Negation_Scope_Detection_NubEs_Training_Development_mBERT_fine_tuned
This model is a fine-tuned version of bert-base-multilingual-cased on NubEs (Training + Development) dataset. It achieves the following results on the evaluation set:
- Loss: 0.1712
- Precision: 0.8970
- Recall: 0.9177
- F1: 0.9072
- Accuracy: 0.9722
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 |
|---|---|---|---|---|---|---|---|
| 0.1805 | 1.0 | 1726 | 0.1542 | 0.8408 | 0.8600 | 0.8503 | 0.9591 |
| 0.126 | 2.0 | 3452 | 0.1271 | 0.8652 | 0.8774 | 0.8713 | 0.9640 |
| 0.0814 | 3.0 | 5178 | 0.1395 | 0.8715 | 0.8881 | 0.8797 | 0.9662 |
| 0.0447 | 4.0 | 6904 | 0.1326 | 0.8893 | 0.9068 | 0.8979 | 0.9696 |
| 0.0247 | 5.0 | 8630 | 0.1805 | 0.9012 | 0.9020 | 0.9016 | 0.9708 |
| 0.0156 | 6.0 | 10356 | 0.1524 | 0.8972 | 0.9120 | 0.9045 | 0.9706 |
| 0.0056 | 7.0 | 12082 | 0.1712 | 0.8970 | 0.9177 | 0.9072 | 0.9722 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2