SecureBERT-finetuned-ner
This model is a fine-tuned version of ehsanaghaei/SecureBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4660
- Precision: 0.8270
- Recall: 0.8429
- F1: 0.8349
- Accuracy: 0.9309
Model description
More information needed
Intended uses & limitations
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Training and evaluation data
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Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.1046 | 1.0 | 2395 | 0.3706 | 0.8023 | 0.8288 | 0.8153 | 0.9250 |
| 0.0707 | 2.0 | 4790 | 0.3917 | 0.8138 | 0.8415 | 0.8274 | 0.9297 |
| 0.0551 | 3.0 | 7185 | 0.4256 | 0.8220 | 0.8374 | 0.8296 | 0.9293 |
| 0.0371 | 4.0 | 9580 | 0.4476 | 0.8293 | 0.8406 | 0.8349 | 0.9306 |
| 0.0265 | 5.0 | 11975 | 0.4660 | 0.8270 | 0.8429 | 0.8349 | 0.9309 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
ehsanaghaei/SecureBERT