| | --- |
| | library_name: transformers |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - bleu |
| | model-index: |
| | - name: 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. --> |
| |
|
| | # 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: 3.1671 |
| | - Accuracy: 0.4217 |
| | - Perplexity: 23.7377 |
| | - Bleu: 0.1460 |
| |
|
| | ## 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 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:| |
| | | 6.0809 | 0.2806 | 500 | 5.9580 | 0.1883 | 386.8254 | 0.0333 | |
| | | 5.0644 | 0.5612 | 1000 | 4.9191 | 0.2623 | 136.8761 | 0.0651 | |
| | | 4.3331 | 0.8418 | 1500 | 4.2124 | 0.3226 | 67.5163 | 0.0890 | |
| | | 3.9451 | 1.1223 | 2000 | 3.8835 | 0.3532 | 48.5942 | 0.1090 | |
| | | 3.7568 | 1.4029 | 2500 | 3.7051 | 0.3684 | 40.6559 | 0.1226 | |
| | | 3.6478 | 1.6835 | 3000 | 3.5827 | 0.3787 | 35.9710 | 0.1311 | |
| | | 3.5435 | 1.9641 | 3500 | 3.4940 | 0.3877 | 32.9179 | 0.1343 | |
| | | 3.4222 | 2.2447 | 4000 | 3.4292 | 0.3936 | 30.8527 | 0.1343 | |
| | | 3.3604 | 2.5253 | 4500 | 3.3728 | 0.3990 | 29.1601 | 0.1414 | |
| | | 3.3288 | 2.8058 | 5000 | 3.3269 | 0.4038 | 27.8518 | 0.1381 | |
| | | 3.2074 | 3.0864 | 5500 | 3.2887 | 0.4079 | 26.8092 | 0.1423 | |
| | | 3.2007 | 3.3670 | 6000 | 3.2605 | 0.4115 | 26.0632 | 0.1464 | |
| | | 3.1787 | 3.6476 | 6500 | 3.2328 | 0.4140 | 25.3497 | 0.1428 | |
| | | 3.1529 | 3.9282 | 7000 | 3.2085 | 0.4166 | 24.7424 | 0.1425 | |
| | | 3.0849 | 4.2088 | 7500 | 3.1921 | 0.4184 | 24.3384 | 0.1430 | |
| | | 3.0471 | 4.4893 | 8000 | 3.1796 | 0.4202 | 24.0366 | 0.1428 | |
| | | 3.0569 | 4.7699 | 8500 | 3.1671 | 0.4217 | 23.7377 | 0.1460 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.49.0 |
| | - Pytorch 2.6.0+cu124 |
| | - Datasets 3.3.2 |
| | - Tokenizers 0.21.0 |
| | |
