GPT_AND_BERT_fine_tunning_huggingface.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "with open('data.txt', 'r') as file:\n",
        "    # Read the entire content of the file\n",
        "    data = file.read()\n",
        "\n",
        "# # Print the content\n",
        "# print(data)\n"
      ],
      "metadata": {
        "id": "KXnX6ljd9L8Z"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "tokens = tokenizer(data, padding=True, truncation=True, return_tensors=\"pt\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fONWYRVN-VD_",
        "outputId": "ffe3bbc0-ded1-4298-a253-375773e95a12"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification\n",
        "\n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "0ca17394792c4a37aa7e72dba30e9cd9",
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            "d183df4590324651b46f7418b1e4db00",
            "8cf92370ee2e4c36976b2354516d72bd",
            "38dd1e72e64541ad9ab514c2f0796ef9",
            "1f90b837f6a8425ebe39ec1370046b5d",
            "0787e8b21fa24eb6b200a4b399b9218c"
          ]
        },
        "id": "gLm6LjhX-ghq",
        "outputId": "c4c153ae-c31f-47cb-fe55-cd0a2a202920"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ca17394792c4a37aa7e72dba30e9cd9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqtoKJWh-yaM",
        "outputId": "ca38a9c0-beca-4362-a88b-93d0ec49699a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting datasets\n",
            "  Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n",
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            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\n",
            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.17-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.9)\n",
            "Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.24.7)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.13.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.22.0->datasets) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n",
            "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Downloading datasets-3.0.1-py3-none-any.whl (471 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m471.6/471.6 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "dataset = Dataset.from_dict({\"text\": [data]})\n"
      ],
      "metadata": {
        "id": "f1Ke7WTs-v07"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sGmNz1Z-xDf",
        "outputId": "1fc4cd76-499c-45e7-96b1-ee2082d34ef4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['text'],\n",
              "    num_rows: 1\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True)"
      ],
      "metadata": {
        "id": "91Jo24CxAZQm"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = dataset.map(tokenize_function, batched=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "a730a924a5284bb3aeffceb9c2160da5",
            "1bcc6db200c24ae898b4fb82387ea565",
            "e1443cf3ef7742139610eb514a129790",
            "1c28e2de42044a4f9a9c72445d578da1",
            "5f412789e3974ad09ecd2c6837d6ecff",
            "aa62e320030444e3b4ae5a7de7598757",
            "ece54aa274c340bbba416feec5f3510c",
            "1044c1d2f4704bfabb6381d035b18cf7",
            "071f041cafa74fa2ba47087b8f5fa9d2",
            "d97a8e1f219449f8b6b73a0746ca940f",
            "9eece6a1589443d49947672d78c567cd"
          ]
        },
        "id": "wWl5butWAfNr",
        "outputId": "ae130ef8-ebb6-4aea-d51c-3ab089cf29ce"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a730a924a5284bb3aeffceb9c2160da5"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tokenized_dataset = tokenized_dataset.rename_column(\"labels\", \"label\")\n",
        "tokenized_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\"])"
      ],
      "metadata": {
        "id": "yE_n92QaAozs"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = tokenized_dataset\n",
        "eval_dataset = tokenized_dataset"
      ],
      "metadata": {
        "id": "tqiVVp7OAsrv"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',  # Where to save the model\n",
        "    evaluation_strategy=\"epoch\",  # Evaluate every epoch\n",
        "    per_device_train_batch_size=8,  # Training batch size\n",
        "    per_device_eval_batch_size=8,  # Evaluation batch size\n",
        "    num_train_epochs=2002,  # Number of training epochs\n",
        "    logging_dir='./logs',  # Directory for storing logs\n",
        "    logging_steps=10,\n",
        ")\n",
        "\n",
        "# Initialize Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=eval_dataset,\n",
        ")\n",
        "\n",
        "trainer.train()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "WOWbYEmd-5Dv",
        "outputId": "44545e22-97f1-4876-ca90-3be7ec3422b1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "The model did not return a loss from the inputs, only the following keys: logits. For reference, the inputs it received are input_ids,attention_mask.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-fdf0c87247d6>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m )\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1936\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1938\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1939\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1940\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2278\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2279\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2281\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   3316\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3317\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3318\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3320\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   3379\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3380\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"loss\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3381\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   3382\u001b[0m                     \u001b[0;34m\"The model did not return a loss from the inputs, only the following keys: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3383\u001b[0m                     \u001b[0;34mf\"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The model did not return a loss from the inputs, only the following keys: logits. For reference, the inputs it received are input_ids,attention_mask."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model('./results')  # Saves the model\n",
        "tokenizer.save_pretrained('./results')  # Saves the tokenize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOzGfh4yC6UO",
        "outputId": "aa8f01a7-72e0-487c-a026-0be5a803da1a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./results/tokenizer_config.json',\n",
              " './results/special_tokens_map.json',\n",
              " './results/vocab.txt',\n",
              " './results/added_tokens.json',\n",
              " './results/tokenizer.json')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xs8sBPTV-87S",
        "outputId": "f9e1869c-8bf8-48fc-8dcb-69e005f6b627"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.24.7)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.16.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.6.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQ0cjhHCBHkI",
        "outputId": "60e44a07-41c6-4aae-9909-cc7ddddcd0ba"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertForSequenceClassification, BertTokenizer\n",
        "model = BertForSequenceClassification.from_pretrained('./results')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "model.push_to_hub(\"zulqarnain-kernel/Mokalma\")\n",
        "tokenizer.push_to_hub(\"zulqarnain-kernel/Mokalma\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205,
          "referenced_widgets": [
            "da6e340e23234d198dc98b4015532b04",
            "54536c9435324284b7ca870f3e35fb70",
            "98bbe6db15ca4bf7a0b807836386b47d",
            "ccc04f92eafd4a28bdeb6d9ac2dc0843",
            "19eddcd5d9924785afa28ba259aabac6",
            "f4f62e750ae54cbf9094a903fec36150",
            "3f6c03e52c834ad09cea4b1e0f5fadcb",
            "8527366b2a69473eb5e9c09c760c76ec",
            "e5b8ffe3d2d0487581c95ebd16aee4a8",
            "d7c07f91cd2b461586d5ebf57c84335c",
            "02eef62b7e54483087d631905befeee8",
            "3a3b82a8ad134ccfbe76a2504381fdbd",
            "fa7a6a8ec36d4bf2a40a7f793a30c5eb",
            "d3f229af52374538af4b85d88d399a16",
            "c34af6611b71431ba85691c1f13ed015",
            "895c352a3f504eefb0e6417c71012b73",
            "f4a7b41e37e34f66bf60b601d3321476",
            "d1fc8d1aea4a4c9eabb8ae315329d38a",
            "2a1649ad698e47adb464c61f9ed74696",
            "69581351dbb54d1ab8815f88fb23d542",
            "925072b82d14446494d6b0e888c2e6fd",
            "10b9e9527af54864b91df012f14e57bc"
          ]
        },
        "id": "KpjbIU0CB5YE",
        "outputId": "aa25d9f6-d052-4dad-c457-e1f87e56e71f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da6e340e23234d198dc98b4015532b04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a3b82a8ad134ccfbe76a2504381fdbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/zulqarnain-kernel/Mokalma/commit/eb411fa6db735c1b7ee6b2c82c4a17668f5d3f6c', commit_message='Upload tokenizer', commit_description='', oid='eb411fa6db735c1b7ee6b2c82c4a17668f5d3f6c', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "\n",
        "model_name = \"zulqarnain-kernel/Mokalma\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "\n",
        "\n",
        "input_text = \"Let's call it 1981.\"\n",
        "\n",
        "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\n",
        "\n",
        "\n",
        "output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)\n",
        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cX3WBCT-CWSN",
        "outputId": "256581d6-3d5d-4064-b32e-e690281c4920"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`\n",
            "Some weights of BertLMHeadModel were not initialized from the model checkpoint at zulqarnain-kernel/Mokalma and are newly initialized: ['cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.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": "stream",
          "name": "stdout",
          "text": [
            "let's call it 1981.ratactuswon yong paudant cnorraoukhendranindenndenstarɴemiarnornoɴmerie decimalninenine dopinglang hari hari barre dopinggia harieme ﬁllusgree manisas dl cn dopingeral decimal downed nwa ponce nationale dopingresh decimal someonedonnhamgreeicogicgreegic handicaptouyedtmndenico fright frightystdersɴsparthorpethorpe dlbainndi decimal mn galacticburnbainwonsasthorpenalrnoyedigen barretouwonɴ\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments\n",
        "from datasets import Dataset\n",
        "import torch\n",
        "\n",
        "def load_text_from_file(file_path):\n",
        "    with open(file_path, 'r', encoding='utf-8') as file:\n",
        "        text_data = file.read()\n",
        "    return text_data\n",
        "\n",
        "text_data = load_text_from_file('data.txt')\n",
        "\n",
        "def split_text(text, chunk_size=1024):\n",
        "    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]\n",
        "\n",
        "chunks = split_text(text_data)\n",
        "\n",
        "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
        "model = GPT2LMHeadModel.from_pretrained('gpt2')\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    tokens = tokenizer(examples['text'], padding=\"max_length\", truncation=True, max_length=1024)\n",
        "    tokens[\"labels\"] = tokens[\"input_ids\"].copy()  # Labels should be the same as input_ids\n",
        "    return tokens\n",
        "\n",
        "\n",
        "data = {\"text\": chunks}\n",
        "dataset = Dataset.from_dict(data)\n",
        "\n",
        "tokenized_dataset = dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "tokenized_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
        "\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=4,\n",
        "    per_device_eval_batch_size=4,  # Adjust according to GPU memory\n",
        "    logging_dir='./logs',\n",
        "    logging_steps=100,\n",
        "    save_steps=500,\n",
        "    save_total_limit=2,\n",
        "    prediction_loss_only=True,  # Only compute the loss\n",
        ")\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_dataset,\n",
        ")\n",
        "\n",
        "\n",
        "trainer.train()\n",
        "\n",
        "model.save_pretrained('./fine-tuned-gpt2')\n",
        "tokenizer.save_pretrained('./fine-tuned-gpt2')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "outputId": "ca446098-d81a-438e-bebc-6baf089640d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/222 [00:00<?, ? examples/s]"
            ],
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    {
      "cell_type": "code",
      "source": [
        "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
        "\n",
        "\n",
        "model = GPT2LMHeadModel.from_pretrained(\"zulqarnain-kernel/mokalma-gpt2\")  # Load from Hugging Face Hub\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(\"zulqarnain-kernel/mokalma-gpt2\")  # Load from Hugging Face Hub\n",
        "\n",
        "\n",
        "def generate_text(prompt, max_length=1028, num_return_sequences=1):\n",
        "\n",
        "    input_ids = tokenizer.encode(prompt, return_tensors='pt')\n",
        "\n",
        "\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        max_length=max_length,\n",
        "        num_return_sequences=num_return_sequences,\n",
        "        no_repeat_ngram_size=2,\n",
        "        do_sample=True,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "        temperature=1.0\n",
        "    )\n",
        "\n",
        "    # Decode and print the generated text\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "    return generated_text\n",
        "\n",
        "# Example usage\n",
        "prompt = \"I don't know anything about it.\"\n",
        "generated_text = generate_text(prompt, max_length=1028)\n",
        "print(\"Generated Text:\\n\", generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799,
          "referenced_widgets": [
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        "id": "vFlcp0vLFiQ2",
        "outputId": "5d0aafa3-2aae-49df-f117-e5de3b4dee59"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/920 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4250d9ba8afe48daa84c829f9c714cee"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/498M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f0c9559b7d441db84d7beba98d9b908"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/119 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3f37a5e711654bcb84ba2ed2ac4d85d8"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/526 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b7d1ae5db48470da57bb4f6dfaed954"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/999k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "1db85d2cc7a1475eb5df5b4e8cce3e9e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/583 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3824737cc254b7faf0ff7bde3386342"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Text:\n",
            " I don't know anything about it.\n",
            "\n",
            "   We stay in a taxi for a while, but after about 30 mins or so we realize it's too dangerous to get out. So we run down\n",
            "  into the street, see nobody but the guy we started the taxi with,\n",
            "two bouncers and two kids playing on the subway car's front\n",
            "door. And then--\n",
            "\n",
            "\n",
            "47  EXT. CITY HOSPITAL - MORNING  (TECHNICAL PLATFORM)  47  INT. SUBWAY HALL - LATER 47\n",
            "\n",
            " Joker steps out of his apartment and walks across the crowded\n",
            "tiled floor, toward the small TV set where he lives. The sound of a\n",
            "reminder machine buzzes through the narrow hallway-- \n",
            "\n",
            "48  BLAM! Out of nowhere, a UPS driver falls off a balcony-- Dead\n",
            "32  in all! Joker grabs his bag and goes toward--\n",
            "49  FLASHBACK! Three cops on horseback running down the alley behind\n",
            "50  BOBBY SHORT (SAVING) TRAFFIC LAND, chasing after him-- CLOSE ON JOKER, blood\n",
            "paint smearing his face. \n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"Joker asked\"\n",
        "generated_text = generate_text(prompt, max_length=1028)\n",
        "print(\"Generated Text:\\n\", generated_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wJvPe5Z9LxLF",
        "outputId": "37fc51e0-90b1-45dd-9970-b3a69f455771"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generated Text:\n",
            " Joker asked if I was interested in writing.\n",
            "   It was a personal matter and he said, \"I don't really have\n",
            "a story yet.\"\n",
            "\n",
            "  His mom looks at him, and as he starts to fill in the blank,\n",
            "laughter subsides.\n",
            "\n",
            "\n",
            "95  INT. MOM'S APARTMENT, LIVING ROOM - DAY 95  95\n",
            "\n",
            " Joker is alone in her apartment. She's dressed in a\n",
            "silky white, faded, worn-out red bathrobe and\n",
            "clothes line her face. Her hair's slicked back. This isn't a big deal. It's\n",
            "the apartment she used to live in when she was young. A cramped\n",
            "room, filthy floor, a TV in one corner and two small\n",
            "lights that can't be turned on. The only sound is the harsh rustling of\n",
            "her hair, the rustle of her shoes. \n",
            "\n",
            " 95 of 96 \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yxvnRTofMBCY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}