learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 2
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: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.3714	1.0	2000	0.3609
0.3259	2.0	4000	0.3323
0.3179	3.0	6000	0.3197
0.3111	4.0	8000	0.3170
0.2988	5.0	10000	0.3155
0.3016	6.0	12000	0.3131
0.2968	7.0	14000	0.3132
0.2787	8.0	16000	0.3118
0.2687	9.0	18000	0.3125
0.2603	10.0	20000	0.3166
0.2621	11.0	22000	0.3220
0.2599	12.0	24000	0.3242
0.2353	13.0	26000	0.3419
0.2255	14.0	28000	0.3517
0.2174	15.0	30000	0.3615
0.2102	16.0	32000	0.3775
0.1969	17.0	34000	0.3847
0.1811	18.0	36000	0.4042
0.174	19.0	38000	0.4146
0.1677	20.0	40000	0.4211
0.156	21.0	42000	0.4401
0.1482	22.0	44000	0.4464
0.1348	23.0	46000	0.4594
0.1385	24.0	48000	0.4720
0.1257	25.0	50000	0.4849
0.1207	26.0	52000	0.4939
0.1086	27.0	54000	0.5138
0.1069	28.0	56000	0.5174
0.1032	29.0	58000	0.5243
0.0952	30.0	60000	0.5406
0.0916	31.0	62000	0.5464
0.0898	32.0	64000	0.5556
0.0865	33.0	66000	0.5704
0.0827	34.0	68000	0.5745
0.0799	35.0	70000	0.5811
0.0807	36.0	72000	0.5907
0.0739	37.0	74000	0.6000
0.0758	38.0	76000	0.6043
0.0713	39.0	78000	0.6086
0.0696	40.0	80000	0.6206
0.0707	41.0	82000	0.6201
0.0709	42.0	84000	0.6230
0.0661	43.0	86000	0.6264
0.0674	44.0	88000	0.6288
0.0655	45.0	90000	0.6371
0.0639	46.0	92000	0.6444
0.0634	47.0	94000	0.6483
0.0623	48.0	96000	0.6471
0.0627	49.0	98000	0.6536
0.0627	50.0	100000	0.6552
0.06	51.0	102000	0.6596
0.0606	52.0	104000	0.6624
0.0617	53.0	106000	0.6768
0.0594	54.0	108000	0.6756
0.0592	55.0	110000	0.6803
0.0594	56.0	112000	0.6758
0.0579	57.0	114000	0.6846
0.0583	58.0	116000	0.6926
0.0581	59.0	118000	0.6880
0.0579	60.0	120000	0.6956
0.0563	61.0	122000	0.6978
0.0572	62.0	124000	0.6940
0.0582	63.0	126000	0.7012
0.0581	64.0	128000	0.6993
0.0561	65.0	130000	0.7005
0.0558	66.0	132000	0.7081
0.0554	67.0	134000	0.7126
0.0552	68.0	136000	0.7082
0.0563	69.0	138000	0.7126
0.0558	70.0	140000	0.7188
0.055	71.0	142000	0.7198
0.0544	72.0	144000	0.7193
0.0538	73.0	146000	0.7265
0.0547	74.0	148000	0.7270
0.0533	75.0	150000	0.7271
0.0534	76.0	152000	0.7316
0.0533	77.0	154000	0.7333
0.0535	78.0	156000	0.7330
0.0528	79.0	158000	0.7275
0.0529	80.0	160000	0.7322
0.0529	81.0	162000	0.7385
0.0519	82.0	164000	0.7334
0.0525	83.0	166000	0.7381
0.0528	84.0	168000	0.7376
0.0536	85.0	170000	0.7375
0.0514	86.0	172000	0.7479
0.052	87.0	174000	0.7430
0.0518	88.0	176000	0.7452
0.0518	89.0	178000	0.7435
0.0512	90.0	180000	0.7500
0.052	91.0	182000	0.7511
0.0519	92.0	184000	0.7519
0.0516	93.0	186000	0.7533
0.0508	94.0	188000	0.7576
0.0502	95.0	190000	0.7598
0.0506	96.0	192000	0.7584
0.0509	97.0	194000	0.7580
0.0508	98.0	196000	0.7577
0.0497	99.0	198000	0.7588
0.0499	100.0	200000	0.7593

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

muriloms/tcc-football-events-finetune-gpt2-3-5k-100

tcc-football-events-finetune-gpt2-3-5k-100

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

muriloms/
tcc-football-events-finetune-gpt2-3-5k-100