| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - bleu |
| model-index: |
| - name: parallel-gpt2-medium-wikitext |
| 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. --> |
|
|
| # parallel-gpt2-medium-wikitext |
|
|
| 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: 3.2350 |
| - Accuracy: 0.4161 |
| - Perplexity: 25.4075 |
| - Bleu: 0.1473 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - 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: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | Bleu | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:| |
| | 6.077 | 0.2806 | 500 | 5.9554 | 0.1870 | 385.8189 | 0.0352 | |
| | 5.1123 | 0.5612 | 1000 | 4.9836 | 0.2568 | 145.9931 | 0.0625 | |
| | 4.4123 | 0.8418 | 1500 | 4.3035 | 0.3159 | 73.9588 | 0.0843 | |
| | 4.0245 | 1.1223 | 2000 | 3.9678 | 0.3470 | 52.8693 | 0.1076 | |
| | 3.8298 | 1.4029 | 2500 | 3.7842 | 0.3630 | 44.0014 | 0.1166 | |
| | 3.7181 | 1.6835 | 3000 | 3.6620 | 0.3733 | 38.9404 | 0.1272 | |
| | 3.6123 | 1.9641 | 3500 | 3.5694 | 0.3818 | 35.4958 | 0.1311 | |
| | 3.4993 | 2.2447 | 4000 | 3.5029 | 0.3877 | 33.2118 | 0.1384 | |
| | 3.4358 | 2.5253 | 4500 | 3.4484 | 0.3930 | 31.4506 | 0.1358 | |
| | 3.4039 | 2.8058 | 5000 | 3.3989 | 0.3979 | 29.9323 | 0.1403 | |
| | 3.2908 | 3.0864 | 5500 | 3.3633 | 0.4018 | 28.8837 | 0.1409 | |
| | 3.2828 | 3.3670 | 6000 | 3.3326 | 0.4051 | 28.0103 | 0.1446 | |
| | 3.2606 | 3.6476 | 6500 | 3.3031 | 0.4081 | 27.1958 | 0.1457 | |
| | 3.234 | 3.9282 | 7000 | 3.2796 | 0.4106 | 26.5655 | 0.1433 | |
| | 3.1713 | 4.2088 | 7500 | 3.2621 | 0.4126 | 26.1045 | 0.1461 | |
| | 3.1314 | 4.4893 | 8000 | 3.2476 | 0.4145 | 25.7281 | 0.1455 | |
| | 3.1412 | 4.7699 | 8500 | 3.2350 | 0.4161 | 25.4075 | 0.1473 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.3.2 |
| - Tokenizers 0.21.0 |
| |
