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just-nce
This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0338
- Able: {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6}
- Eading: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Ext: {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10}
- Mage: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4}
- Ub heading: {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16}
- Overall Precision: 0.6571
- Overall Recall: 0.575
- Overall F1: 0.6133
- Overall Accuracy: 0.68
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
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Able | Eading | Ext | Mage | Ub heading | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.4724 | 14.29 | 100 | 1.0338 | {'precision': 0.4, 'recall': 0.6666666666666666, 'f1': 0.5, 'number': 6} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.75, 'recall': 0.9, 'f1': 0.8181818181818182, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 4} | {'precision': 0.9090909090909091, 'recall': 0.625, 'f1': 0.7407407407407406, 'number': 16} | 0.6571 | 0.575 | 0.6133 | 0.68 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.2
