<!-- 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. -->
mega-base-wikitext-finetuned-INCIBE
This model is a fine-tuned version of mnaylor/mega-base-wikitext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5169
- Accuracy: 0.3850
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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 196 | 1.5359 | 0.3742 |
| No log | 2.0 | 392 | 1.5257 | 0.3753 |
| 1.5035 | 3.0 | 588 | 1.5320 | 0.3719 |
| 1.5035 | 4.0 | 784 | 1.5231 | 0.3715 |
| 1.5035 | 5.0 | 980 | 1.5203 | 0.3745 |
| 1.4755 | 6.0 | 1176 | 1.5217 | 0.3742 |
| 1.4755 | 7.0 | 1372 | 1.5301 | 0.3719 |
| 1.4531 | 8.0 | 1568 | 1.5131 | 0.3805 |
| 1.4531 | 9.0 | 1764 | 1.5212 | 0.3783 |
| 1.4531 | 10.0 | 1960 | 1.5173 | 0.3771 |
| 1.4426 | 11.0 | 2156 | 1.5190 | 0.3809 |
| 1.4426 | 12.0 | 2352 | 1.5122 | 0.3794 |
| 1.4238 | 13.0 | 2548 | 1.5129 | 0.3794 |
| 1.4238 | 14.0 | 2744 | 1.5176 | 0.3783 |
| 1.4238 | 15.0 | 2940 | 1.5139 | 0.3783 |
| 1.4161 | 16.0 | 3136 | 1.5235 | 0.3805 |
| 1.4161 | 17.0 | 3332 | 1.5125 | 0.3846 |
| 1.4115 | 18.0 | 3528 | 1.5171 | 0.3827 |
| 1.4115 | 19.0 | 3724 | 1.5112 | 0.3827 |
| 1.4115 | 20.0 | 3920 | 1.5123 | 0.3816 |
| 1.4052 | 21.0 | 4116 | 1.5126 | 0.3827 |
| 1.4052 | 22.0 | 4312 | 1.5170 | 0.3850 |
| 1.4004 | 23.0 | 4508 | 1.5135 | 0.3805 |
| 1.4004 | 24.0 | 4704 | 1.5157 | 0.3809 |
| 1.4004 | 25.0 | 4900 | 1.5160 | 0.3824 |
Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
