| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen2-1.5B |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: fine_tuned_eli5_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_eli5_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: 0.1230 |
| - Accuracy: 0.9747 |
| |
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.7967 | 0.0210 | 100 | 0.3296 | 0.8710 | |
| | 0.5957 | 0.0421 | 200 | 0.5430 | 0.8329 | |
| | 0.386 | 0.0631 | 300 | 0.6117 | 0.8694 | |
| | 0.3981 | 0.0841 | 400 | 0.3349 | 0.9129 | |
| | 0.3357 | 0.1052 | 500 | 0.2388 | 0.9117 | |
| | 0.2831 | 0.1262 | 600 | 0.4056 | 0.9063 | |
| | 0.4625 | 0.1472 | 700 | 0.2346 | 0.9058 | |
| | 0.3478 | 0.1683 | 800 | 0.1944 | 0.9259 | |
| | 0.2524 | 0.1893 | 900 | 0.3200 | 0.9203 | |
| | 0.3523 | 0.2103 | 1000 | 0.3342 | 0.9113 | |
| | 0.2756 | 0.2314 | 1100 | 0.2443 | 0.9423 | |
| | 0.2814 | 0.2524 | 1200 | 0.2346 | 0.9349 | |
| | 0.2636 | 0.2735 | 1300 | 0.5285 | 0.9018 | |
| | 0.2491 | 0.2945 | 1400 | 0.1802 | 0.9472 | |
| | 0.2328 | 0.3155 | 1500 | 0.2347 | 0.9468 | |
| | 0.2113 | 0.3366 | 1600 | 0.2146 | 0.9453 | |
| | 0.2342 | 0.3576 | 1700 | 0.2253 | 0.9406 | |
| | 0.2102 | 0.3786 | 1800 | 0.1987 | 0.9515 | |
| | 0.1518 | 0.3997 | 1900 | 0.2878 | 0.9373 | |
| | 0.2326 | 0.4207 | 2000 | 0.2071 | 0.9489 | |
| | 0.2018 | 0.4417 | 2100 | 0.1554 | 0.9498 | |
| | 0.1924 | 0.4628 | 2200 | 0.1812 | 0.9515 | |
| | 0.2139 | 0.4838 | 2300 | 0.3613 | 0.9302 | |
| | 0.2801 | 0.5048 | 2400 | 0.1490 | 0.9527 | |
| | 0.1979 | 0.5259 | 2500 | 0.1786 | 0.9546 | |
| | 0.1695 | 0.5469 | 2600 | 0.1765 | 0.9536 | |
| | 0.1541 | 0.5679 | 2700 | 0.1390 | 0.9631 | |
| | 0.1527 | 0.5890 | 2800 | 0.1198 | 0.9598 | |
| | 0.1711 | 0.6100 | 2900 | 0.1841 | 0.9593 | |
| | 0.2014 | 0.6310 | 3000 | 0.1497 | 0.9621 | |
| | 0.1174 | 0.6521 | 3100 | 0.1464 | 0.9671 | |
| | 0.1452 | 0.6731 | 3200 | 0.1323 | 0.9652 | |
| | 0.1367 | 0.6942 | 3300 | 0.1316 | 0.9659 | |
| | 0.1798 | 0.7152 | 3400 | 0.2200 | 0.9553 | |
| | 0.1683 | 0.7362 | 3500 | 0.1399 | 0.9655 | |
| | 0.1426 | 0.7573 | 3600 | 0.1146 | 0.9726 | |
| | 0.203 | 0.7783 | 3700 | 0.1601 | 0.9666 | |
| | 0.1452 | 0.7993 | 3800 | 0.1491 | 0.9692 | |
| | 0.1602 | 0.8204 | 3900 | 0.1251 | 0.9740 | |
| | 0.1451 | 0.8414 | 4000 | 0.1192 | 0.9747 | |
| | 0.14 | 0.8624 | 4100 | 0.1441 | 0.9695 | |
| | 0.158 | 0.8835 | 4200 | 0.1428 | 0.9692 | |
| | 0.1211 | 0.9045 | 4300 | 0.1841 | 0.9619 | |
| | 0.1324 | 0.9255 | 4400 | 0.1587 | 0.9657 | |
| | 0.1153 | 0.9466 | 4500 | 0.1411 | 0.9697 | |
| | 0.1321 | 0.9676 | 4600 | 0.1230 | 0.9747 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu126 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
