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--- |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- bleu |
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model-index: |
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- name: parallel-mean-bottleneck-gpt2-medium-wikitext |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# parallel-mean-bottleneck-gpt2-medium-wikitext |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.1864 |
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- Accuracy: 0.4195 |
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- Perplexity: 24.2005 |
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- Bleu: 0.1476 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Perplexity | Bleu | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:----------:|:------:| |
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| 6.0443 | 0.2806 | 500 | 5.9164 | 0.1901 | 371.0844 | 0.0350 | |
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| 5.0429 | 0.5612 | 1000 | 4.8947 | 0.2638 | 133.5839 | 0.0647 | |
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| 4.3531 | 0.8418 | 1500 | 4.2426 | 0.3176 | 69.5891 | 0.0829 | |
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| 3.9503 | 1.1223 | 2000 | 3.8874 | 0.3517 | 48.7842 | 0.1050 | |
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| 3.7613 | 1.4029 | 2500 | 3.7124 | 0.3672 | 40.9504 | 0.1211 | |
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| 3.6548 | 1.6835 | 3000 | 3.5911 | 0.3780 | 36.2753 | 0.1308 | |
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| 3.5531 | 1.9641 | 3500 | 3.5068 | 0.3860 | 33.3428 | 0.1340 | |
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| 3.4344 | 2.2447 | 4000 | 3.4411 | 0.3920 | 31.2224 | 0.1356 | |
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| 3.3743 | 2.5253 | 4500 | 3.3875 | 0.3972 | 29.5917 | 0.1389 | |
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| 3.3443 | 2.8058 | 5000 | 3.3429 | 0.4016 | 28.3017 | 0.1373 | |
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| 3.225 | 3.0864 | 5500 | 3.3080 | 0.4055 | 27.3310 | 0.1419 | |
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| 3.2185 | 3.3670 | 6000 | 3.2781 | 0.4090 | 26.5258 | 0.1463 | |
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| 3.1972 | 3.6476 | 6500 | 3.2500 | 0.4121 | 25.7899 | 0.1453 | |
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| 3.1719 | 3.9282 | 7000 | 3.2268 | 0.4144 | 25.1990 | 0.1465 | |
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| 3.1052 | 4.2088 | 7500 | 3.2109 | 0.4162 | 24.8018 | 0.1472 | |
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| 3.0672 | 4.4893 | 8000 | 3.1978 | 0.4179 | 24.4788 | 0.1469 | |
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| 3.0773 | 4.7699 | 8500 | 3.1864 | 0.4195 | 24.2005 | 0.1476 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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