multilingual-google-bert/bert-base-multilingual-cased-kanuri-ner-v1

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

• Loss: 0.1582
• Precision: 0.9257
• Recall: 0.9368
• F1: 0.9312
• Accuracy: 0.9764

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	0.1495	0.8764	0.8390	0.8573	0.9552
0.2147	2.0	602	0.1440	0.8558	0.9372	0.8947	0.9603
0.2147	3.0	903	0.1117	0.8943	0.9403	0.9168	0.9686
0.0879	4.0	1204	0.1529	0.875	0.9506	0.9112	0.9670
0.0566	5.0	1505	0.1413	0.9036	0.9306	0.9169	0.9694
0.0566	6.0	1806	0.1745	0.8961	0.9228	0.9093	0.9668
0.0349	7.0	2107	0.1738	0.9021	0.9342	0.9179	0.9699
0.0349	8.0	2408	0.2014	0.8914	0.9372	0.9137	0.9679
0.0201	9.0	2709	0.1736	0.9071	0.9444	0.9254	0.9727
0.0115	10.0	3010	0.1846	0.8935	0.9280	0.9104	0.9675
0.0115	11.0	3311	0.2373	0.8895	0.9403	0.9142	0.9681
0.0073	12.0	3612	0.2186	0.9027	0.9501	0.9258	0.9721
0.0073	13.0	3913	0.2325	0.9015	0.9372	0.9190	0.9686
0.0035	14.0	4214	0.2364	0.9028	0.9465	0.9242	0.9710
0.0016	15.0	4515	0.2366	0.9004	0.9486	0.9238	0.9708

Framework versions

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