bert
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5316
- Accuracy: 0.2936
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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5355 | 1.0 | 6195 | 1.5339 | 0.2923 |
| 1.5248 | 2.0 | 12390 | 1.5316 | 0.2936 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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