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bert-finetuned-am
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4128
- Precision: 0.0054
- Recall: 0.0166
- F1: 0.0082
- Accuracy: 0.8423
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 167 | 0.4448 | 0.0 | 0.0 | 0.0 | 0.8573 |
| No log | 2.0 | 334 | 0.4078 | 0.0009 | 0.0013 | 0.0011 | 0.8572 |
| 0.4231 | 3.0 | 501 | 0.4128 | 0.0054 | 0.0166 | 0.0082 | 0.8423 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
