RoBERTa_Combined_Generated_v1.1
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: 0.0017
- Precision: 0.9980
- Recall: 0.9980
- F1: 0.9980
- Accuracy: 0.9996
- Report: {'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': 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': 0.9979716024340771, 'recall': 0.9979716024340771, 'f1-score': 0.9979716024340771, 'support': 493}, 'macro avg': {'precision': 0.9988700564971751, 'recall': 0.9888888888888889, 'f1-score': 0.9937191420477539, 'support': 493}, 'weighted avg': {'precision': 0.9979830623073309, 'recall': 0.9979716024340771, 'f1-score': 0.9979454984103634, '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: 3
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Report |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 200 | 0.0080 | 0.972 | 0.9858 | 0.9789 | 0.9980 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9339622641509434, 'recall': 0.9801980198019802, 'f1-score': 0.9565217391304348, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 0.9771428571428571, 'recall': 0.9884393063583815, 'f1-score': 0.9827586206896551, 'support': 173}, 'PER': {'precision': 0.9831460674157303, 'recall': 0.9943181818181818, 'f1-score': 0.9887005649717514, 'support': 176}, 'micro avg': {'precision': 0.972, 'recall': 0.9858012170385395, 'f1-score': 0.9788519637462235, 'support': 493}, 'macro avg': {'precision': 0.9788502377419063, 'recall': 0.9734799904845977, 'f1-score': 0.9758002665910214, 'support': 493}, 'weighted avg': {'precision': 0.9724332876878866, 'recall': 0.9858012170385395, 'f1-score': 0.9789305206300083, 'support': 493}} |
No log | 2.0 | 400 | 0.0029 | 0.9959 | 0.9959 | 0.9959 | 0.9993 | {'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': 0.9942528735632183, 'recall': 1.0, 'f1-score': 0.9971181556195965, 'support': 173}, 'PER': {'precision': 0.9943502824858758, 'recall': 1.0, 'f1-score': 0.9971671388101983, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.9977206312098188, 'recall': 0.9808888888888889, 'f1-score': 0.989061140518612, 'support': 493}, 'weighted avg': {'precision': 0.9959663221986833, 'recall': 0.9959432048681541, 'f1-score': 0.9958993256731576, 'support': 493}} |
0.0711 | 3.0 | 600 | 0.0017 | 0.9980 | 0.9980 | 0.9980 | 0.9996 | {'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': 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': 0.9979716024340771, 'recall': 0.9979716024340771, 'f1-score': 0.9979716024340771, 'support': 493}, 'macro avg': {'precision': 0.9988700564971751, 'recall': 0.9888888888888889, 'f1-score': 0.9937191420477539, 'support': 493}, 'weighted avg': {'precision': 0.9979830623073309, 'recall': 0.9979716024340771, 'f1-score': 0.9979454984103634, '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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