BERT_CSIC-Anomaly_Baseline
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6550
- Accuracy: 0.6116
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6745 | 1.0 | 1094 | 0.6729 | 0.5998 |
| 0.6705 | 2.0 | 2188 | 0.6624 | 0.606 |
| 0.6681 | 3.0 | 3282 | 0.6636 | 0.6056 |
| 0.6675 | 4.0 | 4376 | 0.6596 | 0.608 |
| 0.6647 | 5.0 | 5470 | 0.6586 | 0.6082 |
| 0.6647 | 6.0 | 6564 | 0.6587 | 0.608 |
| 0.6643 | 7.0 | 7658 | 0.6552 | 0.6124 |
| 0.6636 | 8.0 | 8752 | 0.6582 | 0.607 |
| 0.6624 | 9.0 | 9846 | 0.6560 | 0.6098 |
| 0.6621 | 10.0 | 10940 | 0.6560 | 0.6098 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3