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: 0.7358
Precision: 0.7910
Recall: 0.7946
F1: 0.7928
Accuracy: 0.8100

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	398	2.7430	0.3351	0.45	0.3841	0.3355
4.9558	2.0	796	2.3019	0.2968	0.4930	0.3705	0.3133
2.7275	3.0	1194	2.0554	0.4145	0.4320	0.4231	0.3836
2.3526	4.0	1592	1.9726	0.7027	0.3305	0.4495	0.3887
2.3526	5.0	1990	1.6121	0.5668	0.5273	0.5463	0.5036
2.1054	6.0	2388	1.5167	0.4953	0.5711	0.5305	0.5334
1.8628	7.0	2786	1.3548	0.5812	0.6125	0.5964	0.5917
1.633	8.0	3184	1.2491	0.592	0.6359	0.6132	0.6126
1.4669	9.0	3582	1.1306	0.6485	0.6617	0.6551	0.6539
1.4669	10.0	3980	1.0705	0.6427	0.6773	0.6596	0.6722
1.2985	11.0	4378	0.8972	0.7033	0.7445	0.7233	0.7229
1.1477	12.0	4776	0.9050	0.7087	0.7336	0.7209	0.7220
1.0512	13.0	5174	0.8057	0.7515	0.7609	0.7562	0.7582
0.9269	14.0	5572	0.7670	0.7545	0.7805	0.7673	0.7718
0.9269	15.0	5970	0.6996	0.7830	0.8063	0.7945	0.7901
0.8489	16.0	6368	0.7031	0.7842	0.8063	0.7951	0.7935
0.7588	17.0	6766	0.6762	0.7891	0.8156	0.8022	0.8059
0.7432	18.0	7164	0.6648	0.7901	0.8234	0.8064	0.8080
0.6983	19.0	7562	0.6598	0.7991	0.8234	0.8111	0.8114
0.6983	20.0	7960	0.6626	0.7937	0.8203	0.8068	0.8097

Framework versions

Transformers 4.55.4
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

Beijuka/mt5-base-lumasaba-ner-v1

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

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Beijuka/
mt5-base-lumasaba-ner-v1