bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0642
- Precision: 0.9367
- Recall: 0.9517
- F1: 0.9442
- Accuracy: 0.9865
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0779 | 1.0 | 1756 | 0.0696 | 0.8995 | 0.9308 | 0.9149 | 0.9817 |
0.0345 | 2.0 | 3512 | 0.0702 | 0.9370 | 0.9458 | 0.9414 | 0.9850 |
0.022 | 3.0 | 5268 | 0.0642 | 0.9367 | 0.9517 | 0.9442 | 0.9865 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.2
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Base model
google-bert/bert-base-casedDataset used to train GustawB/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.937
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.987