bert-base-cased
This model is a fine-tuned version of dslim/distilbert-NER on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0367
- Precision: 0.9569
- Recall: 0.9677
- F1: 0.9623
- Accuracy: 0.9915
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0315 | 1.0 | 1500 | 0.0367 | 0.9569 | 0.9677 | 0.9623 | 0.9915 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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