truecaser-lin-sna

This model is a fine-tuned version of Davlan/afro-xlmr-large-114L on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0276
• Accuracy: 0.9932
• Recall Lower: 0.9984
• Prec Lower: 0.9944
• Recall Cap: 0.9374
• Prec Cap: 0.9788
• Recall Upper: 0.0
• Prec Upper: 0.0
• Cap Recall: 0.9319

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 32
• 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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall Lower	Prec Lower	Recall Cap	Prec Cap	Recall Upper	Prec Upper	Cap Recall
0.0611	0.1037	200	0.0382	0.9926	0.9973	0.9948	0.9418	0.9657	0.0	0.0	0.9362
0.0345	0.2074	400	0.0328	0.9920	0.9948	0.9970	0.9646	0.9348	0.0	0.0	0.9589
0.0303	0.3110	600	0.0285	0.9927	0.9984	0.9940	0.9305	0.9760	0.0	0.0	0.9250
0.033	0.4147	800	0.0272	0.9934	0.9974	0.9959	0.9499	0.9660	0.2105	0.3077	0.9456
0.0296	0.5184	1000	0.0270	0.9934	0.9965	0.9967	0.9609	0.9543	0.0526	1.0	0.9555
0.0327	0.6221	1200	0.0276	0.9932	0.9984	0.9944	0.9374	0.9788	0.0	0.0	0.9319

Framework versions

• Transformers 4.57.6
• Pytorch 2.13.0+cu130
• Datasets 3.6.0
• Tokenizers 0.22.2