RnD-base-tokenizer-mDeBERTa-v3-kor-further
This model is a fine-tuned version of lighthouse/mdeberta-v3-base-kor-further on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9091
- Accuracy: 0.6802
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1325 | 0.9586 | 0.6591 |
| No log | 2.0 | 2650 | 0.9178 | 0.6735 |
| No log | 3.0 | 3975 | 0.9091 | 0.6802 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2