RoBERTa_Combined_Generated_v1.1_epoch_5
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: 0.0009
- 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: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Report |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 200 | 0.0082 | 0.9799 | 0.9878 | 0.9838 | 0.9980 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9514563106796117, 'recall': 0.9702970297029703, 'f1-score': 0.9607843137254902, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 0.9719101123595506, 'recall': 1.0, 'f1-score': 0.9857549857549858, 'support': 173}, 'PER': {'precision': 1.0, 'recall': 0.9943181818181818, 'f1-score': 0.9971509971509972, 'support': 176}, 'micro avg': {'precision': 0.9798792756539235, 'recall': 0.9878296146044625, 'f1-score': 0.9838383838383837, 'support': 493}, 'macro avg': {'precision': 0.9846732846078325, 'recall': 0.9738119311931193, 'f1-score': 0.9789421409589476, 'support': 493}, 'weighted avg': {'precision': 0.9801978434418723, 'recall': 0.9878296146044625, 'f1-score': 0.9838720363581018, 'support': 493}} |
No log | 2.0 | 400 | 0.0021 | 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': 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}} |
0.0769 | 3.0 | 600 | 0.0038 | 0.9919 | 0.9919 | 0.9919 | 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': 0.9166666666666666, 'recall': 0.88, 'f1-score': 0.8979591836734694, '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': 0.9918864097363083, 'recall': 0.9918864097363083, 'f1-score': 0.9918864097363083, 'support': 493}, 'macro avg': {'precision': 0.9810861423220973, 'recall': 0.9648888888888889, 'f1-score': 0.9727476075175833, 'support': 493}, 'weighted avg': {'precision': 0.9917629585735882, 'recall': 0.9918864097363083, 'f1-score': 0.9917654028297467, 'support': 493}} |
0.0769 | 4.0 | 800 | 0.0012 | 0.9939 | 0.9959 | 0.9949 | 0.9996 | {'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': 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': 0.9939271255060729, 'recall': 0.9959432048681541, 'f1-score': 0.9949341438703141, 'support': 493}, 'macro avg': {'precision': 0.9866416978776529, 'recall': 0.9808888888888889, 'f1-score': 0.9836773127330029, 'support': 493}, 'weighted avg': {'precision': 0.9939603892700047, 'recall': 0.9959432048681541, 'f1-score': 0.9949197680241002, 'support': 493}} |
0.0036 | 5.0 | 1000 | 0.0009 | 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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