NLP-CIC-WFU_Clinical_Cases_NER_mBERT_cased_fine_tuned
This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0501
- Precision: 0.8961
- Recall: 0.7009
- F1: 0.7865
- Accuracy: 0.9898
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 94 | 0.0484 | 0.9002 | 0.6340 | 0.7440 | 0.9876 |
| No log | 2.0 | 188 | 0.0436 | 0.9095 | 0.6599 | 0.7649 | 0.9887 |
| No log | 3.0 | 282 | 0.0462 | 0.8545 | 0.7043 | 0.7722 | 0.9883 |
| No log | 4.0 | 376 | 0.0456 | 0.9058 | 0.6761 | 0.7743 | 0.9894 |
| No log | 5.0 | 470 | 0.0447 | 0.9194 | 0.6836 | 0.7841 | 0.9900 |
| 0.0426 | 6.0 | 564 | 0.0480 | 0.8917 | 0.7026 | 0.7859 | 0.9897 |
| 0.0426 | 7.0 | 658 | 0.0501 | 0.8961 | 0.7009 | 0.7865 | 0.9898 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1