| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: Romance-cleaned-2 |
| | 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-2 |
| |
|
| | 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: 5.0319 |
| |
|
| | ## 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 | 0.96 | 16 | 10.3553 | |
| | | No log | 1.96 | 32 | 9.5625 | |
| | | No log | 2.96 | 48 | 9.0898 | |
| | | No log | 3.96 | 64 | 8.7852 | |
| | | No log | 4.96 | 80 | 8.4694 | |
| | | No log | 5.96 | 96 | 8.2122 | |
| | | No log | 6.96 | 112 | 8.0040 | |
| | | No log | 7.96 | 128 | 7.8029 | |
| | | No log | 8.96 | 144 | 7.5950 | |
| | | No log | 9.96 | 160 | 7.4081 | |
| | | No log | 10.96 | 176 | 7.2391 | |
| | | No log | 11.96 | 192 | 7.0784 | |
| | | No log | 12.96 | 208 | 6.9139 | |
| | | No log | 13.96 | 224 | 6.7530 | |
| | | No log | 14.96 | 240 | 6.5983 | |
| | | No log | 15.96 | 256 | 6.4403 | |
| | | No log | 16.96 | 272 | 6.3025 | |
| | | No log | 17.96 | 288 | 6.1562 | |
| | | No log | 18.96 | 304 | 6.0147 | |
| | | No log | 19.96 | 320 | 5.8919 | |
| | | No log | 20.96 | 336 | 5.7709 | |
| | | No log | 21.96 | 352 | 5.6666 | |
| | | No log | 22.96 | 368 | 5.5818 | |
| | | No log | 23.96 | 384 | 5.5051 | |
| | | No log | 24.96 | 400 | 5.4356 | |
| | | No log | 25.96 | 416 | 5.3788 | |
| | | No log | 26.96 | 432 | 5.3230 | |
| | | No log | 27.96 | 448 | 5.2823 | |
| | | No log | 28.96 | 464 | 5.2513 | |
| | | No log | 29.96 | 480 | 5.2218 | |
| | | No log | 30.96 | 496 | 5.1910 | |
| | | No log | 31.96 | 512 | 5.1609 | |
| | | No log | 32.96 | 528 | 5.1500 | |
| | | No log | 33.96 | 544 | 5.1268 | |
| | | No log | 34.96 | 560 | 5.1012 | |
| | | No log | 35.96 | 576 | 5.0973 | |
| | | No log | 36.96 | 592 | 5.0769 | |
| | | No log | 37.96 | 608 | 5.0653 | |
| | | No log | 38.96 | 624 | 5.0489 | |
| | | No log | 39.96 | 640 | 5.0458 | |
| | | No log | 40.96 | 656 | 5.0379 | |
| | | No log | 41.96 | 672 | 5.0347 | |
| | | No log | 42.96 | 688 | 5.0161 | |
| | | No log | 43.96 | 704 | 5.0226 | |
| | | No log | 44.96 | 720 | 5.0215 | |
| | | No log | 45.96 | 736 | 5.0190 | |
| | | No log | 46.96 | 752 | 5.0087 | |
| | | No log | 47.96 | 768 | 5.0309 | |
| | | No log | 48.96 | 784 | 5.0232 | |
| | | No log | 49.96 | 800 | 5.0319 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.24.0 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.6.1 |
| | - Tokenizers 0.13.1 |
| |
|
