videomae-finetuned-nba-5-class-4-batch-8000-vid-multilabel

Los siguientes hiperparámetros se utilizaron durante el entrenamiento:

learning_rate: 1.5e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Adam con betas=(0.9,0.999) y epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 50000

Resultados del entrenamiento:

Pérdida de Entrenamiento
Época
Paso
Pérdida de Validación
F1
Roc Auc
Precisión

0.771
0.04
2000
0.7660
0.6227
0.7324
0.2937

0.6225
1.04
4000
0.6446
0.7401
0.8162
0.4653

0.6093
2.04
6000
0.6517
0.7441
0.8223
0.5253

0.603
3.04
8000
0.5630
0.8151
0.8722
0.6558

0.6547
4.04
10000
0.5009
0.8425
0.8911
0.6695

0.6426
5.04
12000
0.5179
0.8422
0.9012
0.6642

0.4447
6.04
14000
0.5052
0.8537
0.8973
0.7147

0.6949
7.04
16000
0.5045
0.8548
0.8974
0.7337

0.509
8.04
18000
0.5262
0.8705
0.9089
0.7768

0.4341
9.04
20000
0.4731
0.8831
0.9163
0.7863

0.5037
10.04
22000
0.5040
0.8781
0.9154
0.7747

0.3592
11.04
24000
0.5091
0.8746
0.9101
0.7821

0.2829
12.04
26000
0.4709
0.8867
0.9230
0.7905

0.3599
13.04
28000
0.4722
0.8888
0.9230
0.7947

0.4152
14.04
30000
0.4744
0.8911
0.9247
0.8074

0.1167
15.04
32000
0.4817
0.8949
0.9241
0.8179

0.2721
16.04
34000
0.4627
0.9064
0.9337
0.8274

0.42
17.04
36000
0.4849
0.8992
0.9282
0.8295

0.5841
18.04
38000
0.4747
0.9026
0.9304
0.8379

0.509
19.04
40000
0.4779
0.8991
0.9273
0.8316

0.1599
20.04
42000
0.4945
0.9056
0.9319
0.8411

0.3645
21.04
44000
0.4875
0.8984
0.9277
0.8242

0.4402
22.04
46000
0.5156
0.9004
0.9283
0.8368

0.2769
23.04
48000
0.5063
0.9028
0.9296
0.8389

0.3009
24.04
50000
0.5122
0.8975
0.9257
0.8326

Versiones del marco de trabajo:

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1