| --- |
| library_name: transformers |
| license: agpl-3.0 |
| base_model: ttqdunggg/finetune_cls_vs_content_ronbackbone_100k |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: 2_task_v2_new2labels |
| 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. --> |
|
|
| # 2_task_v2_new2labels |
| |
| This model is a fine-tuned version of [ttqdunggg/finetune_cls_vs_content_ronbackbone_100k](https://huggingface.co/ttqdunggg/finetune_cls_vs_content_ronbackbone_100k) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: nan |
| - F1 New: 0.7766 |
| - Acc New: 0.7868 |
| |
| ## 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: 0.001 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 80 |
| - num_epochs: 20 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 New | Acc New | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:| |
| | 0.6547 | 1.0 | 40 | nan | 0.6830 | 0.7426 | |
| | 0.5695 | 2.0 | 80 | nan | 0.7176 | 0.7574 | |
| | 0.5323 | 3.0 | 120 | nan | 0.7236 | 0.75 | |
| | 0.5104 | 4.0 | 160 | nan | 0.7527 | 0.7647 | |
| | 0.4958 | 5.0 | 200 | nan | 0.7654 | 0.7868 | |
| | 0.4795 | 6.0 | 240 | nan | 0.7405 | 0.75 | |
| | 0.4719 | 7.0 | 280 | nan | 0.7874 | 0.8015 | |
| | 0.4584 | 8.0 | 320 | nan | 0.7874 | 0.8015 | |
| | 0.4528 | 9.0 | 360 | nan | 0.7612 | 0.7721 | |
| | 0.4434 | 10.0 | 400 | nan | 0.7766 | 0.7868 | |
| | 0.4443 | 11.0 | 440 | nan | 0.7961 | 0.8088 | |
| | 0.4358 | 12.0 | 480 | nan | 0.7836 | 0.7941 | |
| | 0.4393 | 13.0 | 520 | nan | 0.7696 | 0.7794 | |
| | 0.4391 | 14.0 | 560 | nan | 0.7766 | 0.7868 | |
| | 0.4265 | 15.0 | 600 | nan | 0.7836 | 0.7941 | |
| | 0.4270 | 16.0 | 640 | nan | 0.7766 | 0.7868 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.13.1 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
| |
