grammar_error_detection
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Accuracy: 0.9819
- F1: 0.9196
- Loss: 0.0412
- Precision: 0.9278
- Recall: 0.9117
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.0427 | 2.0 | 151000 | 0.9806 | 0.9133 | 0.0436 | 0.9224 | 0.9043 |
| 0.0384 | 3.0 | 226500 | 0.9816 | 0.9174 | 0.0412 | 0.9275 | 0.9075 |
| 0.0344 | 4.0 | 302000 | 0.9819 | 0.9196 | 0.0412 | 0.9278 | 0.9117 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2