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hasoc19-microsoft-mdeberta-v3-base-sentiment-new
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3933
- Accuracy: 0.8800
- Precision: 0.8799
- Recall: 0.8800
- F1: 0.8799
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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4517 | 1.0 | 537 | 0.3339 | 0.8616 | 0.8614 | 0.8616 | 0.8614 |
| 0.3101 | 2.0 | 1074 | 0.3144 | 0.8763 | 0.8762 | 0.8763 | 0.8762 |
| 0.2612 | 3.0 | 1611 | 0.3505 | 0.8632 | 0.8651 | 0.8632 | 0.8619 |
| 0.2166 | 4.0 | 2148 | 0.3385 | 0.8747 | 0.8753 | 0.8747 | 0.8749 |
| 0.1939 | 5.0 | 2685 | 0.3794 | 0.8747 | 0.8746 | 0.8747 | 0.8746 |
| 0.1604 | 6.0 | 3222 | 0.3933 | 0.8800 | 0.8799 | 0.8800 | 0.8799 |
Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.6.1
- Tokenizers 0.13.1
