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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: 0.0879
- Precision: 0.9373
- Recall: 0.9537
- F1: 0.9454
- Accuracy: 0.9874
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: 6
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0216 | 1.0 | 1756 | 0.0975 | 0.9134 | 0.9357 | 0.9244 | 0.9815 |
0.0128 | 2.0 | 3512 | 0.0873 | 0.9296 | 0.9460 | 0.9377 | 0.9859 |
0.0097 | 3.0 | 5268 | 0.0821 | 0.9320 | 0.9498 | 0.9408 | 0.9859 |
0.0078 | 4.0 | 7024 | 0.0827 | 0.9350 | 0.9532 | 0.944 | 0.9870 |
0.0032 | 5.0 | 8780 | 0.0828 | 0.9301 | 0.9514 | 0.9406 | 0.9870 |
0.0017 | 6.0 | 10536 | 0.0879 | 0.9373 | 0.9537 | 0.9454 | 0.9874 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3