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bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos
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: 0.7724
- Accuracy: 0.9158
Model Training Details
| Parameter | Value |
|---|---|
| Task | text-classification |
| Teacher Model | bert-base-uncased-finetuned-clinc_oos |
| Student Model | distilbert-base-uncased |
| Dataset Name | clinc_oos |
| Dataset Config | plus |
| Evaluation Dataset | validation |
| Batch Size | 48 |
| Number of Epochs | 5 |
| Learning Rate | 0.00002 |
| Alpha* | 1 |
| *alpha: (Total_loss = alpha * Loss_CE + (1-alpha) * Loss_KD) |
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 |
|---|---|---|---|---|
| No log | 1.0 | 318 | 3.2762 | 0.7284 |
| 3.7824 | 2.0 | 636 | 1.8624 | 0.8358 |
| 3.7824 | 3.0 | 954 | 1.1512 | 0.8984 |
| 1.6858 | 4.0 | 1272 | 0.8540 | 0.9132 |
| 0.8983 | 5.0 | 1590 | 0.7724 | 0.9158 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
