Test_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: 0.0581
- Precision: 0.9340
- Recall: 0.9527
- F1: 0.9433
- Accuracy: 0.9868
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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0785 | 1.0 | 1756 | 0.0631 | 0.9074 | 0.9355 | 0.9213 | 0.9827 |
0.0373 | 2.0 | 3512 | 0.0637 | 0.9304 | 0.9475 | 0.9389 | 0.9857 |
0.0223 | 3.0 | 5268 | 0.0581 | 0.9340 | 0.9527 | 0.9433 | 0.9868 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train kennyTheo/Test_bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.934
- Recall on conll2003validation set self-reported0.953
- F1 on conll2003validation set self-reported0.943
- Accuracy on conll2003validation set self-reported0.987