| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen2-1.5B |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: fine_tuned_squad_callback10 |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # fine_tuned_squad_callback10 |
| |
| This model is a fine-tuned version of [Qwen/Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1183 |
| - Accuracy: 0.9656 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.8142 | 0.0249 | 100 | 0.3328 | 0.8705 | |
| | 0.4483 | 0.0497 | 200 | 0.2505 | 0.9276 | |
| | 0.3808 | 0.0746 | 300 | 0.2715 | 0.9267 | |
| | 0.2638 | 0.0994 | 400 | 0.3570 | 0.9116 | |
| | 0.3363 | 0.1243 | 500 | 0.3385 | 0.9284 | |
| | 0.2347 | 0.1491 | 600 | 0.3153 | 0.9273 | |
| | 0.2882 | 0.1740 | 700 | 0.1504 | 0.9516 | |
| | 0.1782 | 0.1989 | 800 | 0.1403 | 0.9611 | |
| | 0.2897 | 0.2237 | 900 | 0.3369 | 0.9424 | |
| | 0.276 | 0.2486 | 1000 | 0.1714 | 0.9595 | |
| | 0.1409 | 0.2734 | 1100 | 0.1756 | 0.9527 | |
| | 0.1726 | 0.2983 | 1200 | 0.1371 | 0.9664 | |
| | 0.2029 | 0.3231 | 1300 | 0.3187 | 0.9223 | |
| | 0.1869 | 0.3480 | 1400 | 0.1917 | 0.9561 | |
| | 0.2551 | 0.3729 | 1500 | 0.1410 | 0.9592 | |
| | 0.1249 | 0.3977 | 1600 | 0.2447 | 0.9547 | |
| | 0.1784 | 0.4226 | 1700 | 0.1548 | 0.9687 | |
| | 0.1567 | 0.4474 | 1800 | 0.2113 | 0.9625 | |
| | 0.1863 | 0.4723 | 1900 | 0.1238 | 0.9723 | |
| | 0.2032 | 0.4971 | 2000 | 0.2280 | 0.9516 | |
| | 0.161 | 0.5220 | 2100 | 0.1819 | 0.9536 | |
| | 0.1687 | 0.5469 | 2200 | 0.1034 | 0.9757 | |
| | 0.1196 | 0.5717 | 2300 | 0.0857 | 0.9807 | |
| | 0.1407 | 0.5966 | 2400 | 0.0824 | 0.9827 | |
| | 0.1028 | 0.6214 | 2500 | 0.1338 | 0.9757 | |
| | 0.1257 | 0.6463 | 2600 | 0.0872 | 0.9776 | |
| | 0.1226 | 0.6711 | 2700 | 0.1050 | 0.9799 | |
| | 0.1249 | 0.6960 | 2800 | 0.0902 | 0.9776 | |
| | 0.0763 | 0.7209 | 2900 | 0.1054 | 0.9787 | |
| | 0.125 | 0.7457 | 3000 | 0.1131 | 0.9765 | |
| | 0.1257 | 0.7706 | 3100 | 0.2562 | 0.9547 | |
| | 0.163 | 0.7954 | 3200 | 0.1519 | 0.9746 | |
| | 0.1246 | 0.8203 | 3300 | 0.1513 | 0.9729 | |
| | 0.1358 | 0.8451 | 3400 | 0.1183 | 0.9656 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu126 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
