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--- |
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library_name: transformers |
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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: reverseadd_lr1e-3_batch128_train1-16_eval17 |
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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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# reverseadd_lr1e-3_batch128_train1-16_eval17 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0176 |
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- Accuracy: 0.9485 |
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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: 128 |
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- eval_batch_size: 512 |
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- seed: 23452399 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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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 | 0 | 0 | 2.7374 | 0.0 | |
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| 2.2579 | 0.0064 | 100 | 2.3228 | 0.0 | |
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| 2.2045 | 0.0128 | 200 | 2.2495 | 0.0 | |
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| 2.1557 | 0.0192 | 300 | 2.2500 | 0.0 | |
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| 2.047 | 0.0256 | 400 | 2.1012 | 0.0 | |
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| 1.7986 | 0.032 | 500 | 1.8433 | 0.0 | |
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| 1.3969 | 0.0384 | 600 | 1.5274 | 0.0001 | |
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| 1.4736 | 0.0448 | 700 | 1.5673 | 0.0 | |
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| 1.3964 | 0.0512 | 800 | 1.4298 | 0.0 | |
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| 1.4033 | 0.0576 | 900 | 1.4695 | 0.0002 | |
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| 1.4159 | 0.064 | 1000 | 1.5377 | 0.0009 | |
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| 1.4881 | 0.0704 | 1100 | 1.5835 | 0.0 | |
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| 1.2746 | 0.0768 | 1200 | 1.4343 | 0.0001 | |
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| 1.3636 | 0.0832 | 1300 | 1.4565 | 0.0001 | |
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| 1.2557 | 0.0896 | 1400 | 1.4595 | 0.0002 | |
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| 1.1776 | 0.096 | 1500 | 1.3051 | 0.0014 | |
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| 1.2127 | 0.1024 | 1600 | 1.2857 | 0.0015 | |
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| 1.242 | 0.1088 | 1700 | 1.2406 | 0.0008 | |
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| 1.2435 | 0.1152 | 1800 | 1.2902 | 0.0011 | |
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| 1.117 | 0.1216 | 1900 | 1.2265 | 0.0029 | |
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| 1.1521 | 0.128 | 2000 | 1.2086 | 0.0018 | |
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| 1.2051 | 0.1344 | 2100 | 1.3105 | 0.0012 | |
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| 1.086 | 0.1408 | 2200 | 1.2504 | 0.0015 | |
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| 1.1507 | 0.1472 | 2300 | 1.3024 | 0.0024 | |
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| 1.14 | 0.1536 | 2400 | 1.2296 | 0.0037 | |
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| 1.2656 | 0.16 | 2500 | 1.2506 | 0.0015 | |
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| 1.3129 | 0.1664 | 2600 | 1.2223 | 0.0025 | |
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| 1.1397 | 0.1728 | 2700 | 1.3158 | 0.0004 | |
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| 1.1166 | 0.1792 | 2800 | 1.2396 | 0.0011 | |
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| 1.0673 | 0.1856 | 2900 | 1.1634 | 0.0042 | |
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| 1.0978 | 0.192 | 3000 | 1.2137 | 0.0039 | |
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| 1.0777 | 0.1984 | 3100 | 1.2017 | 0.004 | |
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| 1.2009 | 0.2048 | 3200 | 1.3148 | 0.0002 | |
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| 1.0797 | 0.2112 | 3300 | 1.1881 | 0.0028 | |
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| 1.1487 | 0.2176 | 3400 | 1.1844 | 0.0033 | |
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| 1.0632 | 0.224 | 3500 | 1.1997 | 0.0021 | |
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| 1.2193 | 0.2304 | 3600 | 1.1709 | 0.0039 | |
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| 1.0189 | 0.2368 | 3700 | 1.2059 | 0.0044 | |
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| 1.1717 | 0.2432 | 3800 | 1.2218 | 0.0003 | |
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| 1.118 | 0.2496 | 3900 | 1.2008 | 0.0052 | |
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| 1.1155 | 0.256 | 4000 | 1.1963 | 0.0035 | |
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| 1.0966 | 0.2624 | 4100 | 1.1721 | 0.003 | |
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| 1.1334 | 0.2688 | 4200 | 1.1965 | 0.0015 | |
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| 0.9901 | 0.2752 | 4300 | 1.2008 | 0.0041 | |
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| 1.1018 | 0.2816 | 4400 | 1.1828 | 0.0025 | |
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| 1.0575 | 0.288 | 4500 | 1.1508 | 0.0052 | |
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| 1.0878 | 0.2944 | 4600 | 1.1773 | 0.004 | |
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| 1.1773 | 0.3008 | 4700 | 1.1889 | 0.0029 | |
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| 1.095 | 0.3072 | 4800 | 1.1653 | 0.0041 | |
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| 1.1231 | 0.3136 | 4900 | 1.1576 | 0.004 | |
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| 1.103 | 0.32 | 5000 | 1.2058 | 0.0042 | |
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| 1.2787 | 0.3264 | 5100 | 1.3836 | 0.0025 | |
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| 1.1088 | 0.3328 | 5200 | 1.1857 | 0.0038 | |
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| 1.0906 | 0.3392 | 5300 | 1.1543 | 0.0043 | |
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| 1.2167 | 0.3456 | 5400 | 1.1758 | 0.0038 | |
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| 1.1803 | 0.352 | 5500 | 1.1681 | 0.0049 | |
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| 1.1166 | 0.3584 | 5600 | 1.1553 | 0.0053 | |
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| 1.0343 | 0.3648 | 5700 | 1.1506 | 0.0068 | |
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| 1.0924 | 0.3712 | 5800 | 1.1725 | 0.0039 | |
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| 1.1639 | 0.3776 | 5900 | 1.1609 | 0.0054 | |
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| 1.205 | 0.384 | 6000 | 1.1615 | 0.0046 | |
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| 0.9991 | 0.3904 | 6100 | 1.2133 | 0.0018 | |
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| 1.2078 | 0.3968 | 6200 | 1.1543 | 0.0068 | |
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| 1.1684 | 0.4032 | 6300 | 1.2028 | 0.0043 | |
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| 1.121 | 0.4096 | 6400 | 1.1512 | 0.0067 | |
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| 1.2152 | 0.416 | 6500 | 1.1733 | 0.005 | |
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| 1.1142 | 0.4224 | 6600 | 1.1565 | 0.0045 | |
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| 1.0497 | 0.4288 | 6700 | 1.1629 | 0.0061 | |
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| 1.0567 | 0.4352 | 6800 | 1.1472 | 0.0054 | |
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| 1.2305 | 0.4416 | 6900 | 1.1740 | 0.0058 | |
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| 1.102 | 0.448 | 7000 | 1.1495 | 0.0054 | |
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| 1.1117 | 0.4544 | 7100 | 1.1490 | 0.0068 | |
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| 1.1326 | 0.4608 | 7200 | 1.1473 | 0.0051 | |
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| 1.0943 | 0.4672 | 7300 | 1.1506 | 0.0039 | |
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| 1.0523 | 0.4736 | 7400 | 1.1493 | 0.0055 | |
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| 1.1823 | 0.48 | 7500 | 1.1465 | 0.006 | |
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| 1.0432 | 0.4864 | 7600 | 1.2053 | 0.0021 | |
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| 1.0672 | 0.4928 | 7700 | 1.1615 | 0.0066 | |
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| 1.1454 | 0.4992 | 7800 | 1.1529 | 0.0047 | |
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| 1.1457 | 0.5056 | 7900 | 1.2006 | 0.0049 | |
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| 1.1083 | 0.512 | 8000 | 1.1548 | 0.0053 | |
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| 1.1441 | 0.5184 | 8100 | 1.1626 | 0.0049 | |
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| 1.1128 | 0.5248 | 8200 | 1.1483 | 0.0064 | |
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| 1.0955 | 0.5312 | 8300 | 1.1454 | 0.0075 | |
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| 1.1613 | 0.5376 | 8400 | 1.1456 | 0.0051 | |
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| 1.0653 | 0.544 | 8500 | 1.1873 | 0.0058 | |
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| 0.9913 | 0.5504 | 8600 | 1.1680 | 0.0066 | |
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| 1.1255 | 0.5568 | 8700 | 1.1915 | 0.0039 | |
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| 1.2043 | 0.5632 | 8800 | 1.1515 | 0.0058 | |
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| 1.1467 | 0.5696 | 8900 | 1.1452 | 0.0079 | |
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| 1.1865 | 0.576 | 9000 | 1.1450 | 0.0069 | |
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| 1.1382 | 0.5824 | 9100 | 1.1447 | 0.0064 | |
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| 1.1424 | 0.5888 | 9200 | 1.1449 | 0.0064 | |
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| 1.1269 | 0.5952 | 9300 | 1.1451 | 0.006 | |
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| 1.129 | 0.6016 | 9400 | 1.1451 | 0.0074 | |
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| 1.0858 | 0.608 | 9500 | 1.1449 | 0.0073 | |
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| 1.0801 | 0.6144 | 9600 | 1.1533 | 0.0062 | |
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| 1.1672 | 0.6208 | 9700 | 1.1621 | 0.0045 | |
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| 1.1604 | 0.6272 | 9800 | 1.1844 | 0.0037 | |
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| 1.0944 | 0.6336 | 9900 | 1.1458 | 0.0057 | |
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| 1.0786 | 0.64 | 10000 | 1.1183 | 0.0063 | |
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| 0.9723 | 0.6464 | 10100 | 1.1249 | 0.0062 | |
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| 0.9659 | 0.6528 | 10200 | 1.0463 | 0.0059 | |
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| 0.6733 | 0.6592 | 10300 | 0.8517 | 0.0149 | |
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| 0.5558 | 0.6656 | 10400 | 0.7018 | 0.0157 | |
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| 0.4558 | 0.672 | 10500 | 0.5640 | 0.027 | |
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| 0.4872 | 0.6784 | 10600 | 0.6440 | 0.0215 | |
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| 0.3834 | 0.6848 | 10700 | 0.4560 | 0.0334 | |
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| 0.3426 | 0.6912 | 10800 | 0.6477 | 0.0242 | |
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| 0.2898 | 0.6976 | 10900 | 0.3979 | 0.0392 | |
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| 0.3459 | 0.704 | 11000 | 0.3506 | 0.0449 | |
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| 0.2746 | 0.7104 | 11100 | 0.4129 | 0.044 | |
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| 0.253 | 0.7168 | 11200 | 0.2852 | 0.0582 | |
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| 0.2651 | 0.7232 | 11300 | 0.4106 | 0.0395 | |
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| 0.4021 | 0.7296 | 11400 | 0.5244 | 0.0314 | |
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| 0.2693 | 0.736 | 11500 | 0.2262 | 0.0617 | |
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| 0.2531 | 0.7424 | 11600 | 0.2425 | 0.0654 | |
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| 0.241 | 0.7488 | 11700 | 0.3370 | 0.0567 | |
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| 0.2242 | 0.7552 | 11800 | 0.3969 | 0.0499 | |
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| 0.2118 | 0.7616 | 11900 | 0.2066 | 0.0625 | |
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| 0.2148 | 0.768 | 12000 | 0.2331 | 0.0635 | |
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| 0.2023 | 0.7744 | 12100 | 0.1896 | 0.0775 | |
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| 0.2403 | 0.7808 | 12200 | 0.3973 | 0.049 | |
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| 0.2005 | 0.7872 | 12300 | 0.1956 | 0.0727 | |
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| 0.2087 | 0.7936 | 12400 | 0.1675 | 0.0843 | |
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| 0.1959 | 0.8 | 12500 | 0.2289 | 0.0864 | |
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| 0.1292 | 0.8064 | 12600 | 0.1869 | 0.1999 | |
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| 0.0253 | 0.8128 | 12700 | 0.0945 | 0.6332 | |
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| 0.0283 | 0.8192 | 12800 | 0.0571 | 0.723 | |
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| 0.0622 | 0.8256 | 12900 | 0.1068 | 0.6635 | |
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| 0.0158 | 0.832 | 13000 | 0.2797 | 0.4979 | |
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| 0.0148 | 0.8384 | 13100 | 0.0956 | 0.6667 | |
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| 0.0038 | 0.8448 | 13200 | 0.0181 | 0.9264 | |
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| 0.0062 | 0.8512 | 13300 | 0.0094 | 0.9703 | |
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| 0.0123 | 0.8576 | 13400 | 0.0134 | 0.9436 | |
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| 0.0042 | 0.864 | 13500 | 0.0321 | 0.9005 | |
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| 0.0024 | 0.8704 | 13600 | 0.0181 | 0.9105 | |
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| 0.0036 | 0.8768 | 13700 | 0.0142 | 0.9384 | |
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| 0.0087 | 0.8832 | 13800 | 0.0252 | 0.9019 | |
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| 0.0036 | 0.8896 | 13900 | 0.0292 | 0.9247 | |
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| 0.0017 | 0.896 | 14000 | 0.0098 | 0.961 | |
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| 0.0029 | 0.9024 | 14100 | 0.0120 | 0.9524 | |
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| 0.0021 | 0.9088 | 14200 | 0.0161 | 0.9306 | |
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| 0.0008 | 0.9152 | 14300 | 0.0132 | 0.9375 | |
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| 0.0055 | 0.9216 | 14400 | 0.0106 | 0.9602 | |
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| 0.0012 | 0.928 | 14500 | 0.0130 | 0.9502 | |
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| 0.0013 | 0.9344 | 14600 | 0.0104 | 0.9487 | |
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| 0.0007 | 0.9408 | 14700 | 0.0128 | 0.9638 | |
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| 0.0031 | 0.9472 | 14800 | 0.0159 | 0.9561 | |
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| 0.0008 | 0.9536 | 14900 | 0.0211 | 0.941 | |
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| 0.0012 | 0.96 | 15000 | 0.0195 | 0.9332 | |
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| 0.0011 | 0.9664 | 15100 | 0.0170 | 0.9434 | |
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| 0.0007 | 0.9728 | 15200 | 0.0174 | 0.9468 | |
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| 0.0026 | 0.9792 | 15300 | 0.0176 | 0.9512 | |
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| 0.0085 | 0.9856 | 15400 | 0.0178 | 0.9481 | |
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| 0.0017 | 0.992 | 15500 | 0.0177 | 0.9481 | |
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| 0.0009 | 0.9984 | 15600 | 0.0176 | 0.9485 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Tokenizers 0.21.1 |
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