text-to-icpc2
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7544
- Accuracy: 0.6679
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
5.2683 | 1.0 | 1093 | 5.0759 | 0.0819 |
4.0898 | 2.0 | 2186 | 4.1968 | 0.2182 |
3.4238 | 3.0 | 3279 | 3.5428 | 0.3321 |
2.9107 | 4.0 | 4372 | 3.0363 | 0.4099 |
2.4188 | 5.0 | 5465 | 2.6765 | 0.4895 |
1.9262 | 6.0 | 6558 | 2.3752 | 0.5339 |
1.6271 | 7.0 | 7651 | 2.1641 | 0.5787 |
1.3296 | 8.0 | 8744 | 1.9912 | 0.6162 |
1.1168 | 9.0 | 9837 | 1.8705 | 0.6455 |
0.8994 | 10.0 | 10930 | 1.8159 | 0.6624 |
0.8697 | 11.0 | 12023 | 1.7745 | 0.6679 |
0.8058 | 12.0 | 13116 | 1.7544 | 0.6679 |
Framework versions
- Transformers 4.44.0
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for diogocarapito/text-to-icpc2
Base model
google-bert/bert-base-uncased