Classifier_with_external_sets_03
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6931
- Accuracy: 0.5034
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: 0.0002
- 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: 10
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 0.9983 | 289 | 0.6943 | 0.5034 |
0.7019 | 2.0 | 579 | 0.6932 | 0.4966 |
0.7019 | 2.9983 | 868 | 0.7004 | 0.5034 |
0.6978 | 4.0 | 1158 | 0.6968 | 0.4966 |
0.6978 | 4.9983 | 1447 | 0.6953 | 0.4966 |
0.6961 | 6.0 | 1737 | 0.6932 | 0.5034 |
0.6958 | 6.9983 | 2026 | 0.6932 | 0.5034 |
0.6958 | 8.0 | 2316 | 0.6934 | 0.4966 |
0.6942 | 8.9983 | 2605 | 0.6940 | 0.5034 |
0.6942 | 9.9827 | 2890 | 0.6931 | 0.5034 |
Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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