bert-base-uncased-conll2003
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1530
- Precision: 0.8885
- Recall: 0.9046
- F1: 0.8965
- Accuracy: 0.9781
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0651 | 1.0 | 3922 | 0.1483 | 0.8842 | 0.9067 | 0.8953 | 0.9775 |
| 0.0287 | 2.0 | 7844 | 0.1530 | 0.8885 | 0.9046 | 0.8965 | 0.9781 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.13.3
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Model tree for joshuaphua/bert-base-uncased-conll2003
Base model
google-bert/bert-base-uncasedDataset used to train joshuaphua/bert-base-uncased-conll2003
Evaluation results
- Precision on conll2003test set self-reported0.889
- Recall on conll2003test set self-reported0.905
- F1 on conll2003test set self-reported0.896
- Accuracy on conll2003test set self-reported0.978