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: 0.0584
- Precision: 0.9263
- Recall: 0.9455
- F1: 0.9358
- Accuracy: 0.9856
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.2283 | 1.0 | 878 | 0.0684 | 0.8963 | 0.9320 | 0.9138 | 0.9805 |
| 0.0454 | 2.0 | 1756 | 0.0634 | 0.9243 | 0.9418 | 0.9330 | 0.9844 |
| 0.024 | 3.0 | 2634 | 0.0584 | 0.9263 | 0.9455 | 0.9358 | 0.9856 |
Framework versions
- Transformers 4.42.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for gigauser/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train gigauser/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.926
- Recall on conll2003validation set self-reported0.945
- F1 on conll2003validation set self-reported0.936
- Accuracy on conll2003validation set self-reported0.986