bert-finetuned-ner-1
This model is a fine-tuned version of bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2526
- Precision: 0.7837
- Recall: 0.8141
- F1: 0.7986
- Accuracy: 0.9687
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.003 | 1.0 | 1041 | 0.2526 | 0.7837 | 0.8141 | 0.7986 | 0.9687 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Willilamvel/bert-finetuned-ner-1
Base model
google-bert/bert-base-casedDataset used to train Willilamvel/bert-finetuned-ner-1
Evaluation results
- Precision on conll2002validation set self-reported0.784
- Recall on conll2002validation set self-reported0.814
- F1 on conll2002validation set self-reported0.799
- Accuracy on conll2002validation set self-reported0.969