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: 0.0648
- Precision: 0.9378
- Recall: 0.9512
- F1: 0.9444
- Accuracy: 0.9863
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 |
---|---|---|---|---|---|---|---|
0.0719 | 1.0 | 1756 | 0.0668 | 0.9014 | 0.9327 | 0.9168 | 0.9819 |
0.0356 | 2.0 | 3512 | 0.0632 | 0.9340 | 0.9504 | 0.9421 | 0.9855 |
0.0229 | 3.0 | 5268 | 0.0648 | 0.9378 | 0.9512 | 0.9444 | 0.9863 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Finetuned from
Dataset used to train akuzdeuov/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.938
- Recall on conll2003validation set self-reported0.951
- F1 on conll2003validation set self-reported0.944
- Accuracy on conll2003validation set self-reported0.986