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distilbert-base-uncased__sst2__train-16-7
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6952
- Accuracy: 0.5025
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6949 | 1.0 | 7 | 0.7252 | 0.2857 |
| 0.6678 | 2.0 | 14 | 0.7550 | 0.2857 |
| 0.6299 | 3.0 | 21 | 0.8004 | 0.2857 |
| 0.5596 | 4.0 | 28 | 0.8508 | 0.2857 |
| 0.5667 | 5.0 | 35 | 0.8464 | 0.2857 |
| 0.367 | 6.0 | 42 | 0.8515 | 0.2857 |
| 0.2706 | 7.0 | 49 | 0.9574 | 0.2857 |
| 0.2163 | 8.0 | 56 | 0.9710 | 0.4286 |
| 0.1024 | 9.0 | 63 | 1.1607 | 0.1429 |
| 0.1046 | 10.0 | 70 | 1.3779 | 0.1429 |
| 0.0483 | 11.0 | 77 | 1.4876 | 0.1429 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
