Example_Fine_Tuning_a_Transformer_Model_with_Hugging_Face.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/OneFineStarstuff/OneFineStardust/blob/main/Example_Fine_Tuning_a_Transformer_Model_with_Hugging_Face.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "pip install datasets"
      ],
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      "source": [
        "from transformers import Trainer, TrainingArguments, BertForSequenceClassification, BertTokenizer\n",
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset\n",
        "dataset = load_dataset('glue', 'mrpc')\n",
        "\n",
        "# Load pre-trained model and tokenizer\n",
        "model = BertForSequenceClassification.from_pretrained('bert-base-uncased')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "\n",
        "# Tokenize the dataset\n",
        "def tokenize_function(examples):\n",
        "    return tokenizer(examples['sentence1'], examples['sentence2'], truncation=True, padding='max_length', max_length=128, clean_up_tokenization_spaces=True)\n",
        "\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n",
        "\n",
        "# Set training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    eval_strategy='epoch',\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    num_train_epochs=3,\n",
        ")\n",
        "\n",
        "# Create Trainer instance\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['validation'],\n",
        ")\n",
        "\n",
        "# Train the model\n",
        "trainer.train()"
      ]
    }
  ]
}