notebooks/14_Transfer_Learning_and_Transformers.ipynb

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        "#Fine-Tuning Transformers with PyTorch and Hugging Face\n",
        "\n",
        "## Introduction\n",
        "This tutorial demonstrates how to fine-tune a pre-trained transformer model for a text classification task using PyTorch and Hugging Face's transformers library. We'll focus on sentiment analysis, modifying a pre-trained model to better understand the sentiment of text. This process is vital for adapting general models to specific needs or datasets.\n",
        "\n",
        "##Setting Up the Environment\n",
        "First, ensure you're using a Google Colab notebook with GPU support for efficient training.\n",
        "\n",
        "##Install Necessary Packages"
      ],
      "metadata": {
        "id": "rTYf3Qb5ReLs"
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      "source": [
        "!pip install -q torch torchvision\n",
        "!pip install -q transformers\n",
        "!pip install -q matplotlib"
      ],
      "metadata": {
        "id": "rIT5VIwyRj8V"
      },
      "execution_count": 1,
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Libraries\n"
      ],
      "metadata": {
        "id": "OR8r7hOtRoEq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import DataLoader, TensorDataset\n",
        "from transformers import AutoModelForSequenceClassification, AutoTokenizer, AdamW\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
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      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Understanding Transformers and Transfer Learning\n",
        "Transformers use self-attention mechanisms to capture relationships in data, excelling in tasks requiring context understanding. Transfer learning adapts a pre-trained model to a new, but related, task, saving on resources and training time.\n",
        "\n",
        "## Loading a Pre-Trained Model\n",
        "We'll start with a model pre-trained on a large corpus and fine-tune it for our specific task."
      ],
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        "model_name = \"distilbert-base-uncased\"\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
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        {
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              "config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "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', 'classifier.bias', 'pre_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"
          ]
        },
        {
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            ],
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    {
      "cell_type": "markdown",
      "source": [
        "## Preparing the Dataset\n",
        "Assume you have a dataset with texts and corresponding sentiment labels. Here's how to preprocess it:"
      ],
      "metadata": {
        "id": "W-S9qRvXR0dd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample data\n",
        "texts = [\"I love this product!\", \"Worst experience ever.\"]\n",
        "labels = [1, 0]  # 1 for positive, 0 for negative sentiment\n",
        "\n",
        "# Tokenize and encode texts\n",
        "inputs = tokenizer(texts, padding=True, truncation=True, return_tensors=\"pt\")\n",
        "labels = torch.tensor(labels)\n",
        "\n",
        "# Create a torch dataset\n",
        "dataset = TensorDataset(inputs['input_ids'], inputs['attention_mask'], labels)\n",
        "loader = DataLoader(dataset, batch_size=2)"
      ],
      "metadata": {
        "id": "UqWU5bdhRyg9"
      },
      "execution_count": 4,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "## Fine-Tuning the Model\n",
        "Adjust the pre-trained model to your specific task by continuing the training process.\n",
        "\n",
        "## Define Optimizer and Training Loop"
      ],
      "metadata": {
        "id": "nmL_xubIR6XQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = AdamW(model.parameters(), lr=1e-5)\n",
        "\n",
        "model.train()\n",
        "for epoch in range(3):  # number of epochs\n",
        "    for batch in loader:\n",
        "        optimizer.zero_grad()\n",
        "        input_ids, attention_mask, labels = batch\n",
        "        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        print(f\"Epoch {epoch}, Loss: {loss.item()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_5FeygJR4KF",
        "outputId": "5cc42564-5eaf-4ae9-f7c5-333fcd9931f6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0, Loss: 0.7034775018692017\n",
            "Epoch 1, Loss: 0.6800037026405334\n",
            "Epoch 2, Loss: 0.6643761396408081\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Save the Fine-Tuned Model\n",
        "After fine-tuning, save the model for later use."
      ],
      "metadata": {
        "id": "6W5cRHRCSBde"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"my_finetuned_model\")"
      ],
      "metadata": {
        "id": "f0vXQLvER-a6"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Making Predictions\n",
        "With the fine-tuned model, you can now make predictions on new data."
      ],
      "metadata": {
        "id": "Xr8iVc7XSHxG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_sentiment(text):\n",
        "    model.eval()\n",
        "    inputs = tokenizer(text, return_tensors='pt')\n",
        "    with torch.no_grad():\n",
        "        logits = model(**inputs).logits\n",
        "    probabilities = torch.nn.functional.softmax(logits, dim=-1)\n",
        "    return probabilities"
      ],
      "metadata": {
        "id": "JyZw3FZESEs-"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing Predictions\n",
        "Visualize the sentiment predictions as a bar chart."
      ],
      "metadata": {
        "id": "PJdQzGRMSNBk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def show_prediction(text):\n",
        "    probabilities = predict_sentiment(text)\n",
        "    plt.bar(range(len(probabilities[0])), probabilities[0])\n",
        "    plt.title('Sentiment Prediction')\n",
        "    plt.xticks(range(len(probabilities[0])), ['Negative', 'Positive'])\n",
        "    plt.ylabel('Probability')\n",
        "    plt.show()\n",
        "\n",
        "# Test with a new text\n",
        "show_prediction(\"I love using transformers for NLP!\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "45ZV9xrkSMab",
        "outputId": "bc573f3c-46ca-4cd0-d31c-47ce0da7be95"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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XS8PNgQMHVFZWVm7PS2hoqHJzcytcplWrVpo3b57effddvfTSS7LZbOratat+/fXXCvtnZGQoMDDQ/oiIiHD6+wAAAHWHyw9LVVWXLl00bNgwxcbGqkePHnrrrbcUEhKi//73vxX2T0tLU0FBgf2xe/fuWq4YAADUJpeeLRUcHCwPDw/l5eU5tOfl5SksLKxSY9SvX18XXXSRtm3bVuHrXl5e8vLyOuNaAQDA2cGl4cbT01MdO3ZUdna2+vfvL0my2WzKzs5WSkpKpcYoKyvT+vXrdeWVV9ZgpZXHKaLAybn69FAA5waXX+cmNTVVSUlJiouLU+fOnZWZmanCwkL7tWyGDRum8PBwZWRkSJImTZqkiy++WC1atNDhw4c1bdo07dy5U7feeqsr3wYAAKgjXB5uhgwZovz8fI0fP165ubmKjY3VkiVL7JOMd+3aJXf3v6YGHTp0SCNGjFBubq6CgoLUsWNHLV++XNHR0a56CwAAoA5xsyzLcnURtenIkSMKDAxUQUGBAgICnD4+h6WAkzPlsBTbOXBqNbGtV+X391l3thQAAMCpEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCh1ItzMnj1bUVFR8vb2Vnx8vFasWFGp5V599VW5ubmpf//+NVsgAAA4a7g83CxatEipqalKT0/X6tWrFRMTo8TERO3fv/+Uy+3YsUPjxo1Tt27daqlSAABwNnB5uJkxY4ZGjBih5ORkRUdHKysrS76+vpo3b95JlykrK9MNN9ygiRMn6oILLjjl+MXFxTpy5IjDAwAAmMul4aakpESrVq1SQkKCvc3d3V0JCQnKyck56XKTJk1SkyZNdMstt5x2HRkZGQoMDLQ/IiIinFI7AACom1wabg4cOKCysjKFhoY6tIeGhio3N7fCZb7++ms999xzevbZZyu1jrS0NBUUFNgfu3fvPuO6AQBA3VXP1QVUxdGjR3XTTTfp2WefVXBwcKWW8fLykpeXVw1XBgAA6gqXhpvg4GB5eHgoLy/PoT0vL09hYWHl+v/888/asWOHrrrqKnubzWaTJNWrV0+bN29W8+bNa7ZoAABQp7n0sJSnp6c6duyo7Oxse5vNZlN2dra6dOlSrn/r1q21fv16rVmzxv64+uqr1atXL61Zs4b5NAAAwPWHpVJTU5WUlKS4uDh17txZmZmZKiwsVHJysiRp2LBhCg8PV0ZGhry9vdW2bVuH5Rs2bChJ5doBAMC5yeXhZsiQIcrPz9f48eOVm5ur2NhYLVmyxD7JeNeuXXJ3d/kZ6wAA4Czh8nAjSSkpKUpJSanwtWXLlp1y2eeff975BQEAgLMWu0QAAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABilWuFm6dKlzq4DAADAKaoVbnr37q3mzZvrscce0+7du51dEwAAQLVVK9zs2bNHKSkpeuONN3TBBRcoMTFRr732mkpKSpxdHwAAQJVUK9wEBwfr7rvv1po1a/Tdd9+pZcuWGj16tJo2baqxY8dq7dq1zq4TAACgUs54QnGHDh2UlpamlJQU/f7775o3b546duyobt26acOGDc6oEQAAoNKqHW5KS0v1xhtv6Morr1RkZKQ+/vhjPfPMM8rLy9O2bdsUGRmpQYMGVWqs2bNnKyoqSt7e3oqPj9eKFStO2vett95SXFycGjZsqAYNGig2NlYvvvhidd8GAAAwTL3qLDRmzBi98sorsixLN910k6ZOnaq2bdvaX2/QoIGmT5+upk2bnnasRYsWKTU1VVlZWYqPj1dmZqYSExO1efNmNWnSpFz/Ro0a6aGHHlLr1q3l6empDz74QMnJyWrSpIkSExOr83YAAIBBqrXnZuPGjZo1a5b27t2rzMxMh2BzQnBwcKVOGZ8xY4ZGjBih5ORkRUdHKysrS76+vpo3b16F/Xv27KkBAwaoTZs2at68ue688061b99eX3/9dXXeCgAAMEy1wk16eroGDRokLy8vh/bjx4/ryy+/lCTVq1dPPXr0OOU4JSUlWrVqlRISEv4qyN1dCQkJysnJOW0dlmUpOztbmzdvVvfu3SvsU1xcrCNHjjg8AACAuaoVbnr16qWDBw+Way8oKFCvXr0qPc6BAwdUVlam0NBQh/bQ0FDl5uaedLmCggL5+fnJ09NTffv21axZs3T55ZdX2DcjI0OBgYH2R0RERKXrAwAAZ59qhRvLsuTm5lau/bffflODBg3OuKjT8ff315o1a7Ry5UpNnjxZqampWrZsWYV909LSVFBQYH9w0UEAAMxWpQnFAwcOlCS5ublp+PDhDoelysrKtG7dOnXt2rXS4wUHB8vDw0N5eXkO7Xl5eQoLCzvpcu7u7mrRooUkKTY2Vj/99JMyMjLUs2fPcn29vLzKHT4DAADmqtKemxOHdizLkr+/v8PhnrCwMI0cOVIvvfRSpcfz9PRUx44dlZ2dbW+z2WzKzs5Wly5dKj2OzWZTcXFxVd4KAAAwVJX23MyfP1+SFBUVpXHjxjnlEFRqaqqSkpIUFxenzp07KzMzU4WFhUpOTpYkDRs2TOHh4crIyJD05xyauLg4NW/eXMXFxfrwww/14osvas6cOWdcCwAAOPtV6zo36enpTitgyJAhys/P1/jx45Wbm6vY2FgtWbLEPsl4165dcnf/awdTYWGhRo8erV9//VU+Pj5q3bq1XnrpJQ0ZMsRpNQEAgLOXm2VZVmU6dujQQdnZ2QoKCtJFF11U4YTiE1avXu20Ap3tyJEjCgwMVEFBgQICApw+ftQDi50+JmCKHVP6uroEp2A7B06tJrb1qvz+rvSem379+tkn5vbv3/+MCgQAAKgplQ43fz8U5czDUgAAAM50xncFBwAAqEsqvecmKCjolPNs/q6iqxcDAADUhkqHm8zMzBosAwAAwDkqHW6SkpJqsg4AAACnqHS4OXLkiP3Uq9PdWbsmTrEGAACojCrNudm3b5+aNGmihg0bVjj/5sQNNcvKypxaJAAAQGVVOtx8/vnnatSokSRp6dKlNVYQAADAmah0uOnRo0eFPwMAANQl1bq3lCQdOnRIzz33nH766SdJUnR0tJKTk+17dwAAAFyhWhfx+/LLLxUVFaWZM2fq0KFDOnTokGbOnKlmzZrpyy+/dHaNAAAAlVatPTd33HGHhgwZojlz5sjDw0OSVFZWptGjR+uOO+7Q+vXrnVokAABAZVVrz822bdt0zz332IONJHl4eCg1NVXbtm1zWnEAAABVVa1w06FDB/tcm7/76aefFBMTc8ZFAQAAVFelD0utW7fO/vPYsWN15513atu2bbr44oslSd9++61mz56tKVOmOL9KAACASqp0uImNjZWbm5ssy7K33XfffeX6XX/99RoyZIhzqgMAAKiiSoeb7du312QdAAAATlHpcBMZGVmTdQAAADhFtS/iJ0kbN27Url27VFJS4tB+9dVXn1FRAAAA1VWtcPPLL79owIABWr9+vcM8nBM30+TGmQAAwFWqdSr4nXfeqWbNmmn//v3y9fXVhg0b9OWXXyouLk7Lli1zcokAAACVV609Nzk5Ofr8888VHBwsd3d3ubu765JLLlFGRobGjh2rH374wdl1AgAAVEq19tyUlZXJ399fkhQcHKy9e/dK+nPS8ebNm51XHQAAQBVVa89N27ZttXbtWjVr1kzx8fGaOnWqPD09NXfuXF1wwQXOrhEAAKDSqhVuHn74YRUWFkqSJk2apP/85z/q1q2bGjdurEWLFjm1QAAAgKqoVrhJTEy0/9yiRQtt2rRJBw8eVFBQkP2MKQAAAFc4o+vcSNLu3bslSREREWdcDAAAwJmq1oTi48eP65FHHlFgYKCioqIUFRWlwMBAPfzwwyotLXV2jQAAAJVWrT03Y8aM0VtvvaWpU6eqS5cukv48PXzChAn67bffNGfOHKcWCQAAUFnVCjcvv/yyXn31VfXp08fe1r59e0VERGjo0KGEGwAA4DLVOizl5eWlqKiocu3NmjWTp6fnmdYEAABQbdUKNykpKXr00UdVXFxsbysuLtbkyZOVkpLitOIAAACqqtKHpQYOHOjw/LPPPtN5552nmJgYSdLatWtVUlKiyy67zLkVAgAAVEGlw01gYKDD82uuucbhOaeCAwCAuqDS4Wb+/Pk1WQcAAIBTnNFF/PLz8+03ymzVqpVCQkKcUhQAAEB1VWtCcWFhoW6++Wb961//Uvfu3dW9e3c1bdpUt9xyi4qKipxdIwAAQKVVK9ykpqbqiy++0Pvvv6/Dhw/r8OHDevfdd/XFF1/onnvucXaNAAAAlVatw1Jvvvmm3njjDfXs2dPeduWVV8rHx0eDBw/mIn4AAMBlqrXnpqioSKGhoeXamzRpwmEpAADgUtUKN126dFF6erqOHTtmb/vjjz80ceJE+72mAAAAXKFah6UyMzPVu3fvchfx8/b21scff+zUAgEAAKqiWuGmXbt22rp1qxYuXKhNmzZJkoYOHaobbrhBPj4+Ti0QAACgKqocbkpLS9W6dWt98MEHGjFiRE3UBAAAUG1VnnNTv359h7k2AAAAdUm1JhTfcccdeuKJJ3T8+HFn1wMAAHBGqjXnZuXKlcrOztYnn3yidu3aqUGDBg6vv/XWW04pDgAAoKqqFW4aNmxY7q7gAAAAdUGVwo3NZtO0adO0ZcsWlZSU6NJLL9WECRM4QwoAANQZVZpzM3nyZD344IPy8/NTeHi4Zs6cqTvuuKOmagMAAKiyKoWbBQsW6P/+7//08ccf65133tH777+vhQsXymaz1VR9AAAAVVKlcLNr1y5deeWV9ucJCQlyc3PT3r17nV4YAABAdVQp3Bw/flze3t4ObfXr11dpaalTiwIAAKiuKk0otixLw4cPl5eXl73t2LFjGjVqlMPp4JwKDgAAXKVK4SYpKalc24033ui0YgAAAM5UlcLN/Pnza6oOAAAAp6jW7RcAAADqKsINAAAwSp0IN7Nnz1ZUVJS8vb0VHx+vFStWnLTvs88+q27duikoKEhBQUFKSEg4ZX8AAHBucXm4WbRokVJTU5Wenq7Vq1crJiZGiYmJ2r9/f4X9ly1bpqFDh2rp0qXKyclRRESErrjiCu3Zs6eWKwcAAHWRy8PNjBkzNGLECCUnJys6OlpZWVny9fXVvHnzKuy/cOFCjR49WrGxsWrdurX+97//yWazKTs7u5YrBwAAdZFLw01JSYlWrVqlhIQEe5u7u7sSEhKUk5NTqTGKiopUWlqqRo0aVfh6cXGxjhw54vAAAADmcmm4OXDggMrKyhQaGurQHhoaqtzc3EqNcf/996tp06YOAenvMjIyFBgYaH9ERESccd0AAKDucvlhqTMxZcoUvfrqq3r77bfL3RbihLS0NBUUFNgfu3fvruUqAQBAbarSRfycLTg4WB4eHsrLy3Noz8vLU1hY2CmXnT59uqZMmaLPPvtM7du3P2k/Ly8vh9tFAAAAs7l0z42np6c6duzoMBn4xOTgLl26nHS5qVOn6tFHH9WSJUsUFxdXG6UCAICzhEv33EhSamqqkpKSFBcXp86dOyszM1OFhYVKTk6WJA0bNkzh4eHKyMiQJD3xxBMaP368Xn75ZUVFRdnn5vj5+cnPz89l7wMAANQNLg83Q4YMUX5+vsaPH6/c3FzFxsZqyZIl9knGu3btkrv7XzuY5syZo5KSEl177bUO46Snp2vChAm1WToAAKiDXB5uJCklJUUpKSkVvrZs2TKH5zt27Kj5ggAAwFnrrD5bCgAA4J8INwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFJeHm9mzZysqKkre3t6Kj4/XihUrTtp3w4YNuuaaaxQVFSU3NzdlZmbWXqEAAOCs4NJws2jRIqWmpio9PV2rV69WTEyMEhMTtX///gr7FxUV6YILLtCUKVMUFhZWy9UCAICzgUvDzYwZMzRixAglJycrOjpaWVlZ8vX11bx58yrs36lTJ02bNk3XXXedvLy8arlaAABwNnBZuCkpKdGqVauUkJDwVzHu7kpISFBOTo7T1lNcXKwjR444PAAAgLlcFm4OHDigsrIyhYaGOrSHhoYqNzfXaevJyMhQYGCg/REREeG0sQEAQN3j8gnFNS0tLU0FBQX2x+7du11dEgAAqEH1XLXi4OBgeXh4KC8vz6E9Ly/PqZOFvby8mJ8DAMA5xGV7bjw9PdWxY0dlZ2fb22w2m7Kzs9WlSxdXlQUAAM5yLttzI0mpqalKSkpSXFycOnfurMzMTBUWFio5OVmSNGzYMIWHhysjI0PSn5OQN27caP95z549WrNmjfz8/NSiRQuXvQ8AAFB3uDTcDBkyRPn5+Ro/frxyc3MVGxurJUuW2CcZ79q1S+7uf+1c2rt3ry666CL78+nTp2v69Onq0aOHli1bVtvlAwCAOsil4UaSUlJSlJKSUuFr/wwsUVFRsiyrFqoCAABnK+PPlgIAAOcWwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMEqdCDezZ89WVFSUvL29FR8frxUrVpyy/+uvv67WrVvL29tb7dq104cfflhLlQIAgLrO5eFm0aJFSk1NVXp6ulavXq2YmBglJiZq//79FfZfvny5hg4dqltuuUU//PCD+vfvr/79++vHH3+s5coBAEBd5PJwM2PGDI0YMULJycmKjo5WVlaWfH19NW/evAr7P/300+rdu7fuvfdetWnTRo8++qg6dOigZ555ppYrBwAAdVE9V668pKREq1atUlpamr3N3d1dCQkJysnJqXCZnJwcpaamOrQlJibqnXfeqbB/cXGxiouL7c8LCgokSUeOHDnD6itmKy6qkXEBE9TUdlfb2M6BU6uJbf3EmJZlnbavS8PNgQMHVFZWptDQUIf20NBQbdq0qcJlcnNzK+yfm5tbYf+MjAxNnDixXHtEREQ1qwZQXYGZrq4AQG2oyW396NGjCgwMPGUfl4ab2pCWluawp8dms+ngwYNq3Lix3NzcXFgZatqRI0cUERGh3bt3KyAgwNXlAKghbOvnBsuydPToUTVt2vS0fV0aboKDg+Xh4aG8vDyH9ry8PIWFhVW4TFhYWJX6e3l5ycvLy6GtYcOG1S8aZ52AgAD+wwPOAWzr5jvdHpsTXDqh2NPTUx07dlR2dra9zWazKTs7W126dKlwmS5dujj0l6RPP/30pP0BAMC5xeWHpVJTU5WUlKS4uDh17txZmZmZKiwsVHJysiRp2LBhCg8PV0ZGhiTpzjvvVI8ePfTkk0+qb9++evXVV/X9999r7ty5rnwbAACgjnB5uBkyZIjy8/M1fvx45ebmKjY2VkuWLLFPGt61a5fc3f/awdS1a1e9/PLLevjhh/Xggw/qwgsv1DvvvKO2bdu66i2gjvLy8lJ6enq5w5IAzMK2jn9ysypzThUAAMBZwuUX8QMAAHAmwg0AADAK4QYAABiFcAMAAIxCuAH+f1FRUcrMzHR1GQAqYdmyZXJzc9Phw4dP2Y/t+txEuEGtGD58uNzc3DRlyhSH9nfeeafWb4Px/PPPV3iV6pUrV2rkyJG1WgtguhPbvpubmzw9PdWiRQtNmjRJx48fP6Nxu3btqn379tmvWMt2jb8j3KDWeHt764knntChQ4dcXUqFQkJC5Ovr6+oyAOP07t1b+/bt09atW3XPPfdowoQJmjZt2hmN6enpqbCwsNP+ccR2fW4i3KDWJCQkKCwszH616Yp8/fXX6tatm3x8fBQREaGxY8eqsLDQ/vq+ffvUt29f+fj4qFmzZnr55ZfL7XaeMWOG2rVrpwYNGigiIkKjR4/W77//LunPXdnJyckqKCiw/zU5YcIESY67r6+//noNGTLEobbS0lIFBwdrwYIFkv68VUhGRoaaNWsmHx8fxcTE6I033nDCJwWYxcvLS2FhYYqMjNTtt9+uhIQEvffeezp06JCGDRumoKAg+fr6qk+fPtq6dat9uZ07d+qqq65SUFCQGjRooH//+9/68MMPJTkelmK7xj8RblBrPDw89Pjjj2vWrFn69ddfy73+888/q3fv3rrmmmu0bt06LVq0SF9//bVSUlLsfYYNG6a9e/dq2bJlevPNNzV37lzt37/fYRx3d3fNnDlTGzZs0AsvvKDPP/9c9913n6Q/d2VnZmYqICBA+/bt0759+zRu3Lhytdxwww16//337aFIkj7++GMVFRVpwIABkqSMjAwtWLBAWVlZ2rBhg+6++27deOON+uKLL5zyeQGm8vHxUUlJiYYPH67vv/9e7733nnJycmRZlq688kqVlpZKku644w4VFxfryy+/1Pr16/XEE0/Iz8+v3Hhs1yjHAmpBUlKS1a9fP8uyLOviiy+2br75ZsuyLOvtt9+2TvwzvOWWW6yRI0c6LPfVV19Z7u7u1h9//GH99NNPliRr5cqV9te3bt1qSbKeeuqpk6779ddftxo3bmx/Pn/+fCswMLBcv8jISPs4paWlVnBwsLVgwQL760OHDrWGDBliWZZlHTt2zPL19bWWL1/uMMYtt9xiDR069NQfBnAO+fu2b7PZrE8//dTy8vKy+vfvb0myvvnmG3vfAwcOWD4+PtZrr71mWZZltWvXzpowYUKF4y5dutSSZB06dMiyLLZrOHL5vaVw7nniiSd06aWXlvvLau3atVq3bp0WLlxob7MsSzabTdu3b9eWLVtUr149dejQwf56ixYtFBQU5DDOZ599poyMDG3atElHjhzR8ePHdezYMRUVFVX62Hu9evU0ePBgLVy4UDfddJMKCwv17rvv6tVXX5Ukbdu2TUVFRbr88ssdlispKdFFF11Upc8DMN0HH3wgPz8/lZaWymaz6frrr9fAgQP1wQcfKD4+3t6vcePGatWqlX766SdJ0tixY3X77bfrk08+UUJCgq655hq1b9++2nWwXZ87CDeodd27d1diYqLS0tI0fPhwe/vvv/+u2267TWPHji23zPnnn68tW7acduwdO3boP//5j26//XZNnjxZjRo10tdff61bbrlFJSUlVZpYeMMNN6hHjx7av3+/Pv30U/n4+Kh37972WiVp8eLFCg8Pd1iOm/cBjnr16qU5c+bI09NTTZs2Vb169fTee++ddrlbb71ViYmJWrx4sT755BNlZGToySef1JgxY6pdC9v1uYFwA5eYMmWKYmNj1apVK3tbhw4dtHHjRrVo0aLCZVq1aqXjx4/rhx9+UMeOHSX9+ZfW38++WrVqlWw2m5588kn73eRfe+01h3E8PT1VVlZ22hq7du2qiIgILVq0SB999JEGDRqk+vXrS5Kio6Pl5eWlXbt2qUePHlV788A5pkGDBuW26zZt2uj48eP67rvv1LVrV0nSb7/9ps2bNys6OtreLyIiQqNGjdKoUaOUlpamZ599tsJww3aNvyPcwCXatWunG264QTNnzrS33X///br44ouVkpKiW2+9VQ0aNNDGjRv16aef6plnnlHr1q2VkJCgkSNHas6cOapfv77uuece+fj42E8HbdGihUpLSzVr1ixdddVV+uabb5SVleWw7qioKP3+++/Kzs5WTEyMfH19T7pH5/rrr1dWVpa2bNmipUuX2tv9/f01btw43X333bLZbLrkkktUUFCgb775RgEBAUpKSqqBTw0wx4UXXqh+/fppxIgR+u9//yt/f3898MADCg8PV79+/SRJd911l/r06aOWLVvq0KFDWrp0qdq0aVPheGzXcODqST84N/x9UuEJ27dvtzw9Pa2//zNcsWKFdfnll1t+fn5WgwYNrPbt21uTJ0+2v753716rT58+lpeXlxUZGWm9/PLLVpMmTaysrCx7nxkzZlj/+te/LB8fHysxMdFasGCBw8RDy7KsUaNGWY0bN7YkWenp6ZZlOU48PGHjxo2WJCsyMtKy2WwOr9lsNiszM9Nq1aqVVb9+fSskJMRKTEy0vvjiizP7sACDVLTtn3Dw4EHrpptusgIDA+3b65YtW+yvp6SkWM2bN7e8vLyskJAQ66abbrIOHDhgWVb5CcWWxXaNv7hZlmW5MFsBZ+TXX39VRESEPvvsM1122WWuLgcAUAcQbnBW+fzzz/X777+rXbt22rdvn+677z7t2bNHW7ZssR83BwCc25hzg7NKaWmpHnzwQf3yyy/y9/dX165dtXDhQoINAMCOPTcAAMAo3H4BAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADDK/wff8AAa/tgliwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ensuring Reproducibility\n",
        "Note the versions of the key packages used for others to reproduce your results."
      ],
      "metadata": {
        "id": "VUKXUWqKSRil"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "print(\"Torch Version:\", torch.__version__)\n",
        "print(\"Transformers Version:\", transformers.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fw8PQRVNSPQQ",
        "outputId": "28e961c2-9e34-4f29-89e3-d19a6c424995"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch Version: 2.1.0+cu121\n",
            "Transformers Version: 4.35.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "Fine-tuning pre-trained transformers allows you to leverage their power for your specific tasks and datasets. It's a potent way to achieve high performance with less data and computational resources. Stay updated with the latest models and practices, and always monitor your model's performance."
      ],
      "metadata": {
        "id": "Xam_aeDCSelk"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "xxn9xepGSV30"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}