vi_gpt_poem_
This model is a fine-tuned version of NlpHUST/gpt-neo-vi-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1334
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: 1e-05
- train_batch_size: 42
- eval_batch_size: 42
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 250
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.8634 | 3.9683 | 500 | 6.1900 |
| 4.7999 | 7.9365 | 1000 | 3.4039 |
| 2.7473 | 11.9048 | 1500 | 2.5766 |
| 2.2513 | 15.8730 | 2000 | 2.2051 |
| 1.9426 | 19.8413 | 2500 | 1.9113 |
| 1.7059 | 23.8095 | 3000 | 1.6723 |
| 1.5333 | 27.7778 | 3500 | 1.5196 |
| 1.3996 | 31.7460 | 4000 | 1.4060 |
| 1.3066 | 35.7143 | 4500 | 1.3193 |
| 1.228 | 39.6825 | 5000 | 1.2513 |
| 1.1642 | 43.6508 | 5500 | 1.2000 |
| 1.1191 | 47.6190 | 6000 | 1.1607 |
| 1.0825 | 51.5873 | 6500 | 1.1295 |
| 1.0483 | 55.5556 | 7000 | 1.1036 |
| 1.0203 | 59.5238 | 7500 | 1.0818 |
| 0.9967 | 63.4921 | 8000 | 1.0631 |
| 0.9745 | 67.4603 | 8500 | 1.0471 |
| 0.9552 | 71.4286 | 9000 | 1.0332 |
| 0.9362 | 75.3968 | 9500 | 1.0208 |
| 0.9165 | 79.3651 | 10000 | 1.0098 |
| 0.8977 | 83.3333 | 10500 | 1.0002 |
| 0.8846 | 87.3016 | 11000 | 0.9915 |
| 0.8641 | 91.2698 | 11500 | 0.9838 |
| 0.8478 | 95.2381 | 12000 | 0.9779 |
| 0.8286 | 99.2063 | 12500 | 0.9721 |
| 0.811 | 103.1746 | 13000 | 0.9677 |
| 0.7916 | 107.1429 | 13500 | 0.9644 |
| 0.7721 | 111.1111 | 14000 | 0.9625 |
| 0.7513 | 115.0794 | 14500 | 0.9616 |
| 0.7292 | 119.0476 | 15000 | 0.9617 |
| 0.7066 | 123.0159 | 15500 | 0.9622 |
| 0.683 | 126.9841 | 16000 | 0.9639 |
| 0.6582 | 130.9524 | 16500 | 0.9661 |
| 0.632 | 134.9206 | 17000 | 0.9690 |
| 0.6047 | 138.8889 | 17500 | 0.9727 |
| 0.5769 | 142.8571 | 18000 | 0.9763 |
| 0.548 | 146.8254 | 18500 | 0.9802 |
| 0.5169 | 150.7937 | 19000 | 0.9844 |
| 0.4863 | 154.7619 | 19500 | 0.9887 |
| 0.4536 | 158.7302 | 20000 | 0.9936 |
| 0.4223 | 162.6984 | 20500 | 0.9975 |
| 0.3891 | 166.6667 | 21000 | 1.0022 |
| 0.3571 | 170.6349 | 21500 | 1.0071 |
| 0.3256 | 174.6032 | 22000 | 1.0118 |
| 0.2946 | 178.5714 | 22500 | 1.0164 |
| 0.2642 | 182.5397 | 23000 | 1.0221 |
| 0.2345 | 186.5079 | 23500 | 1.0271 |
| 0.2069 | 190.4762 | 24000 | 1.0331 |
| 0.1806 | 194.4444 | 24500 | 1.0393 |
| 0.1565 | 198.4127 | 25000 | 1.0462 |
| 0.1351 | 202.3810 | 25500 | 1.0527 |
| 0.1153 | 206.3492 | 26000 | 1.0605 |
| 0.0984 | 210.3175 | 26500 | 1.0679 |
| 0.0842 | 214.2857 | 27000 | 1.0758 |
| 0.0721 | 218.2540 | 27500 | 1.0827 |
| 0.0627 | 222.2222 | 28000 | 1.0906 |
| 0.0555 | 226.1905 | 28500 | 1.0978 |
| 0.0495 | 230.1587 | 29000 | 1.1043 |
| 0.045 | 234.1270 | 29500 | 1.1107 |
| 0.0412 | 238.0952 | 30000 | 1.1166 |
| 0.0382 | 242.0635 | 30500 | 1.1228 |
| 0.0356 | 246.0317 | 31000 | 1.1275 |
| 0.0335 | 250.0 | 31500 | 1.1334 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.19.1
- Downloads last month
- 61