BERT_Mod_3
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.6760
- Accuracy: 0.8199
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5167 | 1.0 | 24544 | 0.4953 | 0.8077 |
| 0.414 | 2.0 | 49088 | 0.4802 | 0.8148 |
| 0.2933 | 3.0 | 73632 | 0.5783 | 0.8186 |
| 0.2236 | 4.0 | 98176 | 0.6760 | 0.8199 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
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