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cudaTest
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6334
- Compute Metrics: :
- Accuracy: 0.676
- Balanced Accuracy: 0.4893
- F1 Score: 0.8058
- Recall: 0.9655
- Precision: 0.6914
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: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Compute Metrics | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision |
|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 2 | 0.6369 | : | 0.688 | 0.5017 | 0.8134 | 0.9770 | 0.6967 |
| No log | 2.0 | 4 | 0.6302 | : | 0.684 | 0.5043 | 0.8092 | 0.9626 | 0.6979 |
| No log | 3.0 | 6 | 0.6313 | : | 0.69 | 0.4975 | 0.8161 | 0.9885 | 0.6949 |
| No log | 4.0 | 8 | 0.6338 | : | 0.668 | 0.4854 | 0.7995 | 0.9511 | 0.6896 |
| 0.6818 | 5.0 | 10 | 0.6334 | : | 0.676 | 0.4893 | 0.8058 | 0.9655 | 0.6914 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
