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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: vietcuna-3b_2048 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vietcuna-3b_2048 |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5250 |
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- Accuracy: 0.7375 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.18 |
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- training_steps: 1000 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.5694 | 1.05 | 50 | 0.5834 | 0.7087 | |
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| 0.5614 | 2.1 | 100 | 0.5772 | 0.7165 | |
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| 0.5475 | 3.15 | 150 | 0.5684 | 0.7165 | |
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| 0.5503 | 4.2 | 200 | 0.5605 | 0.7087 | |
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| 0.5305 | 5.25 | 250 | 0.5784 | 0.7192 | |
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| 0.5353 | 6.3 | 300 | 0.5451 | 0.7323 | |
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| 0.5063 | 7.35 | 350 | 0.5441 | 0.7270 | |
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| 0.5141 | 8.4 | 400 | 0.5365 | 0.7244 | |
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| 0.5035 | 9.45 | 450 | 0.5354 | 0.7297 | |
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| 0.493 | 10.5 | 500 | 0.5322 | 0.7297 | |
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| 0.4763 | 11.55 | 550 | 0.5299 | 0.7375 | |
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| 0.5063 | 12.6 | 600 | 0.5295 | 0.7375 | |
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| 0.4787 | 13.65 | 650 | 0.5280 | 0.7297 | |
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| 0.4841 | 14.7 | 700 | 0.5266 | 0.7375 | |
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| 0.4732 | 15.75 | 750 | 0.5283 | 0.7297 | |
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| 0.4801 | 16.8 | 800 | 0.5259 | 0.7375 | |
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| 0.4651 | 17.85 | 850 | 0.5256 | 0.7375 | |
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| 0.4726 | 18.9 | 900 | 0.5260 | 0.7323 | |
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| 0.4758 | 19.95 | 950 | 0.5248 | 0.7375 | |
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| 0.4701 | 21.0 | 1000 | 0.5250 | 0.7375 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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