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: 0.3238
- Accuracy: 0.9439
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: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.0341 | 1.0 | 318 | 3.0230 | 0.7245 |
| 2.3266 | 2.0 | 636 | 1.5597 | 0.8494 |
| 1.1958 | 3.0 | 954 | 0.8316 | 0.9142 |
| 0.6396 | 4.0 | 1272 | 0.5287 | 0.9332 |
| 0.3865 | 5.0 | 1590 | 0.4087 | 0.9365 |
| 0.2679 | 6.0 | 1908 | 0.3519 | 0.9406 |
| 0.2118 | 7.0 | 2226 | 0.3313 | 0.9416 |
| 0.1874 | 8.0 | 2544 | 0.3238 | 0.9439 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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Model tree for gouse-73/distilbert-base-uncased-distilled-clinc
Base model
distilbert/distilbert-base-uncasedDataset used to train gouse-73/distilbert-base-uncased-distilled-clinc
Evaluation results
- Accuracy on clinc_oosvalidation set self-reported0.944