Gestionabilidad-v3_batch32

This model is a fine-tuned version of dccuchile/tulio-chilean-spanish-bert on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1858
• Accuracy: 0.9298
• Precision: 0.9300
• Recall: 0.9298
• F1: 0.9296

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: 32
• eval_batch_size: 8
• seed: 42
• 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
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.283	0.2289	500	0.2429	0.9044	0.9072	0.9044	0.9048
0.2275	0.4579	1000	0.2073	0.9185	0.9185	0.9185	0.9183
0.2066	0.6868	1500	0.1900	0.9187	0.9202	0.9187	0.9181
0.1949	0.9158	2000	0.2105	0.9194	0.9213	0.9194	0.9187
0.1657	1.1447	2500	0.1920	0.9263	0.9270	0.9263	0.9259
0.1502	1.3736	3000	0.2021	0.9280	0.9279	0.9280	0.9279
0.1412	1.6026	3500	0.1858	0.9298	0.9300	0.9298	0.9296
0.1477	1.8315	4000	0.1950	0.9300	0.9304	0.9300	0.9301
0.1296	2.0604	4500	0.2188	0.9303	0.9304	0.9303	0.9304
0.1004	2.2894	5000	0.2367	0.9304	0.9305	0.9304	0.9305
0.0958	2.5183	5500	0.2294	0.9305	0.9305	0.9305	0.9303
0.1003	2.7473	6000	0.2394	0.9293	0.9299	0.9293	0.9290
0.1029	2.9762	6500	0.2294	0.9321	0.9320	0.9321	0.9320
0.0696	3.2051	7000	0.2727	0.9324	0.9324	0.9324	0.9322
0.0619	3.4341	7500	0.2672	0.9287	0.9301	0.9287	0.9289
0.0627	3.6630	8000	0.2897	0.9326	0.9329	0.9326	0.9327
0.0639	3.8919	8500	0.2970	0.9322	0.9322	0.9322	0.9322
0.0549	4.1209	9000	0.3230	0.9321	0.9322	0.9321	0.9321
0.0409	4.3498	9500	0.3722	0.9313	0.9317	0.9313	0.9314
0.0388	4.5788	10000	0.3326	0.9333	0.9335	0.9333	0.9333
0.0373	4.8077	10500	0.3565	0.9332	0.9335	0.9332	0.9333

Framework versions

• Transformers 4.48.3
• Pytorch 2.5.1+cu124
• Datasets 3.3.2
• Tokenizers 0.21.0