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"Collecting multiprocess<0.70.17 (from datasets)\n", " Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n", "Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.10.0)\n", "Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.12)\n", "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.13.1)\n", "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (1.4.2)\n", "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from scikit-learn) (3.5.0)\n", "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.6)\n", "Requirement already satisfied: aiosignal>=1.1.2 in 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collected packages: xxhash, dill, multiprocess, datasets\n", "Successfully installed datasets-3.3.0 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n" ] } ] }, { "cell_type": "code", "source": [ "import numpy as np\n", "import torch\n", "from datasets import load_dataset\n", "from transformers import (AutoTokenizer, AutoModelForSequenceClassification,\n", " Trainer, TrainingArguments, DataCollatorWithPadding)\n", "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n" ], "metadata": { "id": "sRZhv-kTzpLc" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "Load the IMDb Dataset\n", "Here we load the IMDb dataset (a binary sentiment dataset with labels 0 and 1)" ], "metadata": { "id": "leQayrmKzr2_" } }, { "cell_type": "code", "source": [ "# Load the dataset from Hugging Face\n", "dataset = load_dataset(\"imdb\")\n", "print(dataset)\n" ], "metadata": { "id": "QNVzl6B4zz3J", "colab": { "base_uri": "https://localhost:8080/", 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"stderr", "text": [ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [ "README.md: 0%| | 0.00/7.81k [00:00:18: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n", " trainer = Trainer(\n" ] } ] }, { "cell_type": "markdown", "source": [ "Fine-tune the Model\n", "\n", "Run the training loop" ], "metadata": { "id": "s3hFOd0c0psr" } }, { "cell_type": "code", "source": [ "trainer.train()\n" ], "metadata": { "id": "pMgQEp5c0ufs", "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "outputId": "22ce4413-02b9-43ba-d6d8-12979c3e7f95" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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EpochTraining LossValidation LossAccuracyF1PrecisionRecall
10.1707000.2132920.9196000.9196000.9196010.919600

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=1250, training_loss=0.22624320755004884, metrics={'train_runtime': 999.8344, 'train_samples_per_second': 20.003, 'train_steps_per_second': 1.25, 'total_flos': 2627312849274816.0, 'train_loss': 0.22624320755004884, 'epoch': 1.0})" ] }, "metadata": {}, "execution_count": 15 } ] }, { "cell_type": "markdown", "source": [ "Evaluate the Model on the Test Set\n", "\n", "After training, evaluate the performance on the test set" ], "metadata": { "id": "pxVlnwdk0wZY" } }, { "cell_type": "code", "source": [ "test_results = trainer.evaluate(test_dataset)\n", "print(\"Test set results:\", test_results)\n" ], "metadata": { "id": "RmR8OBI503Mb", "colab": { "base_uri": "https://localhost:8080/", "height": 74 }, "outputId": "bb00d8af-fe3d-4ed7-e6df-c471b8a21133" }, "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Test set results: {'eval_loss': 0.19689615070819855, 'eval_accuracy': 0.92612, 'eval_f1': 0.9261199970448, 'eval_precision': 0.9261200681792109, 'eval_recall': 0.92612, 'eval_runtime': 355.2586, 'eval_samples_per_second': 70.371, 'eval_steps_per_second': 4.4, 'epoch': 1.0}\n" ] } ] }, { "cell_type": "markdown", "source": [ "Save the Fine-tuned Model" ], "metadata": { "id": "nOsQlLGf040T" } }, { "cell_type": "code", "source": [ "trainer.save_model(\"./sentiment_model\")\n" ], "metadata": { "id": "ECIAw7ON07bs" }, "execution_count": 17, "outputs": [] } ] }