bert-large-uncased-for-ner
This model is a fine-tuned version of google-bert/bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0371
- F1: 0.9508
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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- 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 | F1 |
|---|---|---|---|---|
| 0.1141 | 1.0 | 586 | 0.0443 | 0.9336 |
| 0.0267 | 2.0 | 1172 | 0.0382 | 0.9458 |
| 0.0108 | 3.0 | 1758 | 0.0371 | 0.9508 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.19.1
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Base model
google-bert/bert-large-uncasedDataset used to train apak/bert-large-uncased-for-ner
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Evaluation results
- F1 on conll2003validation set self-reported0.951