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This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6024
• Accuracy: 0.8958
• F1: 0.8910
• Precision: 0.8846
• Recall: 0.9295

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: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
2.9886	10.0	50	2.5714	0.2708	0.1833	0.1547	0.2949
1.3584	20.0	100	1.1008	0.8542	0.8496	0.8622	0.9071
0.1639	30.0	150	0.6093	0.8958	0.8897	0.8910	0.9295
0.0136	40.0	200	0.6092	0.9167	0.9112	0.9071	0.9423
0.007	50.0	250	0.6024	0.8958	0.8910	0.8846	0.9295

Framework versions

• Transformers 4.37.2
• Pytorch 2.2.0+cu121
• Tokenizers 0.15.2