klue_bert_base
This model is a fine-tuned version of klue/bert-base on the nsmc dataset. It achieves the following results on the evaluation set:
- Loss: 0.2415
- Accuracy: 0.9056
- F1: 0.9056
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2742 | 1.0 | 2344 | 0.2381 | 0.9005 | 0.9005 |
| 0.1865 | 2.0 | 4688 | 0.2415 | 0.9056 | 0.9056 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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Dataset used to train Woonn/klue_bert_base
Evaluation results
- Accuracy on nsmctest set self-reported0.906
- F1 on nsmctest set self-reported0.906