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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: roberta-mc-5 |
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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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# roberta-mc-5 |
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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: 0.4494 |
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- Accuracy: 0.89 |
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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: 0.001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 60 |
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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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| 1.5837 | 1.0 | 25 | 1.5556 | 0.53 | |
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| 1.5523 | 2.0 | 50 | 1.5001 | 0.51 | |
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| 1.555 | 3.0 | 75 | 1.4666 | 0.48 | |
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| 1.4895 | 4.0 | 100 | 1.4224 | 0.49 | |
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| 1.4951 | 5.0 | 125 | 1.3924 | 0.495 | |
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| 1.4549 | 6.0 | 150 | 1.3716 | 0.53 | |
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| 1.4462 | 7.0 | 175 | 1.3372 | 0.51 | |
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| 1.4262 | 8.0 | 200 | 1.3005 | 0.515 | |
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| 1.3729 | 9.0 | 225 | 1.2497 | 0.525 | |
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| 1.4031 | 10.0 | 250 | 1.2854 | 0.535 | |
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| 1.3962 | 11.0 | 275 | 1.2891 | 0.56 | |
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| 1.3519 | 12.0 | 300 | 1.2060 | 0.53 | |
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| 1.362 | 13.0 | 325 | 1.3458 | 0.555 | |
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| 1.3693 | 14.0 | 350 | 1.1796 | 0.56 | |
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| 1.346 | 15.0 | 375 | 1.1360 | 0.585 | |
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| 1.2285 | 16.0 | 400 | 1.0907 | 0.57 | |
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| 1.2481 | 17.0 | 425 | 1.1393 | 0.56 | |
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| 1.2568 | 18.0 | 450 | 1.0404 | 0.6 | |
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| 1.2249 | 19.0 | 475 | 1.0012 | 0.595 | |
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| 1.1611 | 20.0 | 500 | 1.0123 | 0.615 | |
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| 1.1416 | 21.0 | 525 | 0.9631 | 0.64 | |
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| 1.2197 | 22.0 | 550 | 1.0537 | 0.625 | |
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| 1.2029 | 23.0 | 575 | 0.9518 | 0.66 | |
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| 1.1971 | 24.0 | 600 | 0.9295 | 0.67 | |
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| 1.1513 | 25.0 | 625 | 0.9045 | 0.675 | |
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| 1.0185 | 26.0 | 650 | 0.8620 | 0.71 | |
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| 1.1352 | 27.0 | 675 | 1.0548 | 0.69 | |
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| 1.1593 | 28.0 | 700 | 1.0043 | 0.68 | |
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| 1.1418 | 29.0 | 725 | 0.8569 | 0.7 | |
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| 1.0534 | 30.0 | 750 | 0.8284 | 0.715 | |
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| 1.08 | 31.0 | 775 | 0.7953 | 0.73 | |
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| 1.0148 | 32.0 | 800 | 0.7775 | 0.74 | |
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| 1.0526 | 33.0 | 825 | 0.8120 | 0.755 | |
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| 1.03 | 34.0 | 850 | 0.7630 | 0.76 | |
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| 1.0287 | 35.0 | 875 | 0.7651 | 0.745 | |
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| 1.0287 | 36.0 | 900 | 0.7174 | 0.765 | |
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| 0.9901 | 37.0 | 925 | 0.7268 | 0.75 | |
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| 0.9257 | 38.0 | 950 | 0.7114 | 0.765 | |
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| 0.9372 | 39.0 | 975 | 0.6691 | 0.805 | |
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| 0.9582 | 40.0 | 1000 | 0.6650 | 0.795 | |
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| 0.8728 | 41.0 | 1025 | 0.6588 | 0.78 | |
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| 0.8925 | 42.0 | 1050 | 0.6426 | 0.81 | |
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| 0.9357 | 43.0 | 1075 | 0.6302 | 0.815 | |
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| 0.9257 | 44.0 | 1100 | 0.7645 | 0.795 | |
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| 0.8763 | 45.0 | 1125 | 0.6034 | 0.815 | |
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| 0.838 | 46.0 | 1150 | 0.5711 | 0.815 | |
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| 0.8652 | 47.0 | 1175 | 0.5583 | 0.83 | |
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| 0.8106 | 48.0 | 1200 | 0.5560 | 0.835 | |
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| 0.8567 | 49.0 | 1225 | 0.5361 | 0.825 | |
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| 0.8185 | 50.0 | 1250 | 0.5926 | 0.825 | |
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| 0.8327 | 51.0 | 1275 | 0.5550 | 0.85 | |
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| 0.7822 | 52.0 | 1300 | 0.5193 | 0.85 | |
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| 0.7971 | 53.0 | 1325 | 0.5213 | 0.85 | |
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| 0.8051 | 54.0 | 1350 | 0.5175 | 0.845 | |
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| 0.7815 | 55.0 | 1375 | 0.4801 | 0.885 | |
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| 0.7391 | 56.0 | 1400 | 0.5759 | 0.87 | |
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| 0.8168 | 57.0 | 1425 | 0.4646 | 0.88 | |
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| 0.6991 | 58.0 | 1450 | 0.4713 | 0.885 | |
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| 0.7545 | 59.0 | 1475 | 0.4882 | 0.885 | |
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| 0.7222 | 60.0 | 1500 | 0.4494 | 0.89 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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