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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-fqa-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-fqa-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: 0.0654 |
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- Accuracy: 0.9897 |
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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 | 110 | 5.2087 | 0.0051 | |
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| No log | 2.0 | 220 | 5.2019 | 0.0 | |
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| No log | 3.0 | 330 | 5.1778 | 0.0 | |
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| No log | 4.0 | 440 | 5.0728 | 0.0154 | |
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| 5.1354 | 5.0 | 550 | 4.7829 | 0.0974 | |
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| 5.1354 | 6.0 | 660 | 4.4895 | 0.1385 | |
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| 5.1354 | 7.0 | 770 | 4.1981 | 0.2923 | |
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| 5.1354 | 8.0 | 880 | 3.9328 | 0.4359 | |
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| 5.1354 | 9.0 | 990 | 3.6937 | 0.5641 | |
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| 4.2477 | 10.0 | 1100 | 3.4751 | 0.6308 | |
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| 4.2477 | 11.0 | 1210 | 3.2537 | 0.7026 | |
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| 4.2477 | 12.0 | 1320 | 3.0355 | 0.7590 | |
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| 4.2477 | 13.0 | 1430 | 2.8367 | 0.7846 | |
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| 3.1067 | 14.0 | 1540 | 2.6431 | 0.8103 | |
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| 3.1067 | 15.0 | 1650 | 2.4553 | 0.8462 | |
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| 3.1067 | 16.0 | 1760 | 2.2817 | 0.8718 | |
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| 3.1067 | 17.0 | 1870 | 2.1120 | 0.8821 | |
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| 3.1067 | 18.0 | 1980 | 1.9496 | 0.8974 | |
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| 2.1912 | 19.0 | 2090 | 1.7956 | 0.9128 | |
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| 2.1912 | 20.0 | 2200 | 1.6507 | 0.9179 | |
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| 2.1912 | 21.0 | 2310 | 1.5192 | 0.9282 | |
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| 2.1912 | 22.0 | 2420 | 1.3942 | 0.9333 | |
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| 1.48 | 23.0 | 2530 | 1.2758 | 0.9436 | |
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| 1.48 | 24.0 | 2640 | 1.1671 | 0.9538 | |
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| 1.48 | 25.0 | 2750 | 1.0670 | 0.9590 | |
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| 1.48 | 26.0 | 2860 | 0.9741 | 0.9590 | |
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| 1.48 | 27.0 | 2970 | 0.8877 | 0.9590 | |
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| 0.9627 | 28.0 | 3080 | 0.8078 | 0.9641 | |
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| 0.9627 | 29.0 | 3190 | 0.7388 | 0.9641 | |
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| 0.9627 | 30.0 | 3300 | 0.6762 | 0.9692 | |
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| 0.9627 | 31.0 | 3410 | 0.6123 | 0.9744 | |
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| 0.6161 | 32.0 | 3520 | 0.5591 | 0.9744 | |
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| 0.6161 | 33.0 | 3630 | 0.5129 | 0.9744 | |
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| 0.6161 | 34.0 | 3740 | 0.4734 | 0.9744 | |
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| 0.6161 | 35.0 | 3850 | 0.4341 | 0.9692 | |
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| 0.6161 | 36.0 | 3960 | 0.3932 | 0.9846 | |
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| 0.3834 | 37.0 | 4070 | 0.3645 | 0.9795 | |
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| 0.3834 | 38.0 | 4180 | 0.3398 | 0.9744 | |
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| 0.3834 | 39.0 | 4290 | 0.3128 | 0.9846 | |
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| 0.3834 | 40.0 | 4400 | 0.2884 | 0.9795 | |
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| 0.2414 | 41.0 | 4510 | 0.2659 | 0.9846 | |
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| 0.2414 | 42.0 | 4620 | 0.2488 | 0.9846 | |
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| 0.2414 | 43.0 | 4730 | 0.2286 | 0.9897 | |
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| 0.2414 | 44.0 | 4840 | 0.2145 | 0.9897 | |
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| 0.2414 | 45.0 | 4950 | 0.2014 | 0.9949 | |
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| 0.1522 | 46.0 | 5060 | 0.1913 | 0.9897 | |
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| 0.1522 | 47.0 | 5170 | 0.1808 | 0.9897 | |
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| 0.1522 | 48.0 | 5280 | 0.1697 | 0.9897 | |
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| 0.1522 | 49.0 | 5390 | 0.1604 | 0.9846 | |
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| 0.0981 | 50.0 | 5500 | 0.1551 | 0.9846 | |
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| 0.0981 | 51.0 | 5610 | 0.1448 | 0.9897 | |
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| 0.0981 | 52.0 | 5720 | 0.1390 | 0.9949 | |
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| 0.0981 | 53.0 | 5830 | 0.1348 | 0.9846 | |
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| 0.0981 | 54.0 | 5940 | 0.1268 | 0.9897 | |
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| 0.0679 | 55.0 | 6050 | 0.1206 | 0.9897 | |
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| 0.0679 | 56.0 | 6160 | 0.1178 | 0.9897 | |
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| 0.0679 | 57.0 | 6270 | 0.1162 | 0.9897 | |
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| 0.0679 | 58.0 | 6380 | 0.1126 | 0.9897 | |
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| 0.0679 | 59.0 | 6490 | 0.1086 | 0.9897 | |
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| 0.0504 | 60.0 | 6600 | 0.1053 | 0.9897 | |
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| 0.0504 | 61.0 | 6710 | 0.1020 | 0.9897 | |
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| 0.0504 | 62.0 | 6820 | 0.0995 | 0.9897 | |
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| 0.0504 | 63.0 | 6930 | 0.0973 | 0.9897 | |
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| 0.0397 | 64.0 | 7040 | 0.0968 | 0.9897 | |
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| 0.0397 | 65.0 | 7150 | 0.0921 | 0.9949 | |
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| 0.0397 | 66.0 | 7260 | 0.0900 | 0.9897 | |
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| 0.0397 | 67.0 | 7370 | 0.0886 | 0.9949 | |
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| 0.0397 | 68.0 | 7480 | 0.0865 | 0.9949 | |
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| 0.0321 | 69.0 | 7590 | 0.0854 | 0.9949 | |
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| 0.0321 | 70.0 | 7700 | 0.0849 | 0.9949 | |
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| 0.0321 | 71.0 | 7810 | 0.0835 | 0.9897 | |
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| 0.0321 | 72.0 | 7920 | 0.0822 | 0.9949 | |
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| 0.0269 | 73.0 | 8030 | 0.0805 | 0.9897 | |
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| 0.0269 | 74.0 | 8140 | 0.0789 | 0.9949 | |
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| 0.0269 | 75.0 | 8250 | 0.0781 | 0.9897 | |
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| 0.0269 | 76.0 | 8360 | 0.0773 | 0.9897 | |
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| 0.0269 | 77.0 | 8470 | 0.0760 | 0.9897 | |
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| 0.0229 | 78.0 | 8580 | 0.0749 | 0.9949 | |
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| 0.0229 | 79.0 | 8690 | 0.0745 | 0.9897 | |
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| 0.0229 | 80.0 | 8800 | 0.0736 | 0.9897 | |
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| 0.0229 | 81.0 | 8910 | 0.0728 | 0.9949 | |
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| 0.0202 | 82.0 | 9020 | 0.0717 | 0.9949 | |
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| 0.0202 | 83.0 | 9130 | 0.0711 | 0.9949 | |
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| 0.0202 | 84.0 | 9240 | 0.0711 | 0.9949 | |
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| 0.0202 | 85.0 | 9350 | 0.0710 | 0.9897 | |
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| 0.0202 | 86.0 | 9460 | 0.0704 | 0.9897 | |
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| 0.018 | 87.0 | 9570 | 0.0687 | 0.9897 | |
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| 0.018 | 88.0 | 9680 | 0.0685 | 0.9897 | |
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| 0.018 | 89.0 | 9790 | 0.0677 | 0.9897 | |
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| 0.018 | 90.0 | 9900 | 0.0675 | 0.9897 | |
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| 0.0165 | 91.0 | 10010 | 0.0669 | 0.9897 | |
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| 0.0165 | 92.0 | 10120 | 0.0673 | 0.9897 | |
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| 0.0165 | 93.0 | 10230 | 0.0664 | 0.9897 | |
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| 0.0165 | 94.0 | 10340 | 0.0658 | 0.9897 | |
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| 0.0165 | 95.0 | 10450 | 0.0657 | 0.9897 | |
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| 0.0156 | 96.0 | 10560 | 0.0657 | 0.9897 | |
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| 0.0156 | 97.0 | 10670 | 0.0656 | 0.9897 | |
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| 0.0156 | 98.0 | 10780 | 0.0655 | 0.9897 | |
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| 0.0156 | 99.0 | 10890 | 0.0654 | 0.9897 | |
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| 0.0149 | 100.0 | 11000 | 0.0654 | 0.9897 | |
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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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