fineTuningBert_CR_k-Fold.ipynb

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        "In this notebook, we will use Hugging face [Transformers](https://huggingface.co/transformers/) to build BERT model on text classification task with [Pytorch](https://pytorch.org).\n",
        "\n",
        "Notes: this notebook is entirely run on [Google colab](https://colab.research.google.com/) with GPU. If you start a new notebook, you need to choose \"Runtime\"->\"Change runtime type\" ->\"GPU\" at the begining."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Introduction of BERT"
      ],
      "metadata": {
        "id": "__46lmYK6BSI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art language model developed by Google AI. It is a deep learning model that can learn to represent the meaning of words and phrases in a text. BERT is based on the Transformer architecture, which is a neural network architecture that is particularly well-suited for natural language processing tasks"
      ],
      "metadata": {
        "id": "hoi41TmH59l4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Introduction of huggingface or Transformers"
      ],
      "metadata": {
        "id": "Z6j4XpZp6vSZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hugging face is a company which invented a pacakge called [Transformers](https://github.com/huggingface/transformers). It provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch."
      ],
      "metadata": {
        "id": "FcL8VTH56unB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7MhPt2Cd_a_"
      },
      "source": [
        "# Install dependencies"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets transformers accelerate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8cpfDFz-2m6",
        "outputId": "64323f6c-00f8-42d3-e6a4-32034cd785f8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-2.18.0-py3-none-any.whl (510 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m6.8 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 (4.38.2)\n",
            "Collecting accelerate\n",
            "  Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\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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            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
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            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "  Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
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            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "Installing collected packages: xxhash, 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, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, nvidia-cusolver-cu12, datasets, accelerate\n",
            "Successfully installed accelerate-0.28.0 datasets-2.18.0 dill-0.3.8 multiprocess-0.70.16 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-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#parameters"
      ],
      "metadata": {
        "id": "U3P-HpoATiaW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "you can change parameter here"
      ],
      "metadata": {
        "id": "dVQ3OAvfTkGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "FOLDS = 10 #@param {\"type\": \"integer\"}\n",
        "SEED = 0  #@param {\"type\": \"integer\"}\n",
        "MAX_LENGTH = 256 #@param {\"type\": \"integer\"}\n",
        "BATCH_SIZE = 32 #@param {\"type\": \"integer\"}\n",
        "learning_rate = 1e-5 #@param {\"type\": \"number\"}\n",
        "num_epochs = 6 #@param {\"type\": \"integer\"}\n",
        "\n",
        "# print 3 times befor ending epoch\n",
        "printFreq = 24  #@param {\"type\": \"integer\"}\n",
        "SAVE_DIR = \"/content/drive/MyDrive/nlp/datasets/CR_DATASET\" #@param{\"type\": \"string\"}\n",
        "zip_path = '/content/drive/MyDrive/nlp/datasets/CR_DATASET/CR_DATASET.zip' #@param{\"type\": \"string\"}\n",
        "folder_path = '/content/content/drive/MyDrive/nlp/datasets/CR_DATASET/' #@param{\"type\": \"string\"}"
      ],
      "metadata": {
        "id": "9GnrhoeKNp4e"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "mount to google drive for saving purposes"
      ],
      "metadata": {
        "id": "I7Z3fv-yT_O-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "e98Fo7_NDq7J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1acf78ca-5baf-4d7c-a77c-33d11b771cd3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Load Dataset"
      ],
      "metadata": {
        "id": "f_noxHi_TO6e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Customer Reviews (CR) dataset is a widely used benchmark dataset in the field of natural language processing (NLP) and sentiment analysis. It consists of a large collection of customer reviews from various domains such as electronics, movies, books, and more. Each review in the dataset is accompanied by a corresponding sentiment label indicating whether the sentiment expressed in the review is positive, negative, or neutral. The CR dataset serves as a valuable resource for researchers and practitioners to develop and evaluate sentiment analysis models, assess the effectiveness of different NLP techniques, and explore the nuances of sentiment expression in real-world customer feedback. Its diverse range of domains and sentiments make it an ideal choice for training and testing sentiment analysis algorithms across different contexts and applications."
      ],
      "metadata": {
        "id": "_ANiKNEJTbTY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "L5wBb8WH-XIq"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "from datasets import load_dataset, concatenate_datasets, load_from_disk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"SetFit/CR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
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        },
        "id": "UF_KkU1W-vUs",
        "outputId": "2ea28a8a-792f-42b5-d00a-299f4cd5679d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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%|          | 0.00/255 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5bc6e2d08a3e4918a169173d7d606c59"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/repocard.py:105: UserWarning: Repo card metadata block was not found. Setting CardData to empty.\n",
            "  warnings.warn(\"Repo card metadata block was not found. Setting CardData to empty.\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "803220b7a0794833951c5287f38aa604"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/52.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9cb9dd049894464ab1af7480c6e38206"
            }
          },
          "metadata": {}
        },
        {
          "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": "c27b347a564747c58af758e63d013b62"
            }
          },
          "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": "4684850674df45818264744d1bb1727f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jt3IPAyz_O_I",
        "outputId": "04733616-5256-4836-95ce-c9fbbb7a406f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 3394\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 376\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets.dataset_dict import DatasetDict\n",
        "crDataset = DatasetDict({\"train\": concatenate_datasets([dataset['train'], dataset['test']])})\n",
        "crDataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUnQtlfE_Psh",
        "outputId": "ba85a2a4-43e4-4897-b5bd-ff90e29e83e7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 3770\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(crDataset['train'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfIWtxWEN_bE",
        "outputId": "13e6a4ed-49f2-4cbd-802c-83b77978ad89"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3770"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross validation"
      ],
      "metadata": {
        "id": "PlltyR9uUVxO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "perform k-fold cross validation on CR dataset\n",
        "\n",
        "split train dev test : 80/10/10 %"
      ],
      "metadata": {
        "id": "KplzCGDwUp62"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "def cross_validation (FOLDS, SEED):\n",
        "  '''\n",
        "  perform a k-fold cross validation on CR dataset and save it to the save_dir\n",
        "  '''\n",
        "  #first applying shuffle\n",
        "  crDataset['train'] = crDataset['train'].shuffle(seed= SEED)\n",
        "\n",
        "  for fold in range(1, FOLDS+1):\n",
        "    num_samples = len(crDataset['train'])\n",
        "    #divide samples to FOLDS part and every iteration select one part\n",
        "    start_index = int((fold-1)*(num_samples/FOLDS))\n",
        "    end_index = int((fold*(num_samples/FOLDS)))\n",
        "\n",
        "    testDataset = crDataset['train'].select(range(start_index, end_index))\n",
        "    trainDataset = concatenate_datasets([crDataset['train'].select(range(0, start_index)), crDataset['train'].select(range(end_index, num_samples-1))])\n",
        "    trainDataset = trainDataset.shuffle(seed=SEED)\n",
        "    #split trian/ dev\n",
        "    #90%, 10%\n",
        "    train_dataset, validation_dataset= trainDataset.train_test_split(test_size=0.1).values()\n",
        "    #put everything in datasetDict object\n",
        "    dataset = DatasetDict({\"train\": train_dataset, \"validation\": validation_dataset, \"test\":testDataset})\n",
        "    #save each dataset fold inorder to access them later\n",
        "\n",
        "    dataset.save_to_disk(os.path.join(SAVE_DIR, f\"fold-{fold}-CR\"))\n",
        "\n",
        "cross_validation(FOLDS, SEED)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "36702cd7ce5749c1825606780931bfaa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1808da90e194ee1ae3c3b5a85f097f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05bd6f79af394c47a4f79f38103d8820"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4911ed1c93a7436a85b9da3d81cdb2e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "30b81531870d415a92d42b6e4c8ae370"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "a0d65cb76973465897812791d6848c3d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "8895c156f2724ed6af975d2f8b1a5282"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# zip file\n",
        "!zip -r '/content/drive/MyDrive/nlp/datasets/CR_DATASET/CR_DATASET.zip' '/content/drive/MyDrive/nlp/datasets/CR_DATASET'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZGvFFleQlLF",
        "outputId": "c6abfae4-ae70-4888-c282-e49c44cd985f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/train/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/validation/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-1-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-2-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-3-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/train/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-4-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/validation/data-00000-of-00001.arrow (deflated 62%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/validation/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-5-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/train/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/validation/data-00000-of-00001.arrow (deflated 62%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-6-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-7-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/validation/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/test/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-8-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/train/state.json (deflated 41%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/validation/data-00000-of-00001.arrow (deflated 62%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-9-CR/test/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/dataset_dict.json (deflated 5%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/train/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/train/data-00000-of-00001.arrow (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/train/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/train/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/validation/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/validation/data-00000-of-00001.arrow (deflated 62%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/validation/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/validation/dataset_info.json (deflated 65%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/test/ (stored 0%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/test/data-00000-of-00001.arrow (deflated 63%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/test/state.json (deflated 42%)\n",
            "  adding: content/drive/MyDrive/nlp/datasets/CR_DATASET/fold-10-CR/test/dataset_info.json (deflated 65%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load each Fold\n"
      ],
      "metadata": {
        "id": "2RmMXZdka74U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note:** if you want to run this notebook you should run cross validation once and run this section k-times and finetune model base on each fold and eventualy compute average accuracy and F1-score"
      ],
      "metadata": {
        "id": "AMavaERJeQVL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "using two function we first unzip the k-fold cross validation dataset and then load each fold using 'load_dataset_fold' function\n",
        "\n"
      ],
      "metadata": {
        "id": "s_fgPeicbJBb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#load dataset\n",
        "import zipfile\n",
        "import os\n",
        "from datasets import load_from_disk\n",
        "import os\n",
        "\n",
        "def unzip_file(zip_path, extract_path):\n",
        "    \"\"\"\n",
        "    Unzip a zip file to the specified directory.\n",
        "\n",
        "    Args:\n",
        "    - zip_path (str): The path to the zip file.\n",
        "    - extract_path (str): The directory where the contents of the zip file will be extracted.\n",
        "    \"\"\"\n",
        "    with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n",
        "        zip_ref.extractall(extract_path)\n",
        "\n",
        "def load_dataset_fold(num_fold, dir=folder_path):\n",
        "\n",
        "  # Load each fold separately for training\n",
        "  if(num_fold <=0 or num_fold>10):\n",
        "    return \"it's 10 fold cross-validation please enter valid number in range 1-10!\"\n",
        "  dataset = load_from_disk(os.path.join(dir, f\"fold-{num_fold}-CR\"))\n",
        "  return dataset\n",
        "\n",
        "\n",
        "\n",
        "unzip_file(zip_path, '')\n"
      ],
      "metadata": {
        "id": "WGDkmfG4CjQZ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cFold = 1 #@param{\"type\": \"integer\"}"
      ],
      "metadata": {
        "id": "KO1L2fMspskw"
      },
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dataset = load_dataset_fold(cFold)\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBMKjUM-SyLc",
        "outputId": "170485fb-28f2-43b4-9868-91ec5453e7c7"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 3053\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 340\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 377\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Preprocess\n",
        "\n",
        "The next step is to load a BERT tokenizer to preprocess the `text` field"
      ],
      "metadata": {
        "id": "ViXCwHTTbmcj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertTokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")"
      ],
      "metadata": {
        "id": "QrNszRZTSSs8"
      },
      "execution_count": 222,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "  '''\n",
        "  Preprocesses the input examples for the NLP model.\n",
        "\n",
        "    Args:\n",
        "        examples (List[Dict]): A list of input examples, where each example is a dictionary\n",
        "            containing the following keys:\n",
        "                - \"sentence\": The text of the example.\n",
        "\n",
        "    Returns:\n",
        "        Dict: A dictionary containing the preprocessed examples.\n",
        "  '''\n",
        "  return tokenizer(examples[\"text\"],  truncation=True, max_length= MAX_LENGTH )\n",
        "\n",
        "train_tokenized = dataset['train'].map(tokenize_function, batched = True)\n",
        "valid_tokenized = dataset['validation'].map(tokenize_function, batched = True)\n",
        "test_tokenized = dataset['test'].map(tokenize_function, batched = True)\n"
      ],
      "metadata": {
        "id": "_9Wqi_A8SYyr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
            "5678ce3614ad481d93381eca9f6249ec",
            "6bf878ef485e42cb9aec80b01c5b2663",
            "471d27281f234af1b05f5da88d7fc15b",
            "4879f6fef4ae49f09d003b48a4409f39",
            "57389309ac774d8685e5ff96c1a81767",
            "288eeba88a9c4e2bb2200b9644e0a717",
            "0c4e3eff122e46e5adff57e0c5a26e17",
            "33806c87e6a840058dc965fccc661515",
            "7dbd4f2465004d318ec02da3814f9b38",
            "057f40abf2b744cfac37c1400a154835",
            "f35a28da599f46fd901674c451fb9843",
            "6abfb1ebb4514d2a99d1148a873f37de",
            "7e8668b81c454fb2897bed53b0467598",
            "380b42f87c2140beb5879939d3251b01",
            "c79cc7e82a7a4db5834bf8c5648a63ea",
            "b5f525300eee4727b6aedc490cb3c167",
            "0c7653752efc4a57960038e1ca743d61",
            "bbd42d1d8edf499797b6040e4a608017",
            "99a85495e0ce4460860c64255b5e929b",
            "7fcbca01146c474a81fa04e6271066c4",
            "ecf17ca3da404922aca8a4e9716bd503",
            "d2454b11f2a24f3d830ba097c40de599",
            "c9dd9d7775a64f52bef9eb95a72eee2b",
            "65a2dc063a8c4e4d902629d932d162e3",
            "b506cb9a64e34c3da1bc2b83b9df7616",
            "5cae4207fe434f8a9a0a4556c100f38d",
            "ecf006e33d8942bf92578e926d143595",
            "021aa5cbb1fa4005a94cd30500ccb1b1",
            "cb456f9ac4894a3c93ed884aa8535ba6",
            "f09768cb240c4fa1a1014c6bc9fb96fa",
            "c3a2dc3414534a1995f6f12130a1c20b",
            "be65f2938e584c34bcda58c55ee27e8b",
            "c622415b10ac4823b2e45d52f80ab5e3"
          ]
        },
        "outputId": "817f5248-d47a-48a5-cf23-4282a1d768ba"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3053 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5678ce3614ad481d93381eca9f6249ec"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/340 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6abfb1ebb4514d2a99d1148a873f37de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/377 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c9dd9d7775a64f52bef9eb95a72eee2b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_tokenized.set_format('torch', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\n",
        "valid_tokenized.set_format('torch', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\n",
        "test_tokenized.set_format('torch', columns=['input_ids', 'token_type_ids', 'attention_mask', 'label'])\n",
        "train_tokenized, valid_tokenized, test_tokenized"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCeiG757yZUJ",
        "outputId": "36978c74-5ad9-45b1-a55c-ad65aad122c7"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Dataset({\n",
              "     features: ['text', 'label', 'label_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "     num_rows: 3053\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['text', 'label', 'label_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "     num_rows: 340\n",
              " }),\n",
              " Dataset({\n",
              "     features: ['text', 'label', 'label_text', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "     num_rows: 377\n",
              " }))"
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataloader and Dynamic padding"
      ],
      "metadata": {
        "id": "A3GTBvHoWVqL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dynamic padding means the samples in a batch should all be padded to a length of the maximum length of samples in that batch . Without dynamic padding, all of the samples would have to be padded to the maximum length in the whole dataset, or the maximum length the model can accept"
      ],
      "metadata": {
        "id": "jmiq2C3sb-qj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DataCollatorWithPadding\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "data_collator = DataCollatorWithPadding(tokenizer= tokenizer)\n",
        "\n",
        "train_dataloader = DataLoader(train_tokenized, batch_size= BATCH_SIZE, shuffle=True, collate_fn=data_collator)\n",
        "valid_dataloader = DataLoader(valid_tokenized, batch_size= BATCH_SIZE, shuffle= True, collate_fn= data_collator)\n",
        "test_dataloader = DataLoader(test_tokenized, batch_size= BATCH_SIZE, shuffle=True, collate_fn= data_collator)"
      ],
      "metadata": {
        "id": "fdEG-Vku1w1p"
      },
      "execution_count": 225,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model"
      ],
      "metadata": {
        "id": "zmYXlVqYWvMj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "in this notebook we are going to use [BertModel](https://https://huggingface.co/google-bert/bert-base-uncased)\n",
        "\n",
        "Model description: BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way"
      ],
      "metadata": {
        "id": "oFGRoEOBcTcr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loss function\n"
      ],
      "metadata": {
        "id": "xyAFJxpbIBj4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label.\n",
        "\n",
        "\n",
        "Cross-Entropy Loss=\n",
        "\n",
        "$$ \\text{CrossEntropyLoss} = -\\frac{1}{N} \\sum_{i=1}^{N} \\log\\left(\\frac{e^{x_{\\text{predicted},i}}}{\\sum_{j=1}^{C} e^{x_{\\text{predicted},j}}}\\right)$$\n",
        "\n",
        "\n",
        "- N is the batch size.\n",
        "\n",
        "- C is the number of classes.\n",
        "\n",
        "- x predicted,i is the unnormalized predicted score for class i (logit).\n",
        "- The sum over j in the denominator is the sum of exponentiated predicted scores for all classes in the batch.\n",
        "- The logarithm calculates the log probability of the predicted class."
      ],
      "metadata": {
        "id": "CgsSmV0iDfif"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from transformers import BertModel\n",
        "\n",
        "class Bert(nn.Module):\n",
        "  '''\n",
        "  bert model for binary text classification\n",
        "  '''\n",
        "  def __init__(self, name =\"bert-base-uncased\", num_classes =2):\n",
        "    super(Bert, self).__init__()\n",
        "    self.bert = BertModel.from_pretrained(name)\n",
        "    #add drop out and classifier layers\n",
        "    self.dp = nn.Dropout(0.1)\n",
        "    self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes) #768,2\n",
        "    self.criterion = nn.CrossEntropyLoss()\n",
        "\n",
        "  def forward(self, input_ids, token_type_ids, attention_mask, labels=None):\n",
        "    outputs = self.bert(input_ids= input_ids, attention_mask= attention_mask, token_type_ids= token_type_ids)\n",
        "    CLS = outputs[0][:, 0, :].view(-1,768)\n",
        "    output = self.dp(CLS)\n",
        "    logits = self.classifier(output)\n",
        "    loss = self.criterion(logits, labels)\n",
        "    return {\n",
        "        'loss':loss,\n",
        "        'logits':logits\n",
        "    }\n"
      ],
      "metadata": {
        "id": "9aeGNBHcz29Y"
      },
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "move model to GPU if its available"
      ],
      "metadata": {
        "id": "vKESlVLod9YL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Bert()\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P83a5Ywg1Fk4",
        "outputId": "47299b4a-9adf-48e3-f590-f10c5764a7dd"
      },
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bert(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dp): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              "  (criterion): CrossEntropyLoss()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#optimizer"
      ],
      "metadata": {
        "id": "s5LrKF4ZD5ke"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Adam optimizer, also known as Adaptive Moment Estimation, is a stochastic gradient descent optimization algorithm that is commonly used for training deep learning models."
      ],
      "metadata": {
        "id": "gVHuAvjseDys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AdamW\n",
        "optimizer = AdamW(model.parameters(), lr=learning_rate)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABskwmfQ1HiQ",
        "outputId": "b6b3f574-8c9a-4062-baa8-df78bcdb8818"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:429: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Learning rate Scheduler\n"
      ],
      "metadata": {
        "id": "TmRZFX-wEiT1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The purpose of using a learning rate scheduler is to improve the training process and optimize the model's performance.\n",
        "A learning rate scheduler allows you to dynamically adjust the learning rate based on certain conditions or predefined schedules. This can help the model converge faster and achieve better accuracy."
      ],
      "metadata": {
        "id": "E__7NzMkeHSL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import get_scheduler\n",
        "\n",
        "num_training_steps = num_epochs*len(train_dataloader)\n",
        "lr_scheduler = get_scheduler(\n",
        "    \"linear\",\n",
        "    optimizer=optimizer,\n",
        "    num_warmup_steps=0,\n",
        "    num_training_steps=num_training_steps\n",
        ")"
      ],
      "metadata": {
        "id": "1MNGitVQ3sev"
      },
      "execution_count": 229,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Accelerator"
      ],
      "metadata": {
        "id": "KTrXG0bEFTNF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accelerate is a high-level library that simplifies and accelerates distributed training and evaluation for PyTorch and Transformers models. It provides a user-friendly API that abstracts away the complexities of distributed training, making it easier to train models on multiple GPUs or TPUs."
      ],
      "metadata": {
        "id": "KqOBdHLoeJ37"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from accelerate import Accelerator\n",
        "accelerator = Accelerator()\n",
        "\n",
        "train_dataloader, valid_dataloader, test_dataloader, model, optimizer = accelerator.prepare(train_dataloader, valid_dataloader, test_dataloader, model, optimizer)"
      ],
      "metadata": {
        "id": "97rHsrNWFU7T"
      },
      "execution_count": 230,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Training"
      ],
      "metadata": {
        "id": "ZzyysmeaIfIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_training_steps"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AH87M2jIJ1cM",
        "outputId": "d221ef7f-6bde-447a-f3eb-e5fb1d7f7c33"
      },
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "576"
            ]
          },
          "metadata": {},
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_dataloader)*32, len(train_tokenized)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1g5XSxYLk1L",
        "outputId": "be8f05e1-9b01-43e0-9968-d65e735a0d39"
      },
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3072, 3053)"
            ]
          },
          "metadata": {},
          "execution_count": 232
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_epoch(train_dataloader, model, accelerator, epoch, num_epochs, device):\n",
        "  \"\"\"\n",
        "  Trains the provided model for a single epoch using the given training data loader.\n",
        "\n",
        "  Args:\n",
        "    train_dataloader (DataLoader): The DataLoader object containing the training data batches.\n",
        "    model (torch.nn.Module): The model to be trained.\n",
        "    accelerator (Accelerator): The accelerator object responsible for distributed training acceleration.\n",
        "    epoch (int): The current epoch number.\n",
        "    num_epochs (int): The total number of epochs for training.\n",
        "    device (torch.device): The device (e.g., 'cuda' or 'cpu') on which the model is being trained.\n",
        "\n",
        "  Returns:\n",
        "    float: The average loss over all batches in the epoch.\n",
        "  \"\"\"\n",
        "\n",
        "  epoch_loss = 0.0\n",
        "  model.train()\n",
        "  for idx, batch in enumerate(train_dataloader):\n",
        "    batch = {k:v.to(device) for k,v in batch.items()}\n",
        "    output = model(**batch)\n",
        "    loss = output['loss']\n",
        "    epoch_loss += loss.item()\n",
        "    #back prop\n",
        "    accelerator.backward(loss)\n",
        "    #update parameters\n",
        "    optimizer.step()\n",
        "    lr_scheduler.step()\n",
        "    optimizer.zero_grad()\n",
        "    progress_bar.update(1)\n",
        "    if(idx % printFreq == 0):\n",
        "      print(f\"epoch {epoch+1}/{num_epochs} --> iteration {idx}, loss for last batch: {loss}\")\n",
        "\n",
        "  print(f\"average_loss = {epoch_loss / len(train_dataloader)}\")\n",
        "  return epoch_loss/len(train_dataloader)\n"
      ],
      "metadata": {
        "id": "mgxu0TflK7V7"
      },
      "execution_count": 233,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "def validate_epoch(valid_dataloader, model, accelerator, epoch, num_epochs, device):\n",
        "   \"\"\"\n",
        "    Validates the performance of the model on the validation dataset for a single epoch.\n",
        "\n",
        "    Args:\n",
        "        valid_dataloader (DataLoader): The DataLoader object containing the validation data batches.\n",
        "        model (torch.nn.Module): The model to be validated.\n",
        "        accelerator (Accelerator): The accelerator object responsible for distributed training acceleration.\n",
        "        epoch (int): The current epoch number.\n",
        "        num_epochs (int): The total number of epochs for training.\n",
        "        device (torch.device): The device (e.g., 'cuda' or 'cpu') on which the model is being trained.\n",
        "\n",
        "    Returns:\n",
        "        tuple: A tuple containing the accuracy, F1 score, and average loss for the epoch.\n",
        "   \"\"\"\n",
        "\n",
        "   true_labels = []\n",
        "   predicted_labels = []\n",
        "   epoch_loss = 0.0\n",
        "\n",
        "\n",
        "   model.eval()\n",
        "\n",
        "   for idx, batch in enumerate(valid_dataloader):\n",
        "\n",
        "    batch = {k: v.to(device) for k, v in batch.items()}\n",
        "    with torch.no_grad():\n",
        "\n",
        "      outputs = model(**batch)\n",
        "      loss = outputs['loss']\n",
        "\n",
        "      epoch_loss +=loss.item()\n",
        "\n",
        "      ## calculate acc\n",
        "      labels = batch.pop('labels')\n",
        "      softmax_output = torch.nn.functional.softmax(outputs['logits'], dim=1)\n",
        "      y_pred = softmax_output.argmax(dim=1)\n",
        "\n",
        "      # Convert tensors to numpy arrays and append to the lists\n",
        "      true_labels.extend(labels.cpu())\n",
        "      predicted_labels.extend(y_pred.cpu())\n",
        "\n",
        "\n",
        "    accuracy = accuracy_score(true_labels, predicted_labels)\n",
        "    f1 = f1_score(true_labels, predicted_labels)\n",
        "    avg_loss = epoch_loss /len(valid_dataloader)\n",
        "\n",
        "   print(f\"epoch {epoch+1}/{num_epochs} results on validation acc:{accuracy}, f1:{f1}, avg_loss:{avg_loss} \")\n",
        "   return accuracy, f1, avg_loss\n",
        "\n"
      ],
      "metadata": {
        "id": "c1CBvrp5ScDS"
      },
      "execution_count": 234,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Loop"
      ],
      "metadata": {
        "id": "uzmbDfM9fh_C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "progress_bar = tqdm(range(num_training_steps))\n",
        "\n",
        "trian_epoch_loss = []\n",
        "valid_epoch_loss = []\n",
        "total_loss = 0.0\n",
        "best_accuracy = 0.0\n",
        "patience = 4\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  avg_train_loss = train_epoch(train_dataloader, model, accelerator, epoch, num_epochs, device)\n",
        "  acc, f1, avg_valid_loss = validate_epoch(valid_dataloader, model, accelerator, epoch, num_epochs, device)\n",
        "  if(acc> best_accuracy):\n",
        "    best_accuracy = acc\n",
        "    early_stopping_counter = 0\n",
        "    torch.save(model.state_dict(), f'best_model_{cFold}.pth')\n",
        "    print(\"saving best model to 'best_model.pth'... \")\n",
        "\n",
        "  else:\n",
        "      early_stopping_counter += 1\n",
        "      if early_stopping_counter >= patience:\n",
        "          print(f\"Early stopping: No improvement in {patience} epochs.\")\n",
        "          break\n",
        "\n",
        "print(f\"Best validation Accuracy : {best_accuracy:.4f}\")\n",
        "print(\"Best model saved as 'best_model.pth'\")\n",
        "\n"
      ],
      "metadata": {
        "id": "XucWxnn9FgFt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 761,
          "referenced_widgets": [
            "ce134a14632d4d62bfde4f9b4745a738",
            "b8663e5ed9cb44dfa201423f54511551",
            "092a4f4fcb0b4cfa9f42c1dd0730d047",
            "0c21bdabf1d84e13b614f89fd82f46cd",
            "a78d8f0939cd4d6fa6f9494074bfab3c",
            "0262533781f2460d87aa6dfc09f6deae",
            "50233d2dbf5f4af5b1d1b3a8dd0f853d",
            "9b94113f0f804e7a8666e082b023e3b2",
            "b0d772dfabdb48bda7845a615794eefc",
            "9608ec7041004945addd103c1835e5bb",
            "4a561abc7147462ba0787d76eedc7a4d"
          ]
        },
        "outputId": "703a61e4-15ec-4542-8cd3-ec86de92b919"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/576 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce134a14632d4d62bfde4f9b4745a738"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "epoch 1/6 --> iteration 0, loss for last batch: 0.7574394345283508\n",
            "epoch 1/6 --> iteration 24, loss for last batch: 0.6075069904327393\n",
            "epoch 1/6 --> iteration 48, loss for last batch: 0.453380823135376\n",
            "epoch 1/6 --> iteration 72, loss for last batch: 0.3640196919441223\n",
            "average_loss = 0.4514495865441859\n",
            "epoch 1/6 results on validation acc:0.888235294117647, f1:0.9155555555555556, avg_loss:0.2734760628505187 \n",
            "saving best model to 'best_model.pth'... \n",
            "epoch 2/6 --> iteration 0, loss for last batch: 0.17876149713993073\n",
            "epoch 2/6 --> iteration 24, loss for last batch: 0.11727237701416016\n",
            "epoch 2/6 --> iteration 48, loss for last batch: 0.40878984332084656\n",
            "epoch 2/6 --> iteration 72, loss for last batch: 0.20772378146648407\n",
            "average_loss = 0.20419601739073792\n",
            "epoch 2/6 results on validation acc:0.9088235294117647, f1:0.9280742459396751, avg_loss:0.2376235303553668 \n",
            "saving best model to 'best_model.pth'... \n",
            "epoch 3/6 --> iteration 0, loss for last batch: 0.13691020011901855\n",
            "epoch 3/6 --> iteration 24, loss for last batch: 0.12070094048976898\n",
            "epoch 3/6 --> iteration 48, loss for last batch: 0.10447458922863007\n",
            "epoch 3/6 --> iteration 72, loss for last batch: 0.09088333696126938\n",
            "average_loss = 0.11849116528173909\n",
            "epoch 3/6 results on validation acc:0.9058823529411765, f1:0.9241706161137441, avg_loss:0.24721897664395245 \n",
            "epoch 4/6 --> iteration 0, loss for last batch: 0.10557281225919724\n",
            "epoch 4/6 --> iteration 24, loss for last batch: 0.01867217756807804\n",
            "epoch 4/6 --> iteration 48, loss for last batch: 0.04511990770697594\n",
            "epoch 4/6 --> iteration 72, loss for last batch: 0.061690833419561386\n",
            "average_loss = 0.07771485636476427\n",
            "epoch 4/6 results on validation acc:0.9088235294117647, f1:0.9284064665127021, avg_loss:0.2749057256362655 \n",
            "epoch 5/6 --> iteration 0, loss for last batch: 0.01358901895582676\n",
            "epoch 5/6 --> iteration 24, loss for last batch: 0.05569436401128769\n",
            "epoch 5/6 --> iteration 48, loss for last batch: 0.021224897354841232\n",
            "epoch 5/6 --> iteration 72, loss for last batch: 0.01495428942143917\n",
            "average_loss = 0.05015898254350759\n",
            "epoch 5/6 results on validation acc:0.9147058823529411, f1:0.9339407744874715, avg_loss:0.30006640539927915 \n",
            "saving best model to 'best_model.pth'... \n",
            "epoch 6/6 --> iteration 0, loss for last batch: 0.03909006342291832\n",
            "epoch 6/6 --> iteration 24, loss for last batch: 0.007065822836011648\n",
            "epoch 6/6 --> iteration 48, loss for last batch: 0.18085913360118866\n",
            "epoch 6/6 --> iteration 72, loss for last batch: 0.055315203964710236\n",
            "average_loss = 0.04369592359580565\n",
            "epoch 6/6 results on validation acc:0.9088235294117647, f1:0.929384965831435, avg_loss:0.3131747064604001 \n",
            "Best validation Accuracy : 0.9147\n",
            "Best model saved as 'best_model.pth'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r \"/content/best_model_{cFold}.pth\" '/content/drive/MyDrive/nlp/saved_models/cross_validation'"
      ],
      "metadata": {
        "id": "P4WC_WbAFpDc"
      },
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Evaluate on test dataset"
      ],
      "metadata": {
        "id": "E_vb_NEQj-67"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_state_dict(torch.load(f'best_model_{cFold}.pth'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2AstB1XmnyL",
        "outputId": "2f0f23ce-834f-49df-9f58-f0fb0c154542"
      },
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fold_accuracy=[]\n",
        "#fold_f1=[]"
      ],
      "metadata": {
        "id": "SsxAR9ThpD9W"
      },
      "execution_count": 238,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import torch\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "\n",
        "true_labels= []\n",
        "predicted_labels= []\n",
        "for batch in test_dataloader:\n",
        "  batch = {k:v.to(device) for k,v in batch.items()}\n",
        "\n",
        "\n",
        "  model.eval()\n",
        "  with torch.no_grad():\n",
        "\n",
        "    outputs = model(**batch)\n",
        "    softmax_output = torch.nn.functional.softmax(outputs['logits'], dim=1)\n",
        "    y_pred = softmax_output.argmax(dim=1)\n",
        "    t_labels = batch.pop('labels')\n",
        "    # Convert tensors to numpy arrays and append to the lists\n",
        "    true_labels.extend(t_labels.cpu())\n",
        "    predicted_labels.extend(y_pred.cpu())\n",
        "\n",
        "# Calculate accuracy\n",
        "accuracy = accuracy_score(true_labels, predicted_labels)\n",
        "\n",
        "# Calculate F1 score\n",
        "f1 = f1_score(true_labels, predicted_labels)\n",
        "\n",
        "fold_accuracy.append(accuracy)\n",
        "fold_f1.append(f1)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"F1 Score:\", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SIs3OprCkBaa",
        "outputId": "0c767fc1-ecb1-4cb5-fbb7-c7d64bf34c1d"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9416445623342176\n",
            "F1 Score: 0.9554655870445343\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "def visualize(matrix):\n",
        "\n",
        "  \"\"\"\n",
        "  This function takes in a matrix and generates a heatmap to visualize the data.\n",
        "\n",
        "  Arguments:\n",
        "  - matrix: a 2-dimensional matrix containing the data to be visualized.\n",
        "\n",
        "  Returns:\n",
        "  - None: The function does not return any value. It displays the heatmap plot directly.\n",
        "\n",
        "  \"\"\"\n",
        "  con_mat_df = pd.DataFrame(matrix)\n",
        "  sns.heatmap(con_mat_df, annot=True, fmt='g', cmap=plt.cm.Blues)\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.show()\n",
        "\n",
        "confusion_matrix = confusion_matrix(true_labels, predicted_labels)\n",
        "visualize(confusion_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "Rl4ZvNSmiKTZ",
        "outputId": "989715a7-752a-4d4e-b13a-ed4e6fb92ab9"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fold_accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-uKJ4jSkeMq",
        "outputId": "aad4fe51-9d20-4748-807c-2ec5fa5c9036"
      },
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.8885941644562334,\n",
              " 0.8806366047745358,\n",
              " 0.8806366047745358,\n",
              " 0.8806366047745358,\n",
              " 0.9098143236074271,\n",
              " 0.8859416445623343,\n",
              " 0.9071618037135278,\n",
              " 0.8938992042440318,\n",
              " 0.9283819628647215,\n",
              " 0.9177718832891246,\n",
              " 0.8912466843501327,\n",
              " 0.9416445623342176]"
            ]
          },
          "metadata": {},
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fold_f1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "adRGFzZgktjq",
        "outputId": "3774042d-2bc9-4615-cd33-08e168c019b5"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9139344262295082,\n",
              " 0.9090909090909091,\n",
              " 0.9090909090909091,\n",
              " 0.9090909090909091,\n",
              " 0.9254385964912282,\n",
              " 0.9145129224652087,\n",
              " 0.9272349272349273,\n",
              " 0.9134199134199134,\n",
              " 0.9479768786127167,\n",
              " 0.9333333333333335,\n",
              " 0.9147609147609147,\n",
              " 0.9554655870445343]"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    }
  ]
}