This model is a fine-tuned version of google/mt5-base on the Beijuka/Multilingual_PII_NER_dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.1109
Precision: 0.9538
Recall: 0.8666
F1: 0.9081
Accuracy: 0.9779

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	0.3211	0.7542	0.6104	0.6747	0.9296
1.0472	2.0	602	0.2406	0.8250	0.7618	0.7921	0.9509
1.0472	3.0	903	0.1859	0.8730	0.7920	0.8305	0.9583
0.2379	4.0	1204	0.1926	0.8622	0.8422	0.8521	0.9627
0.1715	5.0	1505	0.1485	0.8893	0.8377	0.8627	0.9651
0.1715	6.0	1806	0.1426	0.9200	0.8371	0.8766	0.9690
0.1376	7.0	2107	0.1424	0.8915	0.8674	0.8792	0.9693
0.1376	8.0	2408	0.1351	0.8992	0.8674	0.8830	0.9706
0.12	9.0	2709	0.1276	0.9287	0.8641	0.8953	0.9735
0.1072	10.0	3010	0.1290	0.9246	0.8609	0.8916	0.9726
0.1072	11.0	3311	0.1447	0.9074	0.8706	0.8886	0.9725
0.0951	12.0	3612	0.1412	0.9070	0.8796	0.8931	0.9734

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

Beijuka/mt5-base-kanuri-ner-v1

multilingual-google/mt5-base-kanuri-ner-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Beijuka/
mt5-base-kanuri-ner-v1