outputs
This model is a fine-tuned version of microsoft/deberta-v3-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0926
- Accuracy: 0.8780
- F1: 0.3881
- Precision: 0.5417
- Recall: 0.3023
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: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
No log | 1.0 | 6 | 0.0874 | 0.8810 | 0.4118 | 0.56 | 0.3256 |
No log | 2.0 | 12 | 0.0936 | 0.8839 | 0.4000 | 0.5909 | 0.3023 |
No log | 3.0 | 18 | 0.0922 | 0.8780 | 0.3881 | 0.5417 | 0.3023 |
No log | 4.0 | 24 | 0.0926 | 0.8780 | 0.3881 | 0.5417 | 0.3023 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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