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initial-dq-model
This model is a fine-tuned version of distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1677
- Precision: 0.7763
- Recall: 0.9380
- F1: 0.8495
- Accuracy: 0.9423
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2251 | 1.0 | 1220 | 0.1768 | 0.7481 | 0.9264 | 0.8277 | 0.9378 |
| 0.186 | 2.0 | 2440 | 0.1677 | 0.7763 | 0.9380 | 0.8495 | 0.9423 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.10.2+cu113
- Datasets 2.8.0
- Tokenizers 0.13.2
