estudiante_S3D_RWF2000

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

• Loss: 0.4851
• Accuracy: 0.8925
• F1: 0.8924
• Precision: 0.8937
• Recall: 0.8925
• Roc Auc: 0.9471

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: 1e-05
• train_batch_size: 75
• eval_batch_size: 75
• seed: 42
• optimizer: Use 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_steps: 189
• training_steps: 1890
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
0.4756	3.0116	94	0.4573	0.77	0.7623	0.8102	0.77	0.9241
0.3034	7.0106	188	0.4936	0.775	0.7641	0.8374	0.775	0.9375
0.2149	11.0095	282	0.7740	0.77	0.7583	0.8348	0.77	0.9408
0.1536	15.0085	376	0.7817	0.795	0.7877	0.8418	0.795	0.9405
0.1242	19.0074	470	0.7977	0.7875	0.7793	0.8375	0.7875	0.9390
0.0884	23.0063	564	0.7113	0.83	0.8263	0.8603	0.83	0.9438
0.0742	27.0053	658	0.6809	0.8325	0.8290	0.8619	0.8325	0.9446
0.055	31.0042	752	0.5733	0.835	0.8319	0.8613	0.835	0.9422
0.0441	35.0032	846	0.5877	0.85	0.8485	0.8646	0.85	0.9428
0.0304	39.0021	940	0.4630	0.865	0.8641	0.8746	0.865	0.9444
0.0242	43.0011	1034	0.5108	0.8675	0.8667	0.8765	0.8675	0.9440
0.0201	46.0127	1128	0.5144	0.87	0.8694	0.8774	0.87	0.9451
0.0165	50.0116	1222	0.3905	0.8825	0.8822	0.8860	0.8825	0.9463
0.0232	54.0106	1316	0.4735	0.8825	0.8822	0.8860	0.8825	0.9466
0.0098	58.0095	1410	0.4823	0.8825	0.8823	0.8847	0.8825	0.9457
0.0132	62.0085	1504	0.4508	0.88	0.8798	0.8824	0.88	0.9475
0.0105	66.0074	1598	0.4055	0.885	0.8849	0.8869	0.885	0.9477
0.0082	70.0063	1692	0.4190	0.885	0.8849	0.8869	0.885	0.9464
0.0072	74.0053	1786	0.4812	0.8875	0.8874	0.8891	0.8875	0.9476
0.0056	78.0042	1880	0.4603	0.8975	0.8975	0.8980	0.8975	0.9484

Framework versions

• Transformers 4.46.1
• Pytorch 2.0.1+cu118
• Datasets 3.1.0
• Tokenizers 0.20.1