yes-no-model
This model is a fine-tuned version of bert-base-chinese on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1002
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7142 | 1.0 | 9 | 0.6571 |
| 0.4763 | 2.0 | 18 | 0.3666 |
| 0.2234 | 3.0 | 27 | 0.2150 |
| 0.1026 | 4.0 | 36 | 0.1338 |
| 0.0537 | 5.0 | 45 | 0.1043 |
| 0.0231 | 6.0 | 54 | 0.0905 |
| 0.0083 | 7.0 | 63 | 0.0916 |
| 0.0057 | 8.0 | 72 | 0.0968 |
| 0.0048 | 9.0 | 81 | 0.0994 |
| 0.0041 | 10.0 | 90 | 0.1002 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for ZON8955/yesno_1
Base model
google-bert/bert-base-chinese