| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: Qwen/Qwen2-1.5B |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: fine_tuned_cmv_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_cmv_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: 0.0523 |
| - Accuracy: 0.9931 |
| |
| ## 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: 24 |
| - eval_batch_size: 24 |
| - 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:| |
| | 0.3459 | 0.1083 | 100 | 0.1297 | 0.9602 | |
| | 0.1385 | 0.2167 | 200 | 0.0771 | 0.9805 | |
| | 0.0918 | 0.3250 | 300 | 0.0951 | 0.9825 | |
| | 0.1103 | 0.4334 | 400 | 0.0834 | 0.9813 | |
| | 0.0943 | 0.5417 | 500 | 0.0607 | 0.9821 | |
| | 0.0692 | 0.6501 | 600 | 0.0714 | 0.9866 | |
| | 0.0584 | 0.7584 | 700 | 0.0607 | 0.9858 | |
| | 0.0599 | 0.8667 | 800 | 0.0531 | 0.9874 | |
| | 0.0672 | 0.9751 | 900 | 0.0312 | 0.9915 | |
| | 0.0086 | 1.0834 | 1000 | 0.0494 | 0.9919 | |
| | 0.0084 | 1.1918 | 1100 | 0.0621 | 0.9890 | |
| | 0.0225 | 1.3001 | 1200 | 0.0433 | 0.9927 | |
| | 0.0146 | 1.4085 | 1300 | 0.0684 | 0.9870 | |
| | 0.0126 | 1.5168 | 1400 | 0.0960 | 0.9878 | |
| | 0.0143 | 1.6251 | 1500 | 0.0454 | 0.9927 | |
| | 0.0081 | 1.7335 | 1600 | 0.0671 | 0.9907 | |
| | 0.0064 | 1.8418 | 1700 | 0.0526 | 0.9919 | |
| | 0.0007 | 1.9502 | 1800 | 0.0458 | 0.9931 | |
| | 0.003 | 2.0585 | 1900 | 0.0523 | 0.9931 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu126 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
