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bert-finetuned-ner
This model is a fine-tuned version of BSC-LT/roberta-base-bne-capitel-ner on the conll2002 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0911
- Precision: 0.8735
- Recall: 0.8851
- F1: 0.8793
- Accuracy: 0.9795
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 |
|---|---|---|---|---|---|---|---|
| 0.0981 | 1.0 | 521 | 0.0849 | 0.8612 | 0.8814 | 0.8712 | 0.9789 |
| 0.0327 | 2.0 | 1042 | 0.0833 | 0.8634 | 0.8814 | 0.8723 | 0.9796 |
| 0.0193 | 3.0 | 1563 | 0.0911 | 0.8735 | 0.8851 | 0.8793 | 0.9795 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
