testing
This model is a fine-tuned version of bert-base-uncased on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set:
- Loss: 1.7135
- Accuracy: 0.6740
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.068 | 0.0 | 5 | 1.7758 | 0.6650 |
| 1.9159 | 0.0 | 10 | 1.7192 | 0.6736 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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Base model
google-bert/bert-base-uncasedEvaluation results
- Accuracy on datasets/all_binary_and_xe_ey_fae_counterfactualself-reported0.674