{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "d1623101ddf840ba969182b8d6d05034": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_7f40427d4ce14afbadb15cbb5d702ffb", "IPY_MODEL_8f32ba771c494b6d9fc558d53f45743b", "IPY_MODEL_7417f72db66649d1b896ab9266e949ee", "IPY_MODEL_57058283c897450189115f82c156ad0b" ], "layout": "IPY_MODEL_e7c5270090f4419fa45c5516421d3439" } }, "de2603ddf194473e8ed7f309c5f40577": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9cfd8f90dcb34082963b3fc4946ba756", "placeholder": "", "style": "IPY_MODEL_c31aca8f8f0e4adbb9f1f245f0737c50", "value": "
Epoch | \n", "Training Loss | \n", "Validation Loss | \n", "
---|
"
],
"text/plain": [
" "
]
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=1620, training_loss=0.2767131463980969, metrics={'train_runtime': 690.7884, 'train_samples_per_second': 23.451, 'train_steps_per_second': 2.345, 'total_flos': 1.2554602436468736e+18, 'train_loss': 0.2767131463980969, 'epoch': 1.0})"
]
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"source": [
"# Assuming you have the trainer and test_ds already defined and trained\n",
"\n",
"# Step 1: Get Predictions from the Test Set\n",
"outputs = trainer.predict(test_ds)\n",
"predictions = outputs.predictions.argmax(-1)\n",
"labels = outputs.label_ids\n",
"\n",
"# Step 2: Compute Accuracy Manually\n",
"import numpy as np\n",
"\n",
"accuracy = np.mean(predictions == labels)\n",
"print(f\"Test accuracy: {accuracy:.2f}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "n_QpHpbUrESt",
"outputId": "fe75b02c-8502-4934-93f4-72da603d0635"
},
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\n",
" \n",
"
\n",
" \n",
" \n",
" \n",
" Epoch \n",
" Training Loss \n",
" Validation Loss \n",
" \n",
" \n",
" \n",
"1 \n",
" 0.156400 \n",
" 0.067153 \n",
"