roberta-base-bne-capitel-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: 0.1137
- Precision: 0.8638
- Recall: 0.8814
- F1: 0.8725
- Accuracy: 0.9780
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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
0.0041 | 1.0 | 1041 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 |
0.004 | 2.0 | 2082 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 |
0.0039 | 3.0 | 3123 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 |
0.003 | 4.0 | 4164 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 |
0.0032 | 5.0 | 5205 | 0.1137 | 0.8638 | 0.8814 | 0.8725 | 0.9780 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Dataset used to train raulgdp/roberta-base-bne-capitel-ner
Evaluation results
- Precision on conll2002validation set self-reported0.864
- Recall on conll2002validation set self-reported0.881
- F1 on conll2002validation set self-reported0.873
- Accuracy on conll2002validation set self-reported0.978