mmodel_v2
This model is a fine-tuned version of ai-forever/ruBert-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0613
- Accuracy: 0.7115
- F1: 0.7076
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
1.631 | 1.0 | 693 | 1.2443 | 0.6671 | 0.6599 |
1.1397 | 2.0 | 1386 | 1.0194 | 0.7142 | 0.7069 |
0.8178 | 3.0 | 2079 | 0.9897 | 0.7126 | 0.7057 |
0.667 | 4.0 | 2772 | 1.0223 | 0.7099 | 0.7049 |
0.5712 | 5.0 | 3465 | 1.0613 | 0.7115 | 0.7076 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
ai-forever/ruBert-base