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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shakilkhan24/Playground_DL/blob/main/playing_HF_custom_data_torch_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_kRYoe_oAre",
        "outputId": "c4808c0b-a425-4fe8-ebd7-4b6bb4b4ce99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully installed datasets-2.16.1 dill-0.3.7 multiprocess-0.70.15\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Conversion of HF data to pytorch\n",
        "```\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FHYndbLS-CEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "data=load_dataset('glue','mrpc')"
      ],
      "metadata": {
        "id": "8UWuwc84oJXD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sFpqStCs_A6G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with HuggingFace DATAS"
      ],
      "metadata": {
        "id": "eHUaI0_p_Dpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset2 = load_dataset(\"glue\", \"mrpc\", split=\"train\")"
      ],
      "metadata": {
        "id": "AMgnoiK1x2ir"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(data)\n",
        "print('___________seperator____________')\n",
        "print(dataset2)    # only picking train datas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXGUoBmLoX0W",
        "outputId": "5329fcc0-e9c7-4bc9-b226-317856289005"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "        num_rows: 3668\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "        num_rows: 408\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "        num_rows: 1725\n",
            "    })\n",
            "})\n",
            "___________seperator____________\n",
            "Dataset({\n",
            "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
            "    num_rows: 3668\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# casting dataset\n",
        "data.cast()\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "50FzKWCx4u3K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('dataset_features before',dataset2.features)\n",
        "print('_____________________')\n",
        "print('_____________________')\n",
        "\n",
        "from datasets import ClassLabel, Value\n",
        "new_features = dataset2.features.copy()\n",
        "new_features[\"label\"] = ClassLabel(names=[\"negative\", \"positive\"])\n",
        "new_features[\"idx\"] = Value(\"int64\")\n",
        "dataset2 = dataset2.cast(new_features)\n",
        "print('dataset after modifying the features types')\n",
        "print('______________________________')\n",
        "dataset2.features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230,
          "referenced_widgets": [
            "3f2db758fd634d8281bb177ec78ba75b",
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        },
        "id": "PGGDnPpI38r6",
        "outputId": "11e71fb5-82c5-42b3-8592-c9ac29860618"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset_features before {'sentence1': Value(dtype='string', id=None), 'sentence2': Value(dtype='string', id=None), 'label': ClassLabel(names=['not_equivalent', 'equivalent'], id=None), 'idx': Value(dtype='int32', id=None)}\n",
            "_____________________\n",
            "_____________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Casting the dataset:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataset after modifying the features types\n",
            "______________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sentence1': Value(dtype='string', id=None),\n",
              " 'sentence2': Value(dtype='string', id=None),\n",
              " 'label': ClassLabel(names=['negative', 'positive'], id=None),\n",
              " 'idx': Value(dtype='int64', id=None)}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=data.shuffle(seed=42)\n",
        "\n",
        "# we can shuffle the datas before procedding to training loop"
      ],
      "metadata": {
        "id": "T_kbp5wjsNdc"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Image DataSet from Hugging_Face ...\n",
        "\n",
        "\n",
        "```\n",
        "Look like interactible with the data format .\n",
        "We can easily preprocess using pytorch or others.\n",
        "```\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DTtugIdjsN0N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Image\n",
        "\n",
        "dataset = load_dataset(\"beans\", split=\"train\")    # image dataset here"
      ],
      "metadata": {
        "id": "ZVdAXL9joZAF"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)     # work on this"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmWJWaV9o2Zn",
        "outputId": "66100f78-952d-4afc-f902-1e8ac053b271"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['image_file_path', 'image', 'labels'],\n",
            "    num_rows: 1034\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(dataset[0]))\n",
        "print(dataset[0].keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zsr_D0ROz2TA",
        "outputId": "82824145-ed28-4dc8-d433-8fda06908254"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "dict_keys(['image_file_path', 'image', 'labels'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "JUST look at the DATA"
      ],
      "metadata": {
        "id": "xZfb5zSl1rXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "x=pd.DataFrame(dataset)"
      ],
      "metadata": {
        "id": "tUMtXSUK1r9T"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RI9wXnKg1008",
        "outputId": "a1eae91e-1c16-4057-dd79-a8df37b12713"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                     image_file_path  \\\n",
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              "3  /home/albert/.cache/huggingface/datasets/downl...   \n",
              "4  /home/albert/.cache/huggingface/datasets/downl...   \n",
              "\n",
              "                                               image  labels  \n",
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              "\n",
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              "\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0900d5fd-1c5f-404d-b72a-fc5c7b7267bb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# BUT REMEMBER , IT'S THE PYTHON FUNCTIONS, NOT FROM 'PANDAS' LIBARY\n",
        "dataset.map(any_func)\n",
        "dataset.filter(lambda x: something(x))\n",
        "dataset.apply()\n",
        "or more functions you can use\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "Lop4OODa0ljl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Many functions are associated with the Dataset object from hugging face ...\n",
        "# for example dataset.rename_columns,remove_columns,select_columns and other stuffs ,\n",
        "\n",
        "# KIND OF PANDAS DATAFRAME LIKE\n",
        "\n"
      ],
      "metadata": {
        "id": "3Pf8MkEw10KL"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# modified_data=dataset.filter(lambda x: x['image'])\n",
        "\n",
        "# x contains three values , in this case lambda function only returing image\n",
        "#  meaning here you can look your datas in different ways\n"
      ],
      "metadata": {
        "id": "4rg8VuWT0Col"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# dataset.train_test_split(test_size=0.2)\n",
        "Flavor of scikit-learn train_test_split module\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "TLoYBBJj1R6H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can look your datas in different ways..."
      ],
      "metadata": {
        "id": "Thw1ucJNzcQF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sub_set_data=dataset.filter(lambda example:example['labels']==0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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            "8610b96f3636440ca1ab58a9db0ecbed",
            "cc3b2e8bfd9e4f899077ce0ca77eff2b",
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            "ed038c4ba60440bf9742c9d48bcb1d95",
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          ]
        },
        "id": "w7onafaNy6A3",
        "outputId": "e461be3c-41b8-4ce5-ce01-94c13c413da3"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filter:   0%|          | 0/1034 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0c4c1f45a2a489c9ca3c38405e73607"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ways of preprocessing data based on any specific requirements\n",
        "\n",
        "def augment_data(examples):\n",
        "    outputs = []\n",
        "    for sentence in examples[\"sentence1\"]:\n",
        "        words = sentence.split(' ')\n",
        "        # preprocess the data , whatever requirements you have\n",
        "        pass\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Mf_53y-g55hV"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "tokenizer=AutoTokenizer.from_pretrained('bert-base-uncased')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "RFMkKax-6tJJ",
        "outputId": "eaaaecfe-57a1-41fc-f251-16a1fa3c0e92"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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              "model_id": "81c66795dfd3497f97cf7279b3b7ef8a"
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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,
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              "model_id": "295e25603b5b4ae1a970672ba2ea9303"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# PREPROCESSING IS NOT HARD PROBABLY\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "jgdCnBiY7n4Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=dataset2.map(lambda example:tokenizer([example['sentence1'],example['sentence2']],truncation=True,padding=True,return_tensors='pt'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "e71c997c225d4969a3a9b9c277fb9162",
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          ]
        },
        "id": "xHYLT3iM64YE",
        "outputId": "4e440dbf-b710-4d3e-c081-315fe56e77a8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e71c997c225d4969a3a9b9c277fb9162"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)\n",
        "print(y[0].keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFtmgpYh7aUA",
        "outputId": "d584d7d9-ee46-4719-a25c-139413e60a0a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
            "    num_rows: 3668\n",
            "})\n",
            "dict_keys(['sentence1', 'sentence2', 'label', 'idx', 'input_ids', 'token_type_ids', 'attention_mask'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('datas with label=0',len(sub_set_data))\n",
        "print('datas with label=1',len(dataset)-len(sub_set_data))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0vW7Cs_zzLnS",
        "outputId": "02d43dcc-28f8-45e4-dfda-2bf2490ea056"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "datas with label=0 345\n",
            "datas with label=1 689\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtiacRjpo4Jz",
        "outputId": "39ce2adc-4d9f-4c6d-b7f3-d0c14becba52"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'image_file_path': '/home/albert/.cache/huggingface/datasets/downloads/extracted/967f0d9f61a7a8de58892c6fab6f02317c06faf3e19fba6a07b0885a9a7142c7/train/angular_leaf_spot/angular_leaf_spot_train.0.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x500 at 0x7DC2006EA920>, 'labels': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_image = dataset[0]['image']\n",
        "first_image.show()"
      ],
      "metadata": {
        "id": "YISLCppbrxZm"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# import matplotlib.pyplot as plt\n",
        "# from PIL import Image\n",
        "# path=dataset[0]['image_file_path']\n",
        "# x=Image.open(path)\n",
        "# plt.imshow(x)\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "0TIJKMMspdiS"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path_of_image=dataset[0]['image_file_path']\n",
        "print(path_of_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z_DSA8WGp82t",
        "outputId": "28773d51-9f61-4c18-a68c-9c3f4b94f420"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/home/albert/.cache/huggingface/datasets/downloads/extracted/967f0d9f61a7a8de58892c6fab6f02317c06faf3e19fba6a07b0885a9a7142c7/train/angular_leaf_spot/angular_leaf_spot_train.0.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Za3vn7ohqBuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "WAY OF PICKING DATA_FOLDER FROM HUGGING FACE DATA HUB..."
      ],
      "metadata": {
        "id": "4vKpv6SbvWca"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "folder_path='/content/drive/MyDrive/data_set/classifier_data/Animals'\n",
        "\n",
        "custom_data = load_dataset(\"Folder\", data_dir=folder_path)\n",
        "\n",
        "# There is an error, becuase we can only load data from hugging face hub\n",
        "# Not our custom data using the load_dataset_module\n"
      ],
      "metadata": {
        "id": "YtQJbkzntS29"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bZghMQKv6RDW"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# pytorch custom data class"
      ],
      "metadata": {
        "id": "LRssIYpN_T2S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# example contains\n",
        "dict_keys(['sentence1', 'sentence2', 'label', 'idx', 'input_ids',\n",
        "'token_type_ids', 'attention_mask'])\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "8F-6wcX5_1YX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TEXT DATA"
      ],
      "metadata": {
        "id": "34er2JlSBXeO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "from transformers import Trainer, TrainingArguments\n",
        "\n",
        "# Load dataset\n",
        "dataset = load_dataset(\"glue\", \"mrpc\")\n",
        "\n",
        "# Load pre-trained tokenizer and model\n",
        "tokenizer = DistilBertTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
        "model = DistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n",
        "\n",
        "# Tokenize and preprocess dataset\n",
        "def tokenize_function(example):\n",
        "    return tokenizer(example[\"sentence1\"], example[\"sentence2\"], truncation=True)\n",
        "\n",
        "tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "# Convert tokenized dataset to PyTorch Dataset\n",
        "class TorchDataset(Dataset):\n",
        "    def __init__(self, examples):\n",
        "        self.examples = examples\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.examples[\"input_ids\"])\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return {\n",
        "            \"input_ids\": self.examples[\"input_ids\"][idx],\n",
        "            \"attention_mask\": self.examples[\"attention_mask\"][idx],\n",
        "            \"labels\": self.examples[\"label\"][idx]\n",
        "        }\n",
        "\n",
        "train_dataset = TorchDataset(tokenized_dataset[\"train\"])\n",
        "eval_dataset = TorchDataset(tokenized_dataset[\"validation\"])\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
        "eval_dataloader = DataLoader(eval_dataset, batch_size=8)\n",
        "\n",
        "# # Define training arguments\n",
        "# training_args = TrainingArguments(\n",
        "#     per_device_train_batch_size=8,\n",
        "#     per_device_eval_batch_size=8,\n",
        "#     num_train_epochs=3,\n",
        "#     logging_dir='./logs',\n",
        "#     logging_steps=100,\n",
        "# )\n",
        "\n",
        "# # Define Trainer\n",
        "# trainer = Trainer(\n",
        "#     model=model,\n",
        "#     args=training_args,\n",
        "#     train_dataset=train_dataset,\n",
        "#     eval_dataset=eval_dataset,\n",
        "# )\n",
        "\n",
        "# # Train the model\n",
        "# trainer.train()\n",
        "\n",
        "# # Evaluate the model\n",
        "# trainer.evaluate()\n"
      ],
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        "colab": {
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        "outputId": "92efbf60-26b0-4197-9ca5-d8cf47cec7c1"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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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": {
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        },
        {
          "output_type": "display_data",
          "data": {
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              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
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              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'classifier.bias', 'pre_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": [
              "Map:   0%|          | 0/3668 [00:00<?, ? examples/s]"
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          "data": {
            "text/plain": [
              "Map:   0%|          | 0/408 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "602ae2f09f674b9ba4e54643689ed6a7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Map:   0%|          | 0/1725 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e5319daacdf4fd5b161fd1c233bed58"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "IMAGE DATA"
      ],
      "metadata": {
        "id": "sU2L4HoyBfC3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset({\n",
        "    features: ['image_file_path', 'image', 'labels'],\n",
        "    num_rows: 1034\n",
        "})"
      ],
      "metadata": {
        "id": "nSDJ891tDIr6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from datasets import load_dataset\n",
        "from torchvision import transforms\n",
        "from PIL import Image\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "class CustomImageDataset(Dataset):\n",
        "    def __init__(self, dataset_name, split_name, transform=None):\n",
        "        self.dataset = load_dataset(dataset_name, split=split_name)\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        image_path = self.dataset[idx]['image_file_path']\n",
        "        image = Image.open(image_path)\n",
        "        label = self.dataset[idx]['label']\n",
        "\n",
        "        if self.transform:\n",
        "            image = self.transform(image)\n",
        "\n",
        "        return image, label\n",
        "\n",
        "# Define transforms for preprocessing\n",
        "transform = transforms.Compose([\n",
        "    transforms.Resize((224, 224)),  # Resize the image to 224x224\n",
        "    transforms.ToTensor(),           # Convert PIL Image to tensor\n",
        "    transforms.Normalize(            # Normalize image tensor\n",
        "      mean=[0.485, 0.456, 0.406],   # Mean values for RGB channels\n",
        "      std=[0.229, 0.224, 0.225]     # Standard deviations for RGB channels\n",
        "    )\n",
        "])\n",
        "\n",
        "# Create custom dataset instance\n",
        "dataset = CustomImageDataset(dataset_name=\"beans\", split_name=\"train\", transform=transform)\n",
        "\n",
        "# Example usage\n",
        "# Accessing a sample image and label\n",
        "sample_image, sample_label = dataset[0]\n",
        "print(\"Sample image shape:\", sample_image.shape)\n",
        "print(\"Sample label:\", sample_label)\n"
      ],
      "metadata": {
        "id": "KR3zz4uiBdxv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EuakwPUB_s6",
        "outputId": "ff043611-ea64-40c8-8023-ac5c2fa3c8b5"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['image_file_path', 'image', 'labels'],\n",
            "    num_rows: 1034\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VXBLpXtcCBPz"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}