BERT_test_graident_accumulation
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: 1.3780
- Accuracy: 0.6384
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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 188 | 0.9354 | 0.5987 |
| No log | 2.0 | 376 | 0.9827 | 0.6208 |
| 0.7728 | 3.0 | 564 | 1.1462 | 0.6298 |
| 0.7728 | 4.0 | 752 | 1.3019 | 0.6323 |
| 0.7728 | 5.0 | 940 | 1.3780 | 0.6384 |
Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for BrianHsu/BERT_test_graident_accumulation
Base model
google-bert/bert-base-chinese