distilbert-base-uncased-finetuned-sst2
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: 0.2842
- Accuracy: 0.9071
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.02 | 100 | 0.3316 | 0.8624 |
| No log | 0.05 | 200 | 0.3357 | 0.8612 |
| No log | 0.07 | 300 | 0.3996 | 0.8383 |
| No log | 0.1 | 400 | 0.3012 | 0.8716 |
| 0.3421 | 0.12 | 500 | 0.3227 | 0.8693 |
| 0.3421 | 0.14 | 600 | 0.3643 | 0.8727 |
| 0.3421 | 0.17 | 700 | 0.2734 | 0.8853 |
| 0.3421 | 0.19 | 800 | 0.3077 | 0.8945 |
| 0.3421 | 0.21 | 900 | 0.2709 | 0.9002 |
| 0.2705 | 0.24 | 1000 | 0.2737 | 0.8899 |
| 0.2705 | 0.26 | 1100 | 0.3079 | 0.8979 |
| 0.2705 | 0.29 | 1200 | 0.2713 | 0.8968 |
| 0.2705 | 0.31 | 1300 | 0.2505 | 0.8933 |
| 0.2705 | 0.33 | 1400 | 0.2932 | 0.8922 |
| 0.239 | 0.36 | 1500 | 0.2842 | 0.9071 |
| 0.239 | 0.38 | 1600 | 0.2509 | 0.9014 |
| 0.239 | 0.4 | 1700 | 0.2819 | 0.8853 |
| 0.239 | 0.43 | 1800 | 0.2515 | 0.8956 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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Model tree for tillschwoerer/distilbert-base-uncased-finetuned-sst2
Base model
distilbert/distilbert-base-uncased