| | --- |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: Romance-baseline |
| | 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-baseline |
| |
|
| | 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: 6.5909 |
| |
|
| | ## 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 | 0.94 | 15 | 10.7009 | |
| | | No log | 1.94 | 30 | 10.0799 | |
| | | No log | 2.94 | 45 | 9.6627 | |
| | | No log | 3.94 | 60 | 9.4619 | |
| | | No log | 4.94 | 75 | 9.2970 | |
| | | No log | 5.94 | 90 | 9.0919 | |
| | | No log | 6.94 | 105 | 8.9071 | |
| | | No log | 7.94 | 120 | 8.7240 | |
| | | No log | 8.94 | 135 | 8.5485 | |
| | | No log | 9.94 | 150 | 8.3952 | |
| | | No log | 10.94 | 165 | 8.2469 | |
| | | No log | 11.94 | 180 | 8.1193 | |
| | | No log | 12.94 | 195 | 7.9918 | |
| | | No log | 13.94 | 210 | 7.8662 | |
| | | No log | 14.94 | 225 | 7.7394 | |
| | | No log | 15.94 | 240 | 7.6219 | |
| | | No log | 16.94 | 255 | 7.5135 | |
| | | No log | 17.94 | 270 | 7.4110 | |
| | | No log | 18.94 | 285 | 7.3021 | |
| | | No log | 19.94 | 300 | 7.2021 | |
| | | No log | 20.94 | 315 | 7.1276 | |
| | | No log | 21.94 | 330 | 7.0278 | |
| | | No log | 22.94 | 345 | 6.9627 | |
| | | No log | 23.94 | 360 | 6.8806 | |
| | | No log | 24.94 | 375 | 6.8214 | |
| | | No log | 25.94 | 390 | 6.7725 | |
| | | No log | 26.94 | 405 | 6.7101 | |
| | | No log | 27.94 | 420 | 6.6792 | |
| | | No log | 28.94 | 435 | 6.6361 | |
| | | No log | 29.94 | 450 | 6.5950 | |
| | | No log | 30.94 | 465 | 6.5745 | |
| | | No log | 31.94 | 480 | 6.5469 | |
| | | No log | 32.94 | 495 | 6.5520 | |
| | | No log | 33.94 | 510 | 6.5121 | |
| | | No log | 34.94 | 525 | 6.5255 | |
| | | No log | 35.94 | 540 | 6.5179 | |
| | | No log | 36.94 | 555 | 6.5079 | |
| | | No log | 37.94 | 570 | 6.5138 | |
| | | No log | 38.94 | 585 | 6.5170 | |
| | | No log | 39.94 | 600 | 6.4807 | |
| | | No log | 40.94 | 615 | 6.5338 | |
| | | No log | 41.94 | 630 | 6.4960 | |
| | | No log | 42.94 | 645 | 6.5342 | |
| | | No log | 43.94 | 660 | 6.5119 | |
| | | No log | 44.94 | 675 | 6.5614 | |
| | | No log | 45.94 | 690 | 6.5235 | |
| | | No log | 46.94 | 705 | 6.5388 | |
| | | No log | 47.94 | 720 | 6.5574 | |
| | | No log | 48.94 | 735 | 6.5581 | |
| | | No log | 49.94 | 750 | 6.5909 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.24.0 |
| | - Pytorch 1.12.1+cu113 |
| | - Datasets 2.6.1 |
| | - Tokenizers 0.13.1 |
| |
|
