| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen2-1.5B |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: fine_tuned_tldr_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_tldr_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: 0.1451 |
| - Accuracy: 0.9682 |
| |
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.8181 | 0.0393 | 100 | 0.2443 | 0.9050 | |
| | 0.4998 | 0.0785 | 200 | 0.2800 | 0.8754 | |
| | 0.4488 | 0.1178 | 300 | 0.5770 | 0.8710 | |
| | 0.3996 | 0.1570 | 400 | 0.1956 | 0.9139 | |
| | 0.298 | 0.1963 | 500 | 0.3754 | 0.9307 | |
| | 0.2918 | 0.2356 | 600 | 0.7744 | 0.8905 | |
| | 0.2906 | 0.2748 | 700 | 0.2349 | 0.9214 | |
| | 0.2113 | 0.3141 | 800 | 0.2182 | 0.9443 | |
| | 0.2552 | 0.3534 | 900 | 0.1959 | 0.9501 | |
| | 0.227 | 0.3926 | 1000 | 0.1768 | 0.9496 | |
| | 0.2203 | 0.4319 | 1100 | 0.1711 | 0.9439 | |
| | 0.2212 | 0.4711 | 1200 | 0.1652 | 0.9585 | |
| | 0.2153 | 0.5104 | 1300 | 0.1695 | 0.9567 | |
| | 0.1975 | 0.5497 | 1400 | 0.1776 | 0.9536 | |
| | 0.1866 | 0.5889 | 1500 | 0.1516 | 0.9602 | |
| | 0.2209 | 0.6282 | 1600 | 0.1139 | 0.9691 | |
| | 0.1788 | 0.6675 | 1700 | 0.1995 | 0.9563 | |
| | 0.1808 | 0.7067 | 1800 | 0.1857 | 0.9554 | |
| | 0.2401 | 0.7460 | 1900 | 0.1397 | 0.9686 | |
| | 0.1602 | 0.7852 | 2000 | 0.1974 | 0.9620 | |
| | 0.2206 | 0.8245 | 2100 | 0.1392 | 0.9633 | |
| | 0.1609 | 0.8638 | 2200 | 0.1904 | 0.9620 | |
| | 0.2108 | 0.9030 | 2300 | 0.1774 | 0.9611 | |
| | 0.1408 | 0.9423 | 2400 | 0.1598 | 0.9669 | |
| | 0.1696 | 0.9815 | 2500 | 0.1694 | 0.9660 | |
| | 0.1231 | 1.0208 | 2600 | 0.1451 | 0.9682 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu126 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
