BERT-ner
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: 0.0664
- Precision: 0.9449
- Recall: 0.9516
- F1: 0.9482
- Accuracy: 0.9872
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0252 | 1.0 | 878 | 0.0652 | 0.9414 | 0.9419 | 0.9417 | 0.9854 |
| 0.0121 | 2.0 | 1756 | 0.0615 | 0.9407 | 0.9498 | 0.9452 | 0.9867 |
| 0.0079 | 3.0 | 2634 | 0.0664 | 0.9449 | 0.9516 | 0.9482 | 0.9872 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Base model
google-bert/bert-base-uncasedDataset used to train Sadashiv/BERT-ner
Space using Sadashiv/BERT-ner 1
Evaluation results
- Precision on conll2003validation set self-reported0.945
- Recall on conll2003validation set self-reported0.952
- F1 on conll2003validation set self-reported0.948
- Accuracy on conll2003validation set self-reported0.987