jim-crow-test2323

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

• Loss: 0.0984
• Accuracy: 0.9720
• Precision: 0.9340
• Recall: 0.9706
• F1: 0.9519
• Macro Precision: 0.9610
• Macro Recall: 0.9716
• Macro F1: 0.9661
• Tn: 248
• Fp: 7
• Fn: 3
• Tp: 99

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 0.1
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Macro Precision	Macro Recall	Macro F1	Tn	Fp	Fn	Tp
0.0677	1.0	90	0.1643	0.9524	0.8899	0.9510	0.9194	0.9349	0.9520	0.9428	243	12	5	97
0.1282	2.0	180	0.0984	0.9720	0.9340	0.9706	0.9519	0.9610	0.9716	0.9661	248	7	3	99
0.0683	3.0	270	0.1819	0.9720	0.9694	0.9314	0.95	0.9712	0.9598	0.9653	252	3	7	95
0.0226	4.0	360	0.1095	0.9692	0.9174	0.9804	0.9479	0.9547	0.9725	0.9630	246	9	2	100
0.0219	5.0	450	0.1491	0.9720	0.9423	0.9608	0.9515	0.9632	0.9686	0.9659	249	6	4	98

Framework versions

• Transformers 5.7.0
• Pytorch 2.11.0+cu130
• Datasets 4.8.5
• Tokenizers 0.22.2