BERT-NER-CoNLL
This model is a fine-tuned version of bert-large-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1243
- F1: 0.9106
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.115 | 1.0 | 878 | 0.1003 | 0.8983 |
| 0.0276 | 2.0 | 1756 | 0.1157 | 0.9081 |
| 0.0128 | 3.0 | 2634 | 0.1243 | 0.9106 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Sharpaxis/BERT-NER-CoNLL
Base model
google-bert/bert-large-uncasedDataset used to train Sharpaxis/BERT-NER-CoNLL
Evaluation results
- F1 on conll2003test set self-reported0.911