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V3_20230929-5-xlm-roberta-base-new
This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.5172
- Loss: 2.6032
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: 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: 15
Training results
Training Loss | Epoch | Step | Accuracy | Validation Loss |
---|---|---|---|---|
4.4963 | 0.46 | 200 | 0.2938 | nan |
4.0957 | 0.91 | 400 | 0.3098 | 3.5806 |
3.7431 | 1.37 | 600 | 0.3455 | nan |
3.6542 | 1.82 | 800 | 0.3075 | nan |
3.5585 | 2.28 | 1000 | 0.3546 | 3.4012 |
3.4027 | 2.73 | 1200 | 0.4049 | 3.2653 |
3.3416 | 3.19 | 1400 | 0.4053 | nan |
3.314 | 3.64 | 1600 | 0.4505 | nan |
3.2035 | 4.1 | 1800 | 0.4140 | 2.8518 |
3.1372 | 4.56 | 2000 | 0.4553 | 2.7572 |
3.0738 | 5.01 | 2200 | 0.4188 | 3.1020 |
3.0354 | 5.47 | 2400 | 0.4483 | 2.9353 |
3.0447 | 5.92 | 2600 | 0.4729 | 2.8608 |
2.6643 | 6.38 | 2800 | 0.4833 | 2.6200 |
2.8909 | 6.83 | 3000 | 0.4858 | 2.4677 |
2.9888 | 7.29 | 3200 | 0.4676 | 2.8088 |
2.8658 | 7.74 | 3400 | 0.5162 | 2.6409 |
2.7865 | 8.2 | 3600 | 0.5294 | nan |
2.8237 | 8.66 | 3800 | 0.4986 | nan |
2.7182 | 9.11 | 4000 | 0.5087 | nan |
2.7962 | 9.57 | 4200 | 0.5459 | nan |
2.5706 | 10.02 | 4400 | 0.4801 | nan |
2.528 | 10.48 | 4600 | 0.4893 | 2.2799 |
2.7482 | 10.93 | 4800 | 0.5227 | nan |
2.799 | 11.39 | 5000 | 0.4501 | nan |
2.471 | 11.85 | 5200 | 0.5323 | 2.4217 |
2.6071 | 12.3 | 5400 | 0.5420 | nan |
2.5139 | 12.76 | 5600 | 0.5511 | 2.1409 |
2.4214 | 13.21 | 5800 | 0.5215 | 2.4055 |
2.608 | 13.67 | 6000 | 0.5197 | 2.3034 |
2.5468 | 14.12 | 6200 | 0.5259 | nan |
2.4802 | 14.58 | 6400 | 0.5172 | 2.6032 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3