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Negation_and_Uncertainty_Scope_Detection_mBERT_fine_tuned
This model is a fine-tuned version of bert-base-multilingual-cased on the NUBES dataset. This is a result of the PhD dissertation of Antonio Tamayo. It achieves the following results on the evaluation set:
- Loss: 0.1954
- Neg Precision: 0.9056
- Neg Recall: 0.9332
- Neg F1: 0.9192
- Nsco Precision: 0.8973
- Nsco Recall: 0.9079
- Nsco F1: 0.9026
- Unc Precision: 0.864
- Unc Recall: 0.9019
- Unc F1: 0.8825
- Usco Precision: 0.8210
- Usco Recall: 0.8650
- Usco F1: 0.8424
- Precision: 0.8886
- Recall: 0.9127
- F1: 0.9005
- Accuracy: 0.9698
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: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Neg Precision | Neg Recall | Neg F1 | Nsco Precision | Nsco Recall | Nsco F1 | Unc Precision | Unc Recall | Unc F1 | Usco Precision | Usco Recall | Usco F1 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.1688 | 1.0 | 1726 | 0.1386 | 0.8885 | 0.9064 | 0.8974 | 0.8706 | 0.8697 | 0.8702 | 0.8326 | 0.8309 | 0.8318 | 0.7694 | 0.8304 | 0.7987 | 0.8621 | 0.8775 | 0.8697 | 0.9633 |
| 0.111 | 2.0 | 3452 | 0.1455 | 0.8803 | 0.9128 | 0.8962 | 0.8726 | 0.8924 | 0.8824 | 0.8244 | 0.8622 | 0.8429 | 0.7391 | 0.8125 | 0.7741 | 0.8547 | 0.8888 | 0.8714 | 0.9624 |
| 0.0678 | 3.0 | 5178 | 0.1621 | 0.9204 | 0.9004 | 0.9103 | 0.9051 | 0.8961 | 0.9006 | 0.8571 | 0.8643 | 0.8607 | 0.8454 | 0.7935 | 0.8187 | 0.9010 | 0.8828 | 0.8918 | 0.9679 |
| 0.043 | 4.0 | 6904 | 0.1590 | 0.9099 | 0.9212 | 0.9155 | 0.8885 | 0.9042 | 0.8963 | 0.8525 | 0.8810 | 0.8665 | 0.8288 | 0.8426 | 0.8356 | 0.8874 | 0.9020 | 0.8946 | 0.9696 |
| 0.0278 | 5.0 | 8630 | 0.1679 | 0.9140 | 0.9272 | 0.9206 | 0.8977 | 0.9057 | 0.9017 | 0.8656 | 0.8873 | 0.8763 | 0.8315 | 0.8594 | 0.8452 | 0.8939 | 0.9076 | 0.9007 | 0.9696 |
| 0.0153 | 6.0 | 10356 | 0.1908 | 0.8994 | 0.9359 | 0.9173 | 0.8888 | 0.9101 | 0.8993 | 0.8676 | 0.8894 | 0.8784 | 0.8122 | 0.8638 | 0.8372 | 0.8819 | 0.9137 | 0.8975 | 0.9688 |
| 0.0082 | 7.0 | 12082 | 0.1954 | 0.9056 | 0.9332 | 0.9192 | 0.8973 | 0.9079 | 0.9026 | 0.864 | 0.9019 | 0.8825 | 0.8210 | 0.8650 | 0.8424 | 0.8886 | 0.9127 | 0.9005 | 0.9698 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
