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--- |
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license: mit |
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base_model: gogamza/kobart-base-v2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: qa_kor_math |
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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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# qa_kor_math |
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This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co./gogamza/kobart-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3294 |
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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: 1e-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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- 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: 400 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 0.56 | 100 | 3.5725 | |
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| No log | 1.13 | 200 | 1.2367 | |
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| No log | 1.69 | 300 | 0.7100 | |
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| No log | 2.26 | 400 | 0.5420 | |
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| 2.4974 | 2.82 | 500 | 0.5891 | |
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| 2.4974 | 3.39 | 600 | 0.5370 | |
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| 2.4974 | 3.95 | 700 | 0.4738 | |
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| 2.4974 | 4.52 | 800 | 0.4985 | |
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| 2.4974 | 5.08 | 900 | 0.4540 | |
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| 0.3445 | 5.65 | 1000 | 0.4439 | |
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| 0.3445 | 6.21 | 1100 | 0.4261 | |
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| 0.3445 | 6.78 | 1200 | 0.4007 | |
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| 0.3445 | 7.34 | 1300 | 0.3739 | |
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| 0.3445 | 7.91 | 1400 | 0.3937 | |
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| 0.26 | 8.47 | 1500 | 0.3550 | |
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| 0.26 | 9.04 | 1600 | 0.3623 | |
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| 0.26 | 9.6 | 1700 | 0.3944 | |
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| 0.26 | 10.17 | 1800 | 0.3669 | |
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| 0.26 | 10.73 | 1900 | 0.3628 | |
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| 0.217 | 11.3 | 2000 | 0.3703 | |
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| 0.217 | 11.86 | 2100 | 0.3580 | |
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| 0.217 | 12.43 | 2200 | 0.3318 | |
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| 0.217 | 12.99 | 2300 | 0.3199 | |
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| 0.217 | 13.56 | 2400 | 0.3537 | |
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| 0.1916 | 14.12 | 2500 | 0.3198 | |
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| 0.1916 | 14.69 | 2600 | 0.3317 | |
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| 0.1916 | 15.25 | 2700 | 0.3333 | |
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| 0.1916 | 15.82 | 2800 | 0.3280 | |
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| 0.1916 | 16.38 | 2900 | 0.3269 | |
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| 0.1737 | 16.95 | 3000 | 0.3315 | |
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| 0.1737 | 17.51 | 3100 | 0.3346 | |
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| 0.1737 | 18.08 | 3200 | 0.3290 | |
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| 0.1737 | 18.64 | 3300 | 0.3317 | |
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| 0.1737 | 19.21 | 3400 | 0.3282 | |
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| 0.1637 | 19.77 | 3500 | 0.3294 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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