bert-base-cased-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the lener_br dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Precision: 0.7641
- Recall: 0.8182
- F1: 0.7902
- Accuracy: 0.9615
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: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 432 | nan | 0.6807 | 0.7773 | 0.7258 | 0.9450 |
0.3019 | 2.0 | 864 | nan | 0.7244 | 0.7725 | 0.7476 | 0.9531 |
0.0871 | 3.0 | 1296 | nan | 0.7352 | 0.8192 | 0.7749 | 0.9571 |
0.0527 | 4.0 | 1728 | nan | 0.7455 | 0.7864 | 0.7654 | 0.9557 |
0.031 | 5.0 | 2160 | nan | 0.7334 | 0.7976 | 0.7642 | 0.9544 |
0.0223 | 6.0 | 2592 | nan | 0.7703 | 0.8343 | 0.8010 | 0.9624 |
0.0171 | 7.0 | 3024 | nan | 0.7279 | 0.8119 | 0.7676 | 0.9569 |
0.0171 | 8.0 | 3456 | nan | 0.7609 | 0.8067 | 0.7831 | 0.9613 |
0.012 | 9.0 | 3888 | nan | 0.7585 | 0.8152 | 0.7858 | 0.9608 |
0.0097 | 10.0 | 4320 | nan | 0.7641 | 0.8182 | 0.7902 | 0.9615 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Finetuned from
Dataset used to train GuiTap/bert-base-cased-finetuned-ner
Evaluation results
- Precision on lener_brvalidation set self-reported0.764
- Recall on lener_brvalidation set self-reported0.818
- F1 on lener_brvalidation set self-reported0.790
- Accuracy on lener_brvalidation set self-reported0.962