torch' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-706cef2f6500>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# Pass input through BERT model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'torch' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(example):\n",
        "\n",
        "  tokenized_input = tokenizer(example[\"transcript\"], padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
        "  example[\"input_ids\"] = tokenized_input.input_ids\n",
        "  example[\"attention_mask\"] = tokenized_input.attention_mask\n",
        "  #example[\"length\"] = [sum(x) for x in tokenized_input.attention_mask]\n",
        "  #example[\"tl\"] = [len(x) for x in example[\"transcript\"]]\n",
        "  return example\n"
      ],
      "metadata": {
        "id": "Ns3YrnMlGcw3"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(tokenize_function, batched=True, batch_size=8, remove_columns=[\"patient_id\", \"transcript\"])"
      ],
      "metadata": {
        "id": "hZFnsInCFbb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "0bc7b361954143fead35d14a77fd12df",
            "b78de26a42e344159a7a28e7355d2e2a",
            "54a4a38b429548618897e0729ab9e022",
            "355038e09a184f4183afaf00066a31d9",
            "246270cfc59d40f1af96520112005e0c",
            "7dd62a6ece754fb3b8ee054993d928b8",
            "66ee7cca720942889f26979348b65ef6",
            "15c2cdf8afcb4087a8f42116426cae75",
            "72556f1b3af04982b027e4823b36b28d",
            "7ad51bfce5764ce697fd05b54e1fe6d4",
            "436cc25276ab47f5add52465e636eff8",
            "447bb61d0d8f4300a1e529c0170b594a",
            "89dbfa926ed144299340491f0bf7e598",
            "aa8c53bba9c740a7afd6242d60368c57",
            "305252f08fc040b99229adaefbd2aef8",
            "f14c25e233394fdeb4ff36623f55e052",
            "01e7524d47684e9e95a48067b8b9f966",
            "9a0f051c42d14df8b964ee1ec3ec0427",
            "5fbd874a62144cb29c8b9824d2a533c3",
            "96127ca6a5c2425787a4e2c826f4fa7b",
            "026cea7d77a84c2b97a8eb37c77ba8df",
            "0e30c5b597ad4aa2ae2521cf251c95f8",
            "e5f7e1e300d24ff2a76a28a14a4a5904",
            "e12dca8ab661465fb2d2f8001a1f00ab",
            "e7128b0e26b545308a1b691a5eff5c80",
            "9c8d684f9b584721a160c1ea9e377abe",
            "a70bf573a5814518b8af16f887db4348",
            "f26529aee95145dbabbb95cf7eae7906",
            "09150158031c49cdb34f056dc14cce32",
            "3737327996e74756b0559a370f97f4ec",
            "21a53984570c4a7a9e1e02c88f982b0e",
            "50235f50e52a4bb3bdc76155d62dad4f",
            "e26b2699eab445b1a2494c626e874d7e"
          ]
        },
        "outputId": "d89c0f83-51db-4259-cd0b-2ad4b2ab0712"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/439 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0bc7b361954143fead35d14a77fd12df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/55 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "447bb61d0d8f4300a1e529c0170b594a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/55 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5f7e1e300d24ff2a76a28a14a4a5904"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = tokenized_dataset.rename_column(\"category\", \"label\")"
      ],
      "metadata": {
        "id": "bmuit2w_GYFv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "a557b1ef-d507-48ba-c56f-899d268fafb5"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Original column name category not in the dataset. Current columns in the dataset: ['label', 'input_ids', 'attention_mask']",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-c14a7332d98f>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokenized_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenized_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"category\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"label\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mrename_column\u001b[0;34m(self, original_column_name, new_column_name)\u001b[0m\n\u001b[1;32m    406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_values_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m         return DatasetDict(\n\u001b[0;32m--> 408\u001b[0;31m             {\n\u001b[0m\u001b[1;32m    409\u001b[0m                 \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_column_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moriginal_column_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_column_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_column_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    407\u001b[0m         return DatasetDict(\n\u001b[1;32m    408\u001b[0m             {\n\u001b[0;32m--> 409\u001b[0;31m                 \u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_column_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moriginal_column_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_column_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_column_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    410\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m             }\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    600\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    603\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/fingerprint.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    480\u001b[0m             \u001b[0;31m# Call actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m             \u001b[0;31m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mrename_column\u001b[0;34m(self, original_column_name, new_column_name, new_fingerprint)\u001b[0m\n\u001b[1;32m   2257\u001b[0m         \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeepcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2258\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moriginal_column_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2259\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   2260\u001b[0m                 \u001b[0;34mf\"Original column name {original_column_name} not in the dataset. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2261\u001b[0m                 \u001b[0;34mf\"Current columns in the dataset: {dataset._data.column_names}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Original column name category not in the dataset. Current columns in the dataset: ['label', 'input_ids', 'attention_mask']"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import DatasetDict, Dataset\n",
        "\n",
        "split_size = 0.1\n",
        "sub_train_size = int(tokenized_dataset[\"train\"].num_rows * split_size)\n",
        "sub_test_size = int(tokenized_dataset[\"test\"].num_rows * split_size)\n",
        "sub_val_size = int(tokenized_dataset[\"validation\"].num_rows * split_size)\n",
        "\n",
        "train_subset = Dataset.from_dict(tokenized_dataset[\"train\"].shuffle(seed=42)[:sub_train_size])\n",
        "test_subset = Dataset.from_dict(tokenized_dataset[\"test\"].shuffle(seed=42)[:sub_test_size])\n",
        "val_subset = Dataset.from_dict(tokenized_dataset[\"validation\"].shuffle(seed=42)[:sub_val_size])\n"
      ],
      "metadata": {
        "id": "ugFhNvwWztXt"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset = DatasetDict({\n",
        "    'train': train_subset,\n",
        "    'test': test_subset,\n",
        "    'validation': val_subset\n",
        "})\n",
        "subset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rbosel-UzyS-",
        "outputId": "795e6091-4250-4220-b6c6-5bfa48b9b401"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 43\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 5\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['label', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 5\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "AxedNGseLa00"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = 'bert-base-cased'\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          # output directory\n",
        "    num_train_epochs=10,\n",
        "    eval_strategy=\"epoch\",# number of training epochs\n",
        "    per_device_train_batch_size=8,   # batch size for training\n",
        "    per_device_eval_batch_size=8,    # batch size for evaluation                # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        "    logging_steps=10,\n",
        ")\n"
      ],
      "metadata": {
        "id": "juu-Xdmsy_R7"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n"
      ],
      "metadata": {
        "id": "cLTKFO5L0CNg"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def compute_metrics(eval_pred):\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "    return metric.compute(predictions=predictions, references=labels)\n"
      ],
      "metadata": {
        "id": "brYnqVNo0el7"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer, padding=True)"
      ],
      "metadata": {
        "id": "1LbFn_ta30EQ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=subset[\"train\"],\n",
        "    eval_dataset=subset[\"validation\"],\n",
        "    compute_metrics=compute_metrics,\n",
        "    tokenizer=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    optimizers=(torch.optim.AdamW(model.parameters(), lr=2e-5), None),\n",
        "\n",
        ")"
      ],
      "metadata": {
        "id": "uw6Sy-om077D"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "0_6P0rt908uq",
        "outputId": "8a7aeec5-0f72-4449-fba5-9e41a9ab8979"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 30:11, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.773405</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.639300</td>\n",
              "      <td>0.640287</td>\n",
              "      <td>0.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.639300</td>\n",
              "      <td>0.607140</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.477000</td>\n",
              "      <td>0.586579</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.296500</td>\n",
              "      <td>0.530963</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.296500</td>\n",
              "      <td>0.542927</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.209100</td>\n",
              "      <td>0.512207</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.209100</td>\n",
              "      <td>0.512869</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.515053</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.143500</td>\n",
              "      <td>0.521865</td>\n",
              "      <td>0.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=60, training_loss=0.32156895399093627, metrics={'train_runtime': 1858.0437, 'train_samples_per_second': 0.231, 'train_steps_per_second': 0.032, 'total_flos': 81826510439400.0, 'train_loss': 0.32156895399093627, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "9aokzY4xEqh8",
        "outputId": "dfa2e767-ed89-4e95-992f-05048d719d75"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1/1 : < :]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.5218647122383118,\n",
              " 'eval_accuracy': 0.8,\n",
              " 'eval_runtime': 3.1612,\n",
              " 'eval_samples_per_second': 1.582,\n",
              " 'eval_steps_per_second': 0.316,\n",
              " 'epoch': 10.0}"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Custom Training Loop"
      ],
      "metadata": {
        "id": "ad2uytzk7Mzc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertForSequenceClassification\n",
        "from datasets import load_dataset, Dataset\n",
        "from torch.utils.data import DataLoader\n",
        "from transformers import AdamW, get_scheduler\n",
        "from tqdm.auto import tqdm\n"
      ],
      "metadata": {
        "id": "G_iLdULO7Wd3"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer, padding=True)"
      ],
      "metadata": {
        "id": "05l-HIGKLp4J"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 8\n",
        "train_dataloader = DataLoader(\n",
        "    subset[\"train\"],\n",
        "    shuffle=True,\n",
        "    collate_fn=data_collator,\n",
        "    batch_size=batch_size,\n",
        ")\n",
        "eval_dataloader = DataLoader(\n",
        "    subset[\"validation\"], collate_fn=data_collator, batch_size=batch_size\n",
        ")\n"
      ],
      "metadata": {
        "id": "z2lcZ1wh7Yth"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.optim import AdamW\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=1e-5)\n"
      ],
      "metadata": {
        "id": "zhMop9Hn7kM1"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_train_epochs = 20\n",
        "num_update_steps_per_epoch = len(train_dataloader)\n",
        "num_training_steps = num_train_epochs * num_update_steps_per_epoch\n",
        "\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps,\n",
        ")\n"
      ],
      "metadata": {
        "id": "aOlMw0pL7mDS"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "num_epochs = 10\n",
        "for epoch in range(num_epochs):\n",
        "    # Training\n",
        "    model.train()\n",
        "    for batch in train_dataloader:\n",
        "        inputs = {k: v.to(device) for k, v in batch.items()}\n",
        "        outputs = model(**inputs)\n",
        "        loss = outputs.loss\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        lr_scheduler.step()\n",
        "        optimizer.zero_grad()\n",
        "        progress_bar.update(1)\n",
        "\n",
        "    # Evaluation\n",
        "    model.eval()\n",
        "    eval_loss = 0\n",
        "    eval_steps = 0\n",
        "    correct_predictions = 0\n",
        "    with torch.no_grad():\n",
        "        for batch in eval_dataloader:\n",
        "            inputs = {k: v.to(device) for k, v in batch.items()}\n",
        "            outputs = model(**inputs)\n",
        "            loss = outputs.loss\n",
        "            eval_loss += loss.item()\n",
        "            eval_steps += 1\n",
        "\n",
        "            logits = outputs.logits\n",
        "            predictions = torch.argmax(logits, dim=-1)\n",
        "            correct_predictions += (predictions == inputs[\"labels\"]).sum().item()\n",
        "\n",
        "    avg_eval_loss = eval_loss / eval_steps\n",
        "    accuracy = correct_predictions / len(subset[\"validation\"])\n",
        "    print(f\"Epoch: {epoch + 1}, Eval Loss: {avg_eval_loss}, Accuracy: {accuracy}\")\n"
      ],
      "metadata": {
        "id": "DHWv2Pym7vfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "415735452a184424b970dc45b45b0d02",
            "9988ac71d63b4fb28a4c793793229b37",
            "f258142ab54e459db68ab90c711ac996",
            "120975cfff5444d8866454ceeabc0ece",
            "1f08872f444e42ceaaa8c930889d6c0b",
            "12e49d25f20746a69df5b147506e1d00",
            "c577423fe4f94bf0855fa61d2a6aea17",
            "2df7bbaebd464865910abfbc2b2b4ecd",
            "f76ffdbd71924f998ca493cc852b8707",
            "e7ca0746279b41f88c4eef95fe1e82d7",
            "5efe7364576d47ce9388707c74996010"
          ]
        },
        "outputId": "342f8d8f-59fc-4fdd-9f44-39c6d4965617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/120 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "415735452a184424b970dc45b45b0d02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1, Eval Loss: 0.714084804058075, Accuracy: 0.4\n",
            "Epoch: 2, Eval Loss: 0.7163178324699402, Accuracy: 0.2\n",
            "Epoch: 3, Eval Loss: 0.7075097560882568, Accuracy: 0.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer_vocab_size = len(tokenizer.get_vocab())\n",
        "tokenizer_vocab_size"
      ],
      "metadata": {
        "id": "G0VllHA2Dj1d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_weight_size = model.get_input_embeddings().weight.size(0)"
      ],
      "metadata": {
        "id": "Rq7le3d5Ftnq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if tokenizer_vocab_size > embedding_weight_size:\n",
        "    raise ValueError(\"The vocabulary size of the tokenizer is larger than the size of the embedding weight matrix in the model.\")"
      ],
      "metadata": {
        "id": "t4WN-5oLFwWb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_weight_size"
      ],
      "metadata": {
        "id": "LI98WlDzF24a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_embedding_weight = torch.zeros(tokenizer_vocab_size, embedding_weight_size)\n",
        "new_embedding_weight[:embedding_weight_size] = model.get_input_embeddings().weight\n",
        "model.get_input_embeddings().weight = torch.nn.Parameter(new_embedding_weight)"
      ],
      "metadata": {
        "id": "KHYu7hWOF3FJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "[x < tokenizer.vocab_size for x in tokenized_dataset[\"train\"][\"input_ids\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "J11M1vWBsgqa",
        "outputId": "ba1f64ee-1419-4517-c4e6-a5f79da35d47"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "'<' not supported between instances of 'list' and 'int'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-dedd78650446>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab_size\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtokenized_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_ids\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-29-dedd78650446>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab_size\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtokenized_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_ids\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'list' and 'int'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = [torch.tensor(x) for x in tokenized_dataset[\"train\"][\"input_ids\"]]"
      ],
      "metadata": {
        "id": "QkVJonQ4tFy4"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "_KV97O5-vBfD",
        "outputId": "48834501-5ce8-43cd-d076-167769bf538f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "stack expects each tensor to be equal size, but got [182] at entry 0 and [145] at entry 8",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-dc956189edf9>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m: stack expects each tensor to be equal size, but got [182] at entry 0 and [145] at entry 8"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i,x in enumerate(t):\n",
        "  print(i)\n",
        "  assert torch.all(x < tokenizer.vocab_size )"
      ],
      "metadata": {
        "id": "uCUaaBSUNWMz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "20d479ec-c4fe-4fdb-8fdf-ab93e6f74506"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "error",
          "ename": "AssertionError",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-099797b211b5>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0;32massert\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab_size\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAssertionError\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i, x in enumerate(dataset[\"validation\"][\"transcript\"]):\n",
        "  to = tokenizer(x, return_tensors=\"pt\")\n",
        "  z = torch.tensor(to[\"input_ids\"])\n",
        "\n",
        "  if torch.all(z < tokenizer.vocab_size):\n",
        "    print(i)\n",
        "\n",
        "  else:\n",
        "    print(z)\n",
        "    print(tokenizer.decode(z))\n",
        "    print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ER-RRAWnuqBe",
        "outputId": "df0ed13d-528a-4e16-c08e-07751b94b5b7"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-68-793c4f93118f>:3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  z = torch.tensor(to[\"input_ids\"])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19xjQG3I6_JI",
        "outputId": "c40814a0-b69a-48e2-a004-933100f86c99"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "439\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tokenizer.decode(tokenized_dataset[\"train\"][\"input_ids\"][0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3k-ypnHw0iw",
        "outputId": "50ba228e-1c50-41cc-dc8c-8c0b00e0e5f4"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[CLS] an average home that looks very much like ours at many times gram and the mother is well she's spilling her water which is not very good but she's doing washing dishes and drying them and one of the children upset the cookie jar and that's an a f p very average thing to go in on s r in a home our home is a very it's a very active family and so that it makes it kinda hard at our house sometime because uh there's a lot of and ac like i said a lot of activity well the poor girl is spilling the water on her feet it's gonna be and k wet but then she'll hafta struggle with that also there are a few more dishes left that didn't get finished this hasta be the dishes and how that little boy fell off that stool i don't know but he did no [SEP] [PAD] [PAD] [PAD]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AnTkJqIc71qM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}