STL-FullFT-mt5-base-stance

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

• Loss: 1.9222
• Accuracy: 0.6995
• Macro F1: 0.7032
• Precision: 0.7004
• Recall: 0.7184

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: 0.0003
• train_batch_size: 16
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• 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: cosine
• lr_scheduler_warmup_steps: 300
• num_epochs: 10
• label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro F1	Precision	Recall
23.3529	1.0	189	2.9369	0.3105	0.1185	0.0776	0.25
4.5129	2.0	378	2.0737	0.5923	0.5891	0.6206	0.6129
4.1711	3.0	567	2.0382	0.6309	0.6317	0.6519	0.6554
3.9759	4.0	756	1.9233	0.6746	0.6808	0.6789	0.6882
3.9283	5.0	945	1.9214	0.6933	0.6951	0.6926	0.7155
3.8477	6.0	1134	1.9499	0.6958	0.6972	0.6988	0.7168
3.7674	7.0	1323	1.9107	0.6895	0.6979	0.7008	0.6996
3.7598	8.0	1512	1.9083	0.7057	0.7116	0.7068	0.7217
3.7421	9.0	1701	1.9191	0.7057	0.7112	0.7081	0.7218
3.7826	10.0	1890	1.9222	0.6995	0.7032	0.7004	0.7184

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2