| --- |
| library_name: transformers |
| base_model: OMRIDRORI/mbert-tibetan-continual-unicode-240k |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: tibetan_code_switching_model |
| 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. --> |
|
|
| # tibetan_code_switching_model |
| |
| This model is a fine-tuned version of [OMRIDRORI/mbert-tibetan-continual-unicode-240k](https://huggingface.co/OMRIDRORI/mbert-tibetan-continual-unicode-240k) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3714 |
| - Accuracy: 0.9445 |
| - Switch Precision: 0.9381 |
| - Switch Recall: 0.9725 |
| - Switch F1: 0.9550 |
| - True Switches: 109 |
| - Pred Switches: 113 |
| - Tp: 106 |
| - Fp: 7 |
| - Fn: 3 |
| |
| ## 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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Switch Precision | Switch Recall | Switch F1 | True Switches | Pred Switches | Tp | Fp | Fn | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:---:|:----:|:--:| |
| | 4.727 | 1.0 | 50 | 5.6020 | 0.2133 | 0.0749 | 0.9908 | 0.1393 | 109 | 1442 | 108 | 1334 | 1 | |
| | 1.7867 | 2.0 | 100 | 1.2875 | 0.5304 | 0.5365 | 0.9450 | 0.6844 | 109 | 192 | 103 | 89 | 6 | |
| | 1.1232 | 3.0 | 150 | 0.9034 | 0.6356 | 0.6835 | 0.9908 | 0.8090 | 109 | 158 | 108 | 50 | 1 | |
| | 0.7733 | 4.0 | 200 | 0.6946 | 0.7664 | 0.6855 | 1.0 | 0.8134 | 109 | 159 | 109 | 50 | 0 | |
| | 0.5582 | 5.0 | 250 | 0.4907 | 0.8514 | 0.7219 | 1.0 | 0.8385 | 109 | 151 | 109 | 42 | 0 | |
| | 0.4037 | 6.0 | 300 | 0.4428 | 0.8808 | 0.7219 | 1.0 | 0.8385 | 109 | 151 | 109 | 42 | 0 | |
| | 0.2593 | 7.0 | 350 | 0.3282 | 0.9213 | 0.8134 | 1.0 | 0.8971 | 109 | 134 | 109 | 25 | 0 | |
| | 0.1714 | 8.0 | 400 | 0.3918 | 0.9044 | 0.8244 | 0.9908 | 0.9000 | 109 | 131 | 108 | 23 | 1 | |
| | 0.1114 | 9.0 | 450 | 0.3473 | 0.9334 | 0.8710 | 0.9908 | 0.9270 | 109 | 124 | 108 | 16 | 1 | |
| | 0.0678 | 10.0 | 500 | 0.3275 | 0.9348 | 0.9145 | 0.9817 | 0.9469 | 109 | 117 | 107 | 10 | 2 | |
| | 0.075 | 11.0 | 550 | 0.3718 | 0.9392 | 0.8917 | 0.9817 | 0.9345 | 109 | 120 | 107 | 13 | 2 | |
| | 0.0276 | 12.0 | 600 | 0.3588 | 0.9435 | 0.9068 | 0.9817 | 0.9427 | 109 | 118 | 107 | 11 | 2 | |
| | 0.0251 | 13.0 | 650 | 0.3488 | 0.9411 | 0.9304 | 0.9817 | 0.9554 | 109 | 115 | 107 | 8 | 2 | |
| | 0.0154 | 14.0 | 700 | 0.3599 | 0.9455 | 0.9386 | 0.9817 | 0.9596 | 109 | 114 | 107 | 7 | 2 | |
| | 0.0165 | 15.0 | 750 | 0.3714 | 0.9445 | 0.9381 | 0.9725 | 0.9550 | 109 | 113 | 106 | 7 | 3 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.46.3 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.0.0 |
| - Tokenizers 0.20.3 |
|
|
