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: 0.0633
- Precision: 0.9294
- Recall: 0.9456
- F1: 0.9374
- Accuracy: 0.9852
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0779 | 1.0 | 1756 | 0.0695 | 0.8938 | 0.9268 | 0.9100 | 0.9810 |
| 0.0334 | 2.0 | 3512 | 0.0633 | 0.9294 | 0.9456 | 0.9374 | 0.9852 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.0
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Model tree for SorrySalmon/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train SorrySalmon/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.929
- Recall on conll2003validation set self-reported0.946
- F1 on conll2003validation set self-reported0.937
- Accuracy on conll2003validation set self-reported0.985