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

Loss: 0.0719
Precision: 0.9064
Recall: 0.9416
F1: 0.9237
Accuracy: 0.9822

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	0.1158	0.9102	0.8262	0.8662	0.9666
0.229	2.0	602	0.0918	0.8883	0.9287	0.9080	0.9736
0.229	3.0	903	0.0924	0.8654	0.9401	0.9012	0.9751
0.0702	4.0	1204	0.1025	0.8772	0.9461	0.9103	0.9750
0.0514	5.0	1505	0.1446	0.8542	0.8670	0.8605	0.9671
0.0514	6.0	1806	0.1227	0.8946	0.9203	0.9073	0.9732
0.034	7.0	2107	0.1240	0.8949	0.9233	0.9089	0.9747

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

Beijuka/multilingual-xlm-roberta-base-kanuri-ner-v1

multilingual-xlm-roberta-base-kanuri-ner-v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Beijuka/
multilingual-xlm-roberta-base-kanuri-ner-v1