| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: Romance-cleaned-1 |
| | 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. --> |
| |
|
| | # Romance-cleaned-1 |
| |
|
| | This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 4.7175 |
| |
|
| | ## 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: 5e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 32 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 8 |
| | - total_train_batch_size: 256 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 1000 |
| | - num_epochs: 50 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | No log | 0.97 | 29 | 9.9497 | |
| | | No log | 1.97 | 58 | 9.1816 | |
| | | No log | 2.97 | 87 | 8.5947 | |
| | | No log | 3.97 | 116 | 8.2217 | |
| | | No log | 4.97 | 145 | 7.8354 | |
| | | No log | 5.97 | 174 | 7.5075 | |
| | | No log | 6.97 | 203 | 7.2112 | |
| | | No log | 7.97 | 232 | 6.9077 | |
| | | No log | 8.97 | 261 | 6.5994 | |
| | | No log | 9.97 | 290 | 6.3077 | |
| | | No log | 10.97 | 319 | 6.0416 | |
| | | No log | 11.97 | 348 | 5.8126 | |
| | | No log | 12.97 | 377 | 5.6197 | |
| | | No log | 13.97 | 406 | 5.4789 | |
| | | No log | 14.97 | 435 | 5.3665 | |
| | | No log | 15.97 | 464 | 5.2738 | |
| | | No log | 16.97 | 493 | 5.1942 | |
| | | No log | 17.97 | 522 | 5.1382 | |
| | | No log | 18.97 | 551 | 5.0784 | |
| | | No log | 19.97 | 580 | 5.0347 | |
| | | No log | 20.97 | 609 | 4.9873 | |
| | | No log | 21.97 | 638 | 4.9514 | |
| | | No log | 22.97 | 667 | 4.9112 | |
| | | No log | 23.97 | 696 | 4.8838 | |
| | | No log | 24.97 | 725 | 4.8468 | |
| | | No log | 25.97 | 754 | 4.8221 | |
| | | No log | 26.97 | 783 | 4.7996 | |
| | | No log | 27.97 | 812 | 4.7815 | |
| | | No log | 28.97 | 841 | 4.7606 | |
| | | No log | 29.97 | 870 | 4.7394 | |
| | | No log | 30.97 | 899 | 4.7167 | |
| | | No log | 31.97 | 928 | 4.7140 | |
| | | No log | 32.97 | 957 | 4.6910 | |
| | | No log | 33.97 | 986 | 4.6844 | |
| | | No log | 34.97 | 1015 | 4.6765 | |
| | | No log | 35.97 | 1044 | 4.6687 | |
| | | No log | 36.97 | 1073 | 4.6721 | |
| | | No log | 37.97 | 1102 | 4.6724 | |
| | | No log | 38.97 | 1131 | 4.6629 | |
| | | No log | 39.97 | 1160 | 4.6772 | |
| | | No log | 40.97 | 1189 | 4.6795 | |
| | | No log | 41.97 | 1218 | 4.6788 | |
| | | No log | 42.97 | 1247 | 4.6832 | |
| | | No log | 43.97 | 1276 | 4.6954 | |
| | | No log | 44.97 | 1305 | 4.7009 | |
| | | No log | 45.97 | 1334 | 4.7082 | |
| | | No log | 46.97 | 1363 | 4.7140 | |
| | | No log | 47.97 | 1392 | 4.7158 | |
| | | No log | 48.97 | 1421 | 4.7181 | |
| | | No log | 49.97 | 1450 | 4.7175 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.24.0 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.6.1 |
| | - Tokenizers 0.13.1 |
| |
|
