multilingual-roberta-base-kanuri-ner-v1

This model is a fine-tuned version of roberta-base on the Beijuka/Multilingual_PII_NER_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.0957
• Precision: 0.9576
• Recall: 0.9302
• F1: 0.9437
• Accuracy: 0.9867

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: 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
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	301	0.1120	0.8716	0.8372	0.8541	0.9691
0.187	2.0	602	0.0885	0.8735	0.9206	0.8964	0.9750
0.187	3.0	903	0.0975	0.8666	0.8911	0.8787	0.9742
0.0664	4.0	1204	0.0992	0.8715	0.9194	0.8948	0.9764
0.0458	5.0	1505	0.0900	0.9008	0.9228	0.9116	0.9767
0.0458	6.0	1806	0.0900	0.9050	0.9267	0.9157	0.9800
0.0311	7.0	2107	0.1075	0.8921	0.9328	0.9120	0.9787
0.0311	8.0	2408	0.1353	0.8920	0.9311	0.9111	0.9791
0.0215	9.0	2709	0.1167	0.9090	0.9267	0.9177	0.9792
0.0109	10.0	3010	0.1201	0.9082	0.9289	0.9184	0.9807
0.0109	11.0	3311	0.1304	0.9110	0.9272	0.9191	0.9810
0.0064	12.0	3612	0.1823	0.8918	0.9344	0.9126	0.9788
0.0064	13.0	3913	0.1507	0.9038	0.9289	0.9162	0.9803
0.0042	14.0	4214	0.1763	0.8990	0.935	0.9167	0.9807

Framework versions

• Transformers 4.55.4
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.21.4