bert-clf-crossencoder-cross_entropy
This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0410
- Accuracy: 0.6019
- Precision: 0.6044
- Recall: 0.6019
- F1: 0.6029
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
- lr_scheduler_warmup_steps: 100
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 1.2489 | 1.0 | 78 | 1.2061 | 0.4790 | 0.3740 | 0.4790 | 0.3999 |
| 1.0356 | 2.0 | 156 | 1.0236 | 0.6019 | 0.6244 | 0.6019 | 0.5841 |
| 0.8625 | 3.0 | 234 | 0.9983 | 0.6181 | 0.6274 | 0.6181 | 0.6126 |
| 0.7101 | 4.0 | 312 | 0.9687 | 0.6019 | 0.6004 | 0.6019 | 0.5998 |
| 0.5945 | 5.0 | 390 | 0.9962 | 0.6181 | 0.6178 | 0.6181 | 0.6157 |
| 0.4753 | 6.0 | 468 | 1.0245 | 0.6246 | 0.6337 | 0.6246 | 0.6256 |
| 0.3903 | 7.0 | 546 | 1.0410 | 0.6019 | 0.6044 | 0.6019 | 0.6029 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0