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baseline_bert-large-cased_epoch3_batch4_lr2e-05_w0.01
This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5408
- Accuracy: 0.8932
- F1: 0.8532
- Precision: 0.8746
- Recall: 0.8328
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.5768 | 1.0 | 788 | 0.5440 | 0.8487 | 0.7908 | 0.8159 | 0.7672 |
0.3931 | 2.0 | 1576 | 0.4749 | 0.8921 | 0.8537 | 0.8628 | 0.8448 |
0.2643 | 3.0 | 2364 | 0.5408 | 0.8932 | 0.8532 | 0.8746 | 0.8328 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3