bert-finetuned-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: 0.0532
- Precision: 0.9271
- Recall: 0.9463
- F1: 0.9366
- Accuracy: 0.9869
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 |
|---|---|---|---|---|---|---|---|
| 0.2141 | 1.0 | 878 | 0.0595 | 0.9003 | 0.9305 | 0.9152 | 0.9836 |
| 0.0449 | 2.0 | 1756 | 0.0529 | 0.9236 | 0.9455 | 0.9344 | 0.9861 |
| 0.0243 | 3.0 | 2634 | 0.0532 | 0.9271 | 0.9463 | 0.9366 | 0.9869 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for upendrawappgo/bert-finetuned-ner
Base model
google-bert/bert-base-uncasedDataset used to train upendrawappgo/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.927
- Recall on conll2003validation set self-reported0.946
- F1 on conll2003validation set self-reported0.937
- Accuracy on conll2003validation set self-reported0.987