RoBERTa_Combined_Generated_v1.1_epoch_8
This model is a fine-tuned version of ICT2214Team7/RoBERTa_Test_Training on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0001
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0
- Report: {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 176}, 'micro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}}
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: 8
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Report |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 200 | 0.0063 | 0.9859 | 0.9899 | 0.9879 | 0.9984 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9615384615384616, 'recall': 0.9900990099009901, 'f1-score': 0.975609756097561, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 0.9885057471264368, 'recall': 0.9942196531791907, 'f1-score': 0.9913544668587896, 'support': 173}, 'PER': {'precision': 0.9943181818181818, 'recall': 0.9943181818181818, 'f1-score': 0.9943181818181818, 'support': 176}, 'micro avg': {'precision': 0.9858585858585859, 'recall': 0.9898580121703854, 'f1-score': 0.9878542510121457, 'support': 493}, 'macro avg': {'precision': 0.988872478096616, 'recall': 0.9766162578685614, 'f1-score': 0.9824605625875596, 'support': 493}, 'weighted avg': {'precision': 0.9860585778260815, 'recall': 0.9898580121703854, 'f1-score': 0.9878629175182675, 'support': 493}} |
No log | 2.0 | 400 | 0.0023 | 0.9959 | 0.9959 | 0.9959 | 0.9991 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9887640449438202, 'recall': 1.0, 'f1-score': 0.9943502824858756, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.997752808988764, 'recall': 0.9808888888888889, 'f1-score': 0.9890741381298283, 'support': 493}, 'weighted avg': {'precision': 0.9959887868359277, 'recall': 0.9959432048681541, 'f1-score': 0.995904989698977, 'support': 493}} |
0.0703 | 3.0 | 600 | 0.0007 | 0.9960 | 0.9980 | 0.9970 | 0.9998 | {'AGE': {'precision': 0.9444444444444444, 'recall': 0.9444444444444444, 'f1-score': 0.9444444444444444, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9943502824858758, 'recall': 1.0, 'f1-score': 0.9971671388101983, 'support': 176}, 'micro avg': {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1-score': 0.9969604863221885, 'support': 493}, 'macro avg': {'precision': 0.987758945386064, 'recall': 0.9888888888888889, 'f1-score': 0.9883223166509285, 'support': 493}, 'weighted avg': {'precision': 0.9959546647414079, 'recall': 0.9979716024340771, 'f1-score': 0.9969602767354866, 'support': 493}} |
0.0703 | 4.0 | 800 | 0.0013 | 0.9960 | 0.9980 | 0.9970 | 0.9998 | {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 0.9900990099009901, 'recall': 0.9900990099009901, 'f1-score': 0.9900990099009901, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9943502824858758, 'recall': 1.0, 'f1-score': 0.9971671388101983, 'support': 176}, 'micro avg': {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1-score': 0.9969604863221885, 'support': 493}, 'macro avg': {'precision': 0.9968898584773731, 'recall': 0.998019801980198, 'f1-score': 0.9974532297422376, 'support': 493}, 'weighted avg': {'precision': 0.9959546647414079, 'recall': 0.9979716024340771, 'f1-score': 0.9969602767354866, 'support': 493}} |
0.0022 | 5.0 | 1000 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 | {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 176}, 'micro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}} |
0.0022 | 6.0 | 1200 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 176}, 'micro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}} |
0.0022 | 7.0 | 1400 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 176}, 'micro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}} |
0.0008 | 8.0 | 1600 | 0.0001 | 1.0 | 1.0 | 1.0 | 1.0 | {'AGE': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 176}, 'micro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'macro avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}, 'weighted avg': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 493}} |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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