|
|
--- |
|
|
library_name: transformers |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
model-index: |
|
|
- name: reverseadd_lr5e-4_batch128_train1-16_eval17 |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
|
|
# reverseadd_lr5e-4_batch128_train1-16_eval17 |
|
|
|
|
|
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.0001 |
|
|
- Accuracy: 1.0 |
|
|
|
|
|
## Model description |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Training and evaluation data |
|
|
|
|
|
More information needed |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 0.0005 |
|
|
- train_batch_size: 128 |
|
|
- eval_batch_size: 512 |
|
|
- seed: 23452399 |
|
|
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
|
- lr_scheduler_type: cosine |
|
|
- lr_scheduler_warmup_ratio: 0.1 |
|
|
- num_epochs: 1 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|
|:-------------:|:------:|:-----:|:---------------:|:--------:| |
|
|
| No log | 0 | 0 | 2.8155 | 0.0 | |
|
|
| 2.2946 | 0.0064 | 100 | 2.3351 | 0.0 | |
|
|
| 2.1902 | 0.0128 | 200 | 2.2386 | 0.0 | |
|
|
| 2.1098 | 0.0192 | 300 | 2.1977 | 0.0 | |
|
|
| 2.0555 | 0.0256 | 400 | 2.1978 | 0.0 | |
|
|
| 2.0039 | 0.032 | 500 | 2.1589 | 0.0 | |
|
|
| 1.8206 | 0.0384 | 600 | 1.9316 | 0.0 | |
|
|
| 1.7494 | 0.0448 | 700 | 1.8544 | 0.0 | |
|
|
| 1.6857 | 0.0512 | 800 | 1.8468 | 0.0 | |
|
|
| 1.4657 | 0.0576 | 900 | 1.6150 | 0.0 | |
|
|
| 1.4123 | 0.064 | 1000 | 1.6095 | 0.0 | |
|
|
| 1.4308 | 0.0704 | 1100 | 1.4342 | 0.0 | |
|
|
| 1.4203 | 0.0768 | 1200 | 1.4498 | 0.0014 | |
|
|
| 1.2564 | 0.0832 | 1300 | 1.3778 | 0.0 | |
|
|
| 1.4974 | 0.0896 | 1400 | 1.4512 | 0.0 | |
|
|
| 1.2751 | 0.096 | 1500 | 1.3121 | 0.0008 | |
|
|
| 1.3045 | 0.1024 | 1600 | 1.3020 | 0.0004 | |
|
|
| 1.4441 | 0.1088 | 1700 | 1.2960 | 0.0019 | |
|
|
| 1.2986 | 0.1152 | 1800 | 1.3620 | 0.0023 | |
|
|
| 1.202 | 0.1216 | 1900 | 1.3917 | 0.0 | |
|
|
| 1.0536 | 0.128 | 2000 | 1.2194 | 0.003 | |
|
|
| 1.1365 | 0.1344 | 2100 | 1.2133 | 0.002 | |
|
|
| 1.1016 | 0.1408 | 2200 | 1.3385 | 0.0012 | |
|
|
| 1.0749 | 0.1472 | 2300 | 1.2830 | 0.0002 | |
|
|
| 1.1077 | 0.1536 | 2400 | 1.2144 | 0.0018 | |
|
|
| 1.2018 | 0.16 | 2500 | 1.3076 | 0.0001 | |
|
|
| 1.0792 | 0.1664 | 2600 | 1.1722 | 0.0035 | |
|
|
| 1.1767 | 0.1728 | 2700 | 1.4412 | 0.0006 | |
|
|
| 1.1921 | 0.1792 | 2800 | 1.4885 | 0.0009 | |
|
|
| 1.1296 | 0.1856 | 2900 | 1.1856 | 0.0044 | |
|
|
| 1.1497 | 0.192 | 3000 | 1.2461 | 0.0003 | |
|
|
| 1.1368 | 0.1984 | 3100 | 1.2033 | 0.0019 | |
|
|
| 1.0723 | 0.2048 | 3200 | 1.1648 | 0.0044 | |
|
|
| 1.2805 | 0.2112 | 3300 | 1.3671 | 0.0012 | |
|
|
| 1.1424 | 0.2176 | 3400 | 1.2261 | 0.0034 | |
|
|
| 1.0507 | 0.224 | 3500 | 1.6901 | 0.0012 | |
|
|
| 1.1209 | 0.2304 | 3600 | 1.1904 | 0.0037 | |
|
|
| 1.0004 | 0.2368 | 3700 | 1.1728 | 0.0052 | |
|
|
| 1.0119 | 0.2432 | 3800 | 1.3097 | 0.0014 | |
|
|
| 0.8694 | 0.2496 | 3900 | 1.2167 | 0.0021 | |
|
|
| 0.7671 | 0.256 | 4000 | 0.9645 | 0.0063 | |
|
|
| 0.6854 | 0.2624 | 4100 | 0.8259 | 0.0103 | |
|
|
| 0.6831 | 0.2688 | 4200 | 1.8649 | 0.0052 | |
|
|
| 0.7282 | 0.2752 | 4300 | 1.0701 | 0.0129 | |
|
|
| 0.5102 | 0.2816 | 4400 | 0.7336 | 0.0177 | |
|
|
| 0.504 | 0.288 | 4500 | 0.9905 | 0.0037 | |
|
|
| 0.6358 | 0.2944 | 4600 | 0.9810 | 0.0056 | |
|
|
| 0.4155 | 0.3008 | 4700 | 0.7320 | 0.0203 | |
|
|
| 0.5209 | 0.3072 | 4800 | 0.7939 | 0.0143 | |
|
|
| 0.4059 | 0.3136 | 4900 | 0.7709 | 0.0224 | |
|
|
| 1.1919 | 0.32 | 5000 | 1.2763 | 0.0095 | |
|
|
| 0.4233 | 0.3264 | 5100 | 0.9546 | 0.0332 | |
|
|
| 0.4587 | 0.3328 | 5200 | 0.5571 | 0.024 | |
|
|
| 0.357 | 0.3392 | 5300 | 0.6538 | 0.0319 | |
|
|
| 0.1951 | 0.3456 | 5400 | 0.7499 | 0.0794 | |
|
|
| 0.0627 | 0.352 | 5500 | 0.1778 | 0.4928 | |
|
|
| 0.1673 | 0.3584 | 5600 | 0.4276 | 0.3864 | |
|
|
| 0.108 | 0.3648 | 5700 | 0.2546 | 0.475 | |
|
|
| 0.303 | 0.3712 | 5800 | 0.8399 | 0.1792 | |
|
|
| 0.0955 | 0.3776 | 5900 | 0.1259 | 0.6165 | |
|
|
| 0.3565 | 0.384 | 6000 | 0.4181 | 0.2727 | |
|
|
| 0.1023 | 0.3904 | 6100 | 0.3169 | 0.2621 | |
|
|
| 0.088 | 0.3968 | 6200 | 0.5066 | 0.4139 | |
|
|
| 0.1426 | 0.4032 | 6300 | 0.2009 | 0.4383 | |
|
|
| 0.0982 | 0.4096 | 6400 | 0.3973 | 0.4288 | |
|
|
| 0.0761 | 0.416 | 6500 | 0.1796 | 0.5228 | |
|
|
| 0.0535 | 0.4224 | 6600 | 0.1319 | 0.554 | |
|
|
| 0.067 | 0.4288 | 6700 | 0.1653 | 0.5779 | |
|
|
| 0.0809 | 0.4352 | 6800 | 0.2924 | 0.3632 | |
|
|
| 0.0287 | 0.4416 | 6900 | 0.2800 | 0.4756 | |
|
|
| 0.2862 | 0.448 | 7000 | 0.4914 | 0.4814 | |
|
|
| 0.1426 | 0.4544 | 7100 | 0.1894 | 0.4845 | |
|
|
| 0.5235 | 0.4608 | 7200 | 0.4767 | 0.3179 | |
|
|
| 0.1059 | 0.4672 | 7300 | 0.0758 | 0.7329 | |
|
|
| 0.0569 | 0.4736 | 7400 | 0.0930 | 0.6479 | |
|
|
| 0.2759 | 0.48 | 7500 | 0.6940 | 0.2162 | |
|
|
| 0.0365 | 0.4864 | 7600 | 0.1118 | 0.7336 | |
|
|
| 0.1459 | 0.4928 | 7700 | 0.3021 | 0.5704 | |
|
|
| 0.0642 | 0.4992 | 7800 | 0.3398 | 0.3859 | |
|
|
| 0.0475 | 0.5056 | 7900 | 0.1293 | 0.6375 | |
|
|
| 0.2529 | 0.512 | 8000 | 0.1974 | 0.5196 | |
|
|
| 0.0545 | 0.5184 | 8100 | 0.5865 | 0.0885 | |
|
|
| 0.023 | 0.5248 | 8200 | 0.2984 | 0.5018 | |
|
|
| 0.1067 | 0.5312 | 8300 | 0.1996 | 0.419 | |
|
|
| 0.0251 | 0.5376 | 8400 | 0.0553 | 0.7663 | |
|
|
| 0.0202 | 0.544 | 8500 | 0.0368 | 0.8618 | |
|
|
| 0.0487 | 0.5504 | 8600 | 0.3866 | 0.4186 | |
|
|
| 0.0637 | 0.5568 | 8700 | 0.1638 | 0.681 | |
|
|
| 0.0237 | 0.5632 | 8800 | 0.0808 | 0.6553 | |
|
|
| 0.0278 | 0.5696 | 8900 | 0.0793 | 0.6923 | |
|
|
| 0.0181 | 0.576 | 9000 | 0.0814 | 0.7107 | |
|
|
| 0.0385 | 0.5824 | 9100 | 0.0419 | 0.8253 | |
|
|
| 0.1081 | 0.5888 | 9200 | 0.2834 | 0.4629 | |
|
|
| 0.0539 | 0.5952 | 9300 | 0.1097 | 0.6756 | |
|
|
| 0.0059 | 0.6016 | 9400 | 0.0250 | 0.8896 | |
|
|
| 0.0109 | 0.608 | 9500 | 0.2312 | 0.4467 | |
|
|
| 0.0091 | 0.6144 | 9600 | 0.0257 | 0.9123 | |
|
|
| 0.0158 | 0.6208 | 9700 | 0.0657 | 0.7171 | |
|
|
| 0.0093 | 0.6272 | 9800 | 0.0579 | 0.7496 | |
|
|
| 0.0046 | 0.6336 | 9900 | 0.0079 | 0.9606 | |
|
|
| 0.0006 | 0.64 | 10000 | 0.3415 | 0.5193 | |
|
|
| 0.0284 | 0.6464 | 10100 | 0.1142 | 0.7089 | |
|
|
| 0.0011 | 0.6528 | 10200 | 0.0064 | 0.9759 | |
|
|
| 0.0035 | 0.6592 | 10300 | 0.1225 | 0.7545 | |
|
|
| 0.0081 | 0.6656 | 10400 | 0.0127 | 0.9453 | |
|
|
| 0.0003 | 0.672 | 10500 | 0.0052 | 0.9788 | |
|
|
| 0.0015 | 0.6784 | 10600 | 0.0175 | 0.9311 | |
|
|
| 0.0001 | 0.6848 | 10700 | 0.0066 | 0.9688 | |
|
|
| 0.0012 | 0.6912 | 10800 | 0.0459 | 0.877 | |
|
|
| 0.0002 | 0.6976 | 10900 | 0.0725 | 0.7628 | |
|
|
| 0.0002 | 0.704 | 11000 | 0.3270 | 0.4999 | |
|
|
| 0.0003 | 0.7104 | 11100 | 0.0195 | 0.8962 | |
|
|
| 0.0001 | 0.7168 | 11200 | 0.0041 | 0.9819 | |
|
|
| 0.0 | 0.7232 | 11300 | 0.0029 | 0.9864 | |
|
|
| 0.0 | 0.7296 | 11400 | 0.0010 | 0.9952 | |
|
|
| 0.0 | 0.736 | 11500 | 0.0017 | 0.9913 | |
|
|
| 0.0 | 0.7424 | 11600 | 0.0015 | 0.992 | |
|
|
| 0.0 | 0.7488 | 11700 | 0.0011 | 0.9941 | |
|
|
| 0.0002 | 0.7552 | 11800 | 0.0091 | 0.9548 | |
|
|
| 0.0 | 0.7616 | 11900 | 0.0002 | 0.9994 | |
|
|
| 0.0 | 0.768 | 12000 | 0.0003 | 0.9993 | |
|
|
| 0.0 | 0.7744 | 12100 | 0.0001 | 0.9997 | |
|
|
| 0.0 | 0.7808 | 12200 | 0.0005 | 0.9981 | |
|
|
| 0.0 | 0.7872 | 12300 | 0.0002 | 0.9995 | |
|
|
| 0.0 | 0.7936 | 12400 | 0.0002 | 0.9995 | |
|
|
| 0.0 | 0.8 | 12500 | 0.0001 | 0.9999 | |
|
|
| 0.0 | 0.8064 | 12600 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8128 | 12700 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8192 | 12800 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8256 | 12900 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.832 | 13000 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8384 | 13100 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8448 | 13200 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8512 | 13300 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8576 | 13400 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.864 | 13500 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8704 | 13600 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8768 | 13700 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8832 | 13800 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.8896 | 13900 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.896 | 14000 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.9024 | 14100 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.9088 | 14200 | 0.0000 | 1.0 | |
|
|
| 0.0 | 0.9152 | 14300 | 0.0001 | 0.9998 | |
|
|
| 0.0 | 0.9216 | 14400 | 0.0000 | 0.9999 | |
|
|
| 0.0 | 0.928 | 14500 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9344 | 14600 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9408 | 14700 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9472 | 14800 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9536 | 14900 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.96 | 15000 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9664 | 15100 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9728 | 15200 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9792 | 15300 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9856 | 15400 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.992 | 15500 | 0.0001 | 1.0 | |
|
|
| 0.0 | 0.9984 | 15600 | 0.0001 | 1.0 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.50.3 |
|
|
- Pytorch 2.6.0+cu124 |
|
|
- Tokenizers 0.21.1 |
|
|
|
