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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: DNADebertaSentencepiece10k_continuation_continuation |
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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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# DNADebertaSentencepiece10k_continuation_continuation |
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This model is a fine-tuned version of [Vlasta/DNADebertaSentencepiece10k_continuation](https://huggingface.co/Vlasta/DNADebertaSentencepiece10k_continuation) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.3056 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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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- num_epochs: 15 |
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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 | |
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|:-------------:|:-----:|:------:|:---------------:| |
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| 5.4806 | 0.36 | 5000 | 5.4385 | |
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| 5.4848 | 0.72 | 10000 | 5.4333 | |
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| 5.4803 | 1.08 | 15000 | 5.4312 | |
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| 5.4759 | 1.45 | 20000 | 5.4223 | |
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| 5.4703 | 1.81 | 25000 | 5.4199 | |
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| 5.4626 | 2.17 | 30000 | 5.4147 | |
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| 5.4596 | 2.53 | 35000 | 5.4094 | |
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| 5.4534 | 2.89 | 40000 | 5.4014 | |
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| 5.4466 | 3.25 | 45000 | 5.4017 | |
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| 5.445 | 3.61 | 50000 | 5.3954 | |
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| 5.4446 | 3.97 | 55000 | 5.3916 | |
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| 5.4359 | 4.34 | 60000 | 5.3809 | |
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| 5.4327 | 4.7 | 65000 | 5.3846 | |
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| 5.4281 | 5.06 | 70000 | 5.3765 | |
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| 5.4207 | 5.42 | 75000 | 5.3744 | |
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| 5.4207 | 5.78 | 80000 | 5.3704 | |
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| 5.4167 | 6.14 | 85000 | 5.3685 | |
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| 5.41 | 6.5 | 90000 | 5.3641 | |
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| 5.4117 | 6.86 | 95000 | 5.3582 | |
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| 5.4075 | 7.23 | 100000 | 5.3568 | |
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| 5.4017 | 7.59 | 105000 | 5.3547 | |
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| 5.4006 | 7.95 | 110000 | 5.3494 | |
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| 5.3969 | 8.31 | 115000 | 5.3475 | |
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| 5.3935 | 8.67 | 120000 | 5.3453 | |
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| 5.3926 | 9.03 | 125000 | 5.3422 | |
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| 5.3895 | 9.39 | 130000 | 5.3351 | |
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| 5.3813 | 9.75 | 135000 | 5.3326 | |
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| 5.3841 | 10.12 | 140000 | 5.3340 | |
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| 5.3787 | 10.48 | 145000 | 5.3301 | |
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| 5.3781 | 10.84 | 150000 | 5.3280 | |
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| 5.3769 | 11.2 | 155000 | 5.3258 | |
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| 5.3733 | 11.56 | 160000 | 5.3198 | |
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| 5.3683 | 11.92 | 165000 | 5.3180 | |
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| 5.3682 | 12.28 | 170000 | 5.3181 | |
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| 5.3673 | 12.64 | 175000 | 5.3167 | |
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| 5.3623 | 13.01 | 180000 | 5.3116 | |
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| 5.3602 | 13.37 | 185000 | 5.3109 | |
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| 5.361 | 13.73 | 190000 | 5.3071 | |
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| 5.3573 | 14.09 | 195000 | 5.3078 | |
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| 5.3575 | 14.45 | 200000 | 5.3051 | |
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| 5.3544 | 14.81 | 205000 | 5.3038 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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