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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: Fine_Tuning_SC_Method_2_Epoch_13B |
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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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# Fine_Tuning_SC_Method_2_Epoch_13B |
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This model is a fine-tuned version of [rafsankabir/Pretrained_E13B_Method2](https://huggingface.co/rafsankabir/Pretrained_E13B_Method2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4244 |
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- Accuracy: 0.6873 |
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- F1 Macro: 0.6544 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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_steps: 500 |
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- num_epochs: 40 |
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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 | F1 Macro | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| |
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| No log | 1.27 | 500 | 1.0673 | 0.3976 | 0.1896 | |
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| 1.0138 | 2.54 | 1000 | 0.8217 | 0.6331 | 0.5569 | |
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| 1.0138 | 3.82 | 1500 | 0.7889 | 0.6662 | 0.6049 | |
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| 0.7305 | 5.09 | 2000 | 0.7821 | 0.6765 | 0.6382 | |
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| 0.7305 | 6.36 | 2500 | 0.7867 | 0.6918 | 0.6457 | |
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| 0.5856 | 7.63 | 3000 | 0.8236 | 0.6892 | 0.6623 | |
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| 0.5856 | 8.91 | 3500 | 0.8490 | 0.6835 | 0.6551 | |
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| 0.4723 | 10.18 | 4000 | 0.9057 | 0.6854 | 0.6533 | |
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| 0.4723 | 11.45 | 4500 | 0.9237 | 0.6796 | 0.6455 | |
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| 0.3896 | 12.72 | 5000 | 0.9814 | 0.6879 | 0.6499 | |
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| 0.3896 | 13.99 | 5500 | 0.9984 | 0.6745 | 0.6487 | |
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| 0.3299 | 15.27 | 6000 | 1.0226 | 0.6822 | 0.6545 | |
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| 0.3299 | 16.54 | 6500 | 1.0579 | 0.6758 | 0.6485 | |
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| 0.2783 | 17.81 | 7000 | 1.0932 | 0.6796 | 0.6487 | |
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| 0.2783 | 19.08 | 7500 | 1.1047 | 0.6950 | 0.6609 | |
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| 0.2455 | 20.36 | 8000 | 1.1643 | 0.6860 | 0.6559 | |
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| 0.2455 | 21.63 | 8500 | 1.1953 | 0.6841 | 0.6548 | |
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| 0.2181 | 22.9 | 9000 | 1.2043 | 0.6835 | 0.6516 | |
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| 0.2181 | 24.17 | 9500 | 1.2603 | 0.6867 | 0.6502 | |
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| 0.1894 | 25.45 | 10000 | 1.2652 | 0.6860 | 0.6552 | |
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| 0.1894 | 26.72 | 10500 | 1.2860 | 0.6790 | 0.6474 | |
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| 0.1757 | 27.99 | 11000 | 1.2892 | 0.6854 | 0.6541 | |
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| 0.1757 | 29.26 | 11500 | 1.3400 | 0.6803 | 0.6496 | |
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| 0.1599 | 30.53 | 12000 | 1.3630 | 0.6828 | 0.6493 | |
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| 0.1599 | 31.81 | 12500 | 1.3688 | 0.6854 | 0.6538 | |
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| 0.1531 | 33.08 | 13000 | 1.3962 | 0.6854 | 0.6534 | |
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| 0.1531 | 34.35 | 13500 | 1.4021 | 0.6841 | 0.6523 | |
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| 0.1452 | 35.62 | 14000 | 1.4029 | 0.6847 | 0.6524 | |
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| 0.1452 | 36.9 | 14500 | 1.4130 | 0.6886 | 0.6562 | |
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| 0.1391 | 38.17 | 15000 | 1.4203 | 0.6879 | 0.6553 | |
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| 0.1391 | 39.44 | 15500 | 1.4244 | 0.6873 | 0.6544 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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