mt5-small-nepali-v3.1

This model is a fine-tuned version of aku47/mt5-small-nepali-final on an unknown dataset. It achieves the following results on the evaluation set:

• loss : 1.1750
• rouge1 : 46.7405
• rouge2 : 33.0106
• rougeL : 41.9579
• rougeLsum : 41.9541
• bertscore_precision : 80.7205
• bertscore_recall : 79.6989
• bertscore_f1 : 80.1352

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-06
• train_batch_size: 8
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Bertscore Precision	Bertscore Recall	Bertscore F1	Gen Len
2.1943	1.0	487	1.2857	0.2067	0.1381	0.1971	0.1969	0.7842	0.6609	0.7164	21.0
1.9194	2.0	974	1.2561	0.2102	0.1423	0.2015	0.2013	0.7874	0.6616	0.7182	21.0
1.7597	3.0	1461	1.2300	0.2099	0.1436	0.2017	0.2015	0.7884	0.6613	0.7185	21.0
1.7099	4.0	1948	1.1986	0.208	0.1413	0.2002	0.1999	0.7875	0.6612	0.718	21.0
1.676	5.0	2435	1.1750	0.2073	0.1407	0.1994	0.1991	0.7868	0.6609	0.7175	21.0

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1