distilbert-finetuned-ner
This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2474
- Precision: 0.8034
- Recall: 0.8171
- F1: 0.8102
- Accuracy: 0.9123
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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 1.1978 | 0.9581 | 20 | 0.9474 | 0.3903 | 0.2630 | 0.3143 | 0.6719 |
| 0.5288 | 1.9760 | 41 | 0.3356 | 0.7248 | 0.7698 | 0.7466 | 0.8912 |
| 0.2881 | 2.8982 | 60 | 0.2474 | 0.8034 | 0.8171 | 0.8102 | 0.9123 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Tokenizers 0.20.3