lilt-ruroberta

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4919
• Comment: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}
• Date: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
• Labname: {'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}
• Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
• Measure: {'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}
• Ref Value: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}
• Result: {'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}
• Overall Precision: 0.4528
• Overall Recall: 0.4
• Overall F1: 0.4248
• Overall Accuracy: 0.8698

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: 25
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Comment	Date	Labname	Laboratory	Measure	Ref Value	Result	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
2.4398	5.0	5	1.5928	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.0	0.0	0.0	0.5850
1.4788	10.0	10	1.1857	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.0	0.0	0.0	0.6512
0.9806	15.0	15	0.8188	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.21875, 'recall': 0.3333333333333333, 'f1': 0.2641509433962264, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.5, 'recall': 0.1111111111111111, 'f1': 0.1818181818181818, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 12}	0.1667	0.1333	0.1481	0.7660
0.6358	20.0	20	0.5763	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.41935483870967744, 'recall': 0.6190476190476191, 'f1': 0.5, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.7, 'recall': 0.7777777777777778, 'f1': 0.7368421052631577, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.42857142857142855, 'recall': 0.25, 'f1': 0.3157894736842105, 'number': 12}	0.4182	0.3833	0.4	0.8675
0.4712	25.0	25	0.4919	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 6}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.5833333333333334, 'recall': 0.6666666666666666, 'f1': 0.6222222222222222, 'number': 21}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.5833333333333334, 'recall': 0.7777777777777778, 'f1': 0.6666666666666666, 'number': 9}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8}	{'precision': 0.25, 'recall': 0.25, 'f1': 0.25, 'number': 12}	0.4528	0.4	0.4248	0.8698

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.1
• Datasets 2.8.0
• Tokenizers 0.13.2