| --- |
| library_name: transformers |
| base_model: UBC-NLP/MARBERTv2 |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: MASRIBERTV4 |
| 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. --> |
|
|
| # MASRIBERTV4 |
|
|
| This model is a fine-tuned version of [UBC-NLP/MARBERTv2](https://huggingface.co/UBC-NLP/MARBERTv2) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.4905 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 64 |
| - 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: cosine |
| - lr_scheduler_warmup_steps: 10000 |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 49.3326 | 0.0427 | 500 | 5.4990 | |
| | 42.0818 | 0.0854 | 1000 | 4.8453 | |
| | 38.9605 | 0.1281 | 1500 | 4.5192 | |
| | 36.9452 | 0.1709 | 2000 | 4.3034 | |
| | 35.4721 | 0.2136 | 2500 | 4.1469 | |
| | 17.1800 | 0.2563 | 3000 | 2.0153 | |
| | 16.6866 | 0.2990 | 3500 | 1.9642 | |
| | 16.3991 | 0.3417 | 4000 | 1.9193 | |
| | 16.0343 | 0.3844 | 4500 | 1.8890 | |
| | 15.7752 | 0.4271 | 5000 | 1.8541 | |
| | 15.5412 | 0.4699 | 5500 | 1.8296 | |
| | 15.3676 | 0.5126 | 6000 | 1.8037 | |
| | 15.1004 | 0.5553 | 6500 | 1.7819 | |
| | 14.9579 | 0.5980 | 7000 | 1.7591 | |
| | 14.8206 | 0.6407 | 7500 | 1.7475 | |
| | 14.5731 | 0.6834 | 8000 | 1.7312 | |
| | 14.4853 | 0.7262 | 8500 | 1.7171 | |
| | 14.4356 | 0.7689 | 9000 | 1.7090 | |
| | 14.2340 | 0.8116 | 9500 | 1.6929 | |
| | 14.2497 | 0.8543 | 10000 | 1.6809 | |
| | 14.1660 | 0.8970 | 10500 | 1.6745 | |
| | 14.0404 | 0.9397 | 11000 | 1.6584 | |
| | 13.8540 | 0.9824 | 11500 | 1.6474 | |
| | 13.7538 | 1.0251 | 12000 | 1.6362 | |
| | 13.6777 | 1.0678 | 12500 | 1.6223 | |
| | 13.5928 | 1.1105 | 13000 | 1.6111 | |
| | 13.4528 | 1.1533 | 13500 | 1.6016 | |
| | 13.3583 | 1.1960 | 14000 | 1.5926 | |
| | 13.3129 | 1.2387 | 14500 | 1.5797 | |
| | 13.2261 | 1.2814 | 15000 | 1.5714 | |
| | 13.2270 | 1.3241 | 15500 | 1.5613 | |
| | 13.0836 | 1.3668 | 16000 | 1.5562 | |
| | 13.0298 | 1.4096 | 16500 | 1.5463 | |
| | 12.9826 | 1.4523 | 17000 | 1.5360 | |
| | 12.9178 | 1.4950 | 17500 | 1.5267 | |
| | 12.8210 | 1.5377 | 18000 | 1.5218 | |
| | 12.7591 | 1.5804 | 18500 | 1.5172 | |
| | 12.7104 | 1.6231 | 19000 | 1.5090 | |
| | 12.6183 | 1.6658 | 19500 | 1.5056 | |
| | 12.5794 | 1.7086 | 20000 | 1.5013 | |
| | 12.5963 | 1.7513 | 20500 | 1.4976 | |
| | 12.5140 | 1.7940 | 21000 | 1.4922 | |
| | 12.4895 | 1.8367 | 21500 | 1.4932 | |
| | 12.4931 | 1.8794 | 22000 | 1.4914 | |
| | 12.5685 | 1.9221 | 22500 | 1.4908 | |
| | 12.4823 | 1.9648 | 23000 | 1.4929 | |
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|
| ### Framework versions |
|
|
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
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|
