<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
BERT_CSIC-Anomaly
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: 0.0098
- Accuracy: 0.998
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 |
---|---|---|---|---|
0.1107 | 1.0 | 1094 | 0.0456 | 0.9898 |
0.0284 | 2.0 | 2188 | 0.0121 | 0.9968 |
0.0173 | 3.0 | 3282 | 0.0175 | 0.9964 |
0.0142 | 4.0 | 4376 | 0.0135 | 0.9974 |
0.0115 | 5.0 | 5470 | 0.0110 | 0.9978 |
0.0088 | 6.0 | 6564 | 0.0092 | 0.9984 |
0.0064 | 7.0 | 7658 | 0.0101 | 0.998 |
0.0056 | 8.0 | 8752 | 0.0108 | 0.9984 |
0.0034 | 9.0 | 9846 | 0.0128 | 0.9978 |
0.0031 | 10.0 | 10940 | 0.0126 | 0.998 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3