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bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4233
- Precision: 0.8462
- Recall: 0.8462
- F1: 0.8462
- Accuracy: 0.9340
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 4 | 0.7926 | 0.6875 | 0.8462 | 0.7586 | 0.9137 |
| No log | 2.0 | 8 | 0.5065 | 0.8462 | 0.8462 | 0.8462 | 0.9289 |
| No log | 3.0 | 12 | 0.4233 | 0.8462 | 0.8462 | 0.8462 | 0.9340 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
