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--- |
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license: apache-2.0 |
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base_model: vietgpt/bert-30M-uncased |
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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: bert-30M-uncased-classification-CMC-fqa-new-100e |
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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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# bert-30M-uncased-classification-CMC-fqa-new-100e |
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This model is a fine-tuned version of [vietgpt/bert-30M-uncased](https://huggingface.co/vietgpt/bert-30M-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0430 |
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- Accuracy: 1.0 |
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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: 2e-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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- num_epochs: 100 |
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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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| No log | 1.0 | 20 | 3.4282 | 0.0323 | |
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| No log | 2.0 | 40 | 3.4133 | 0.0323 | |
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| No log | 3.0 | 60 | 3.4014 | 0.0645 | |
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| No log | 4.0 | 80 | 3.3872 | 0.1613 | |
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| No log | 5.0 | 100 | 3.3703 | 0.2581 | |
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| No log | 6.0 | 120 | 3.3503 | 0.2903 | |
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| No log | 7.0 | 140 | 3.3193 | 0.5161 | |
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| No log | 8.0 | 160 | 3.2707 | 0.5484 | |
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| No log | 9.0 | 180 | 3.1889 | 0.5484 | |
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| No log | 10.0 | 200 | 3.0520 | 0.4839 | |
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| No log | 11.0 | 220 | 2.8649 | 0.3548 | |
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| No log | 12.0 | 240 | 2.6818 | 0.4839 | |
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| No log | 13.0 | 260 | 2.5264 | 0.5161 | |
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| No log | 14.0 | 280 | 2.3816 | 0.5806 | |
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| No log | 15.0 | 300 | 2.2296 | 0.7419 | |
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| No log | 16.0 | 320 | 2.0829 | 0.7097 | |
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| No log | 17.0 | 340 | 1.9155 | 0.8387 | |
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| No log | 18.0 | 360 | 1.7785 | 0.8065 | |
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| No log | 19.0 | 380 | 1.6237 | 0.8710 | |
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| No log | 20.0 | 400 | 1.4944 | 0.9355 | |
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| No log | 21.0 | 420 | 1.3666 | 0.9355 | |
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| No log | 22.0 | 440 | 1.2578 | 0.9355 | |
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| No log | 23.0 | 460 | 1.1628 | 0.9677 | |
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| No log | 24.0 | 480 | 1.0483 | 0.9355 | |
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| 2.5506 | 25.0 | 500 | 0.9724 | 0.9677 | |
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| 2.5506 | 26.0 | 520 | 0.9024 | 0.9355 | |
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| 2.5506 | 27.0 | 540 | 0.8159 | 0.9677 | |
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| 2.5506 | 28.0 | 560 | 0.7614 | 0.9355 | |
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| 2.5506 | 29.0 | 580 | 0.6883 | 0.9677 | |
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| 2.5506 | 30.0 | 600 | 0.6365 | 0.9677 | |
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| 2.5506 | 31.0 | 620 | 0.5864 | 0.9677 | |
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| 2.5506 | 32.0 | 640 | 0.5423 | 1.0 | |
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| 2.5506 | 33.0 | 660 | 0.4985 | 1.0 | |
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| 2.5506 | 34.0 | 680 | 0.4666 | 1.0 | |
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| 2.5506 | 35.0 | 700 | 0.4262 | 1.0 | |
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| 2.5506 | 36.0 | 720 | 0.3942 | 1.0 | |
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| 2.5506 | 37.0 | 740 | 0.3629 | 1.0 | |
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| 2.5506 | 38.0 | 760 | 0.3336 | 1.0 | |
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| 2.5506 | 39.0 | 780 | 0.3079 | 1.0 | |
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| 2.5506 | 40.0 | 800 | 0.2836 | 1.0 | |
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| 2.5506 | 41.0 | 820 | 0.2579 | 1.0 | |
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| 2.5506 | 42.0 | 840 | 0.2381 | 1.0 | |
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| 2.5506 | 43.0 | 860 | 0.2167 | 1.0 | |
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| 2.5506 | 44.0 | 880 | 0.2053 | 1.0 | |
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| 2.5506 | 45.0 | 900 | 0.1846 | 1.0 | |
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| 2.5506 | 46.0 | 920 | 0.1720 | 1.0 | |
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| 2.5506 | 47.0 | 940 | 0.1579 | 1.0 | |
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| 2.5506 | 48.0 | 960 | 0.1452 | 1.0 | |
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| 2.5506 | 49.0 | 980 | 0.1366 | 1.0 | |
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| 0.4668 | 50.0 | 1000 | 0.1262 | 1.0 | |
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| 0.4668 | 51.0 | 1020 | 0.1165 | 1.0 | |
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| 0.4668 | 52.0 | 1040 | 0.1098 | 1.0 | |
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| 0.4668 | 53.0 | 1060 | 0.1042 | 1.0 | |
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| 0.4668 | 54.0 | 1080 | 0.1022 | 1.0 | |
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| 0.4668 | 55.0 | 1100 | 0.0956 | 1.0 | |
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| 0.4668 | 56.0 | 1120 | 0.0916 | 1.0 | |
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| 0.4668 | 57.0 | 1140 | 0.0875 | 1.0 | |
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| 0.4668 | 58.0 | 1160 | 0.0819 | 1.0 | |
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| 0.4668 | 59.0 | 1180 | 0.0785 | 1.0 | |
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| 0.4668 | 60.0 | 1200 | 0.0761 | 1.0 | |
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| 0.4668 | 61.0 | 1220 | 0.0740 | 1.0 | |
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| 0.4668 | 62.0 | 1240 | 0.0709 | 1.0 | |
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| 0.4668 | 63.0 | 1260 | 0.0692 | 1.0 | |
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| 0.4668 | 64.0 | 1280 | 0.0672 | 1.0 | |
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| 0.4668 | 65.0 | 1300 | 0.0654 | 1.0 | |
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| 0.4668 | 66.0 | 1320 | 0.0653 | 1.0 | |
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| 0.4668 | 67.0 | 1340 | 0.0628 | 1.0 | |
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| 0.4668 | 68.0 | 1360 | 0.0618 | 1.0 | |
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| 0.4668 | 69.0 | 1380 | 0.0608 | 1.0 | |
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| 0.4668 | 70.0 | 1400 | 0.0593 | 1.0 | |
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| 0.4668 | 71.0 | 1420 | 0.0582 | 1.0 | |
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| 0.4668 | 72.0 | 1440 | 0.0579 | 1.0 | |
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| 0.4668 | 73.0 | 1460 | 0.0558 | 1.0 | |
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| 0.4668 | 74.0 | 1480 | 0.0540 | 1.0 | |
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| 0.0861 | 75.0 | 1500 | 0.0528 | 1.0 | |
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| 0.0861 | 76.0 | 1520 | 0.0522 | 1.0 | |
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| 0.0861 | 77.0 | 1540 | 0.0516 | 1.0 | |
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| 0.0861 | 78.0 | 1560 | 0.0502 | 1.0 | |
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| 0.0861 | 79.0 | 1580 | 0.0494 | 1.0 | |
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| 0.0861 | 80.0 | 1600 | 0.0490 | 1.0 | |
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| 0.0861 | 81.0 | 1620 | 0.0484 | 1.0 | |
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| 0.0861 | 82.0 | 1640 | 0.0480 | 1.0 | |
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| 0.0861 | 83.0 | 1660 | 0.0479 | 1.0 | |
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| 0.0861 | 84.0 | 1680 | 0.0476 | 1.0 | |
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| 0.0861 | 85.0 | 1700 | 0.0470 | 1.0 | |
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| 0.0861 | 86.0 | 1720 | 0.0465 | 1.0 | |
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| 0.0861 | 87.0 | 1740 | 0.0455 | 1.0 | |
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| 0.0861 | 88.0 | 1760 | 0.0449 | 1.0 | |
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| 0.0861 | 89.0 | 1780 | 0.0444 | 1.0 | |
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| 0.0861 | 90.0 | 1800 | 0.0444 | 1.0 | |
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| 0.0861 | 91.0 | 1820 | 0.0445 | 1.0 | |
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| 0.0861 | 92.0 | 1840 | 0.0441 | 1.0 | |
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| 0.0861 | 93.0 | 1860 | 0.0439 | 1.0 | |
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| 0.0861 | 94.0 | 1880 | 0.0438 | 1.0 | |
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| 0.0861 | 95.0 | 1900 | 0.0436 | 1.0 | |
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| 0.0861 | 96.0 | 1920 | 0.0434 | 1.0 | |
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| 0.0861 | 97.0 | 1940 | 0.0433 | 1.0 | |
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| 0.0861 | 98.0 | 1960 | 0.0431 | 1.0 | |
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| 0.0861 | 99.0 | 1980 | 0.0430 | 1.0 | |
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| 0.0493 | 100.0 | 2000 | 0.0430 | 1.0 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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