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: 0.0632
- Precision: 0.9382
- Recall: 0.9530
- F1: 0.9456
- Accuracy: 0.9863
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 |
---|---|---|---|---|---|---|---|
0.0778 | 1.0 | 1756 | 0.0629 | 0.9111 | 0.9362 | 0.9235 | 0.9830 |
0.0354 | 2.0 | 3512 | 0.0727 | 0.9332 | 0.9446 | 0.9389 | 0.9842 |
0.0229 | 3.0 | 5268 | 0.0632 | 0.9382 | 0.9530 | 0.9456 | 0.9863 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for onkar125/bert-finetuned-ner
Base model
google-bert/bert-base-casedDataset used to train onkar125/bert-finetuned-ner
Evaluation results
- Precision on conll2003validation set self-reported0.938
- Recall on conll2003validation set self-reported0.953
- F1 on conll2003validation set self-reported0.946
- Accuracy on conll2003validation set self-reported0.986