RoBERTa_Combined_Generated_v1.1_epoch_7
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: 0.0004
- Precision: 0.9960
- Recall: 0.9980
- F1: 0.9970
- Accuracy: 0.9998
- Report: {'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}}
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 | Report |
---|---|---|---|---|---|---|---|---|
No log | 1.0 | 200 | 0.0077 | 0.9738 | 0.9797 | 0.9767 | 0.9975 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9603960396039604, 'recall': 0.9603960396039604, 'f1-score': 0.9603960396039604, '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': 0.9720670391061452, 'recall': 0.9886363636363636, 'f1-score': 0.9802816901408451, 'support': 176}, 'micro avg': {'precision': 0.9737903225806451, 'recall': 0.9797160243407708, 'f1-score': 0.9767441860465116, 'support': 493}, 'macro avg': {'precision': 0.9819211871705924, 'recall': 0.96838323080863, 'f1-score': 0.9748913517195452, 'support': 493}, 'weighted avg': {'precision': 0.9738935358385311, 'recall': 0.9797160243407708, 'f1-score': 0.9767187201788655, 'support': 493}} |
No log | 2.0 | 400 | 0.0021 | 0.9939 | 0.9959 | 0.9949 | 0.9995 | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9901960784313726, 'recall': 1.0, 'f1-score': 0.9950738916256158, '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': 0.9957920246750385, 'recall': 0.9808888888888889, 'f1-score': 0.9880889164549513, 'support': 493}, 'weighted avg': {'precision': 0.993980275520651, 'recall': 0.9959432048681541, 'f1-score': 0.9948957869691336, 'support': 493}} |
0.0688 | 3.0 | 600 | 0.0013 | 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}} |
0.0688 | 4.0 | 800 | 0.0005 | 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}} |
0.0028 | 5.0 | 1000 | 0.0006 | 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}} |
0.0028 | 6.0 | 1200 | 0.0005 | 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}} |
0.0028 | 7.0 | 1400 | 0.0004 | 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}} |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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