distilbert-base-uncased-finetuned-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: 0.7683
- Accuracy: 0.9190
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.2971 | 1.0 | 318 | 3.2827 | 0.7152 |
| 2.6163 | 2.0 | 636 | 1.8665 | 0.84 |
| 1.5389 | 3.0 | 954 | 1.1485 | 0.8981 |
| 1.0054 | 4.0 | 1272 | 0.8495 | 0.9135 |
| 0.7912 | 5.0 | 1590 | 0.7683 | 0.9190 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for chansick/distilbert-base-uncased-finetuned-clinc
Base model
distilbert/distilbert-base-uncasedDataset used to train chansick/distilbert-base-uncased-finetuned-clinc
Evaluation results
- Accuracy on clinc_oosvalidation set self-reported0.919