tun-en-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: 0.2051
- Precision: 0.7569
- Recall: 0.8176
- F1: 0.7861
- Accuracy: 0.9381
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 |
---|---|---|---|---|---|---|---|
0.2205 | 1.0 | 2078 | 0.2123 | 0.7143 | 0.8032 | 0.7561 | 0.9295 |
0.1718 | 2.0 | 4156 | 0.1922 | 0.7461 | 0.8174 | 0.7801 | 0.9383 |
0.1322 | 3.0 | 6234 | 0.2051 | 0.7569 | 0.8176 | 0.7861 | 0.9381 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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