bert-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0618
- Precision: 0.9320
- Recall: 0.9504
- F1: 0.9411
- Accuracy: 0.9865
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: 8
- eval_batch_size: 8
- 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 |
---|---|---|---|---|---|---|---|
0.0765 | 1.0 | 1756 | 0.0730 | 0.8902 | 0.9295 | 0.9094 | 0.9799 |
0.0339 | 2.0 | 3512 | 0.0721 | 0.9264 | 0.9429 | 0.9346 | 0.9838 |
0.0209 | 3.0 | 5268 | 0.0618 | 0.9320 | 0.9504 | 0.9411 | 0.9865 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Finetuned from
Dataset used to train Govardhan-06/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.932
- Recall on conll2003validation set self-reported0.950
- F1 on conll2003validation set self-reported0.941
- Accuracy on conll2003validation set self-reported0.986