bert-base-uncased-Federal-Regulations-TEST

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5948
• Accuracy: 0.7301
• Precision: 0.7528
• Recall: 0.7301
• F1: 0.7374
• Confusion Matrix: [[2549, 836], [472, 989]]

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: 4e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Confusion Matrix
0.5881	1.0	600	0.5638	0.7154	0.7492	0.7154	0.7249	[[2445, 940], [439, 1022]]
0.5078	2.0	1200	0.5653	0.7266	0.7489	0.7266	0.7339	[[2544, 841], [484, 977]]
0.4387	3.0	1800	0.5948	0.7301	0.7528	0.7301	0.7374	[[2549, 836], [472, 989]]

Framework versions

• Transformers 4.46.2
• Pytorch 2.5.1+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3