NLP-CIC-WFU_Clinical_Cases_NER_Sents_Tokenized_bertin_roberta_base_spanish_fine_tuned
This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0973
- Precision: 0.9012
- Recall: 0.6942
- F1: 0.7842
- Accuracy: 0.9857
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: 5e-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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0605 | 1.0 | 2568 | 0.0625 | 0.9400 | 0.6322 | 0.7560 | 0.9836 |
| 0.0475 | 2.0 | 5136 | 0.0622 | 0.9533 | 0.6572 | 0.7781 | 0.9849 |
| 0.0374 | 3.0 | 7704 | 0.0552 | 0.9261 | 0.6784 | 0.7831 | 0.9855 |
| 0.0246 | 4.0 | 10272 | 0.0693 | 0.9381 | 0.6658 | 0.7788 | 0.9849 |
| 0.0126 | 5.0 | 12840 | 0.0974 | 0.8918 | 0.6830 | 0.7735 | 0.9849 |
| 0.0061 | 6.0 | 15408 | 0.0886 | 0.8771 | 0.7099 | 0.7847 | 0.9850 |
| 0.0031 | 7.0 | 17976 | 0.0973 | 0.9012 | 0.6942 | 0.7842 | 0.9857 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1