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: 0.0076
- Precision: 0.9219
- Recall: 0.8872
- F1: 0.9042
- Accuracy: 0.9971
- Report: {'PER': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'micro avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'macro avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'weighted avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}}
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 | 0.0087 | 0.8562 | 0.9398 | 0.8961 | 0.9967 | {'PER': {'precision': 0.8561643835616438, 'recall': 0.9398496240601504, 'f1-score': 0.8960573476702508, 'support': 133}, 'micro avg': {'precision': 0.8561643835616438, 'recall': 0.9398496240601504, 'f1-score': 0.8960573476702508, 'support': 133}, 'macro avg': {'precision': 0.8561643835616438, 'recall': 0.9398496240601504, 'f1-score': 0.8960573476702508, 'support': 133}, 'weighted avg': {'precision': 0.8561643835616438, 'recall': 0.9398496240601504, 'f1-score': 0.8960573476702508, 'support': 133}} |
| No log | 2.0 | 400 | 0.0072 | 0.9111 | 0.9248 | 0.9179 | 0.9973 | {'PER': {'precision': 0.9111111111111111, 'recall': 0.924812030075188, 'f1-score': 0.917910447761194, 'support': 133}, 'micro avg': {'precision': 0.9111111111111111, 'recall': 0.924812030075188, 'f1-score': 0.917910447761194, 'support': 133}, 'macro avg': {'precision': 0.9111111111111111, 'recall': 0.924812030075188, 'f1-score': 0.917910447761194, 'support': 133}, 'weighted avg': {'precision': 0.9111111111111111, 'recall': 0.924812030075188, 'f1-score': 0.917910447761194, 'support': 133}} |
| 0.0267 | 3.0 | 600 | 0.0076 | 0.9219 | 0.8872 | 0.9042 | 0.9971 | {'PER': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'micro avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'macro avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}, 'weighted avg': {'precision': 0.921875, 'recall': 0.8872180451127819, 'f1-score': 0.9042145593869733, 'support': 133}} |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for ICT2214Team7/RoBERTa_Combined_Generated_v1.1_epoch_8
Base model
distilbert/distilroberta-base
Finetuned
ICT2214Team7/RoBERTa_Test_Training