learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
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
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Exact	F1	Total	Hasans Exact	Hasans F1	Hasans Total	Best Exact	Best F1
0.68	0.0265	100	0.5825	75.1479	81.0196	15041	75.1479	81.0196	15041	75.1479	81.0196
0.6035	0.0530	200	0.5453	75.5601	81.4615	15041	75.5601	81.4615	15041	75.5601	81.4615
0.5286	0.0795	300	0.5200	75.8527	81.4575	15041	75.8527	81.4575	15041	75.8527	81.4575
0.5957	0.1060	400	0.4978	76.3978	81.9120	15041	76.3978	81.9120	15041	76.3978	81.9120
0.5173	0.1325	500	0.4919	75.9457	81.4362	15041	75.9457	81.4362	15041	75.9457	81.4362
0.4847	0.1589	600	0.4845	76.4244	81.9856	15041	76.4244	81.9856	15041	76.4244	81.9856
0.4764	0.1854	700	0.4717	76.5441	82.1418	15041	76.5441	82.1418	15041	76.5441	82.1418
0.4707	0.2119	800	0.4601	76.8632	82.2239	15041	76.8632	82.2239	15041	76.8632	82.2239
0.4725	0.2384	900	0.4577	76.9829	82.3913	15041	76.9829	82.3913	15041	76.9829	82.3913
0.4905	0.2649	1000	0.4448	77.1092	82.3780	15041	77.1092	82.3780	15041	77.1092	82.3780
0.5156	0.2914	1100	0.4387	77.0826	82.3692	15041	77.0826	82.3692	15041	77.0826	82.3692
0.4482	0.3179	1200	0.4300	77.0162	82.2991	15041	77.0162	82.2991	15041	77.0162	82.2991
0.4548	0.3444	1300	0.4350	76.9497	82.1747	15041	76.9497	82.1747	15041	76.9497	82.1747
0.4344	0.3709	1400	0.4237	77.1957	82.5521	15041	77.1957	82.5521	15041	77.1957	82.5521
0.4431	0.3974	1500	0.4237	77.1092	82.4167	15041	77.1092	82.4167	15041	77.1092	82.4167
0.4462	0.4238	1600	0.4120	77.2754	82.4544	15041	77.2754	82.4544	15041	77.2754	82.4544
0.4353	0.4503	1700	0.4079	77.6411	82.8295	15041	77.6411	82.8295	15041	77.6411	82.8295
0.4344	0.4768	1800	0.4006	77.5347	82.8334	15041	77.5347	82.8334	15041	77.5347	82.8334
0.3835	0.5033	1900	0.4012	77.8140	82.9213	15041	77.8140	82.9213	15041	77.8140	82.9213
0.4618	0.5298	2000	0.3891	77.6345	82.8467	15041	77.6345	82.8467	15041	77.6345	82.8467
0.4156	0.5563	2100	0.3844	77.5613	82.7081	15041	77.5613	82.7081	15041	77.5613	82.7081
0.4051	0.5828	2200	0.3852	77.8871	82.8588	15041	77.8871	82.8588	15041	77.8871	82.8588
0.4071	0.6093	2300	0.3833	77.6810	82.8300	15041	77.6810	82.8300	15041	77.6810	82.8300
0.3738	0.6358	2400	0.3814	77.8938	83.0841	15041	77.8938	83.0841	15041	77.8938	83.0841
0.4027	0.6623	2500	0.3717	77.6012	82.6103	15041	77.6012	82.6103	15041	77.6012	82.6103
0.4326	0.6887	2600	0.3652	77.9469	82.9494	15041	77.9469	82.9494	15041	77.9469	82.9494
0.3293	0.7152	2700	0.3660	78.1265	83.0404	15041	78.1265	83.0404	15041	78.1265	83.0404
0.4206	0.7417	2800	0.3569	77.6544	82.5986	15041	77.6544	82.5986	15041	77.6544	82.5986
0.3474	0.7682	2900	0.3634	77.8339	82.7735	15041	77.8339	82.7735	15041	77.8339	82.7735
0.3742	0.7947	3000	0.3526	78.3326	83.1619	15041	78.3326	83.1619	15041	78.3326	83.1619
0.3992	0.8212	3100	0.3491	77.9735	82.7812	15041	77.9735	82.7812	15041	77.9735	82.7812
0.4146	0.8477	3200	0.3492	78.3459	83.1638	15041	78.3459	83.1638	15041	78.3459	83.1638
0.3934	0.8742	3300	0.3444	77.8605	82.6116	15041	77.8605	82.6116	15041	77.8605	82.6116
0.3673	0.9007	3400	0.3465	77.9403	82.7155	15041	77.9403	82.7155	15041	77.9403	82.7155
0.4128	0.9272	3500	0.3406	77.8738	82.7600	15041	77.8738	82.7600	15041	77.8738	82.7600
0.3976	0.9536	3600	0.3368	78.0533	82.8822	15041	78.0533	82.8822	15041	78.0533	82.8822
0.4077	0.9801	3700	0.3392	77.7408	82.5339	15041	77.7408	82.5339	15041	77.7408	82.5339
0.3512	1.0066	3800	0.3395	78.1331	82.8586	15041	78.1331	82.8586	15041	78.1331	82.8586
0.2996	1.0331	3900	0.3442	77.7010	82.5547	15041	77.7010	82.5547	15041	77.7010	82.5547
0.2646	1.0596	4000	0.3471	78.0999	82.8657	15041	78.0999	82.8657	15041	78.0999	82.8657
0.2925	1.0861	4100	0.3470	77.9270	82.5864	15041	77.9270	82.5864	15041	77.9270	82.5864
0.2995	1.1126	4200	0.3456	78.0068	82.8115	15041	78.0068	82.8115	15041	78.0068	82.8115

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.1

leomaurodesenv/bert-basketball-qa

bert-basketball-qa

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leomaurodesenv/
bert-basketball-qa