distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
- Loss: 0.1259
- Accuracy: 0.9332
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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 318 | 0.5952 | 0.7355 |
| 0.7663 | 2.0 | 636 | 0.3130 | 0.8742 |
| 0.7663 | 3.0 | 954 | 0.2024 | 0.9206 |
| 0.3043 | 4.0 | 1272 | 0.1590 | 0.9235 |
| 0.181 | 5.0 | 1590 | 0.1378 | 0.9303 |
| 0.181 | 6.0 | 1908 | 0.1287 | 0.9329 |
| 0.1468 | 7.0 | 2226 | 0.1259 | 0.9332 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
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Dataset used to train Omar95farag/distilbert-base-uncased-distilled-clinc
Evaluation results
- Accuracy on clinc_oosself-reported0.933
- Accuracy on clinc_oostest set self-reported0.859
- Precision Macro on clinc_oostest set self-reported0.862
- Precision Micro on clinc_oostest set self-reported0.859
- Precision Weighted on clinc_oostest set self-reported0.880
- Recall Macro on clinc_oostest set self-reported0.936
- Recall Micro on clinc_oostest set self-reported0.859
- Recall Weighted on clinc_oostest set self-reported0.859
- F1 Macro on clinc_oostest set self-reported0.892
- F1 Micro on clinc_oostest set self-reported0.859