distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository.
It achieves the following results on the evaluation set:
- Loss: 0.7773
- Accuracy: 0.9174
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 |
|---|---|---|---|---|
| 4.2923 | 1.0 | 318 | 3.2893 | 0.7423 |
| 2.6307 | 2.0 | 636 | 1.8837 | 0.8281 |
| 1.5483 | 3.0 | 954 | 1.1583 | 0.8968 |
| 1.0153 | 4.0 | 1272 | 0.8618 | 0.9094 |
| 0.7958 | 5.0 | 1590 | 0.7773 | 0.9174 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.0
- Tokenizers 0.10.3
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