learning_rate: 5e-05
train_batch_size: 64
eval_batch_size: 128
seed: 0
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: 20

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1 Macro	Accuracy
No log	0	0	3.9808	0.0433	0.25	0.0737	0.1730
0.2093	0.7812	1000	0.2214	0.7611	0.5177	0.5153	0.7086
0.1698	1.5625	2000	0.1857	0.5631	0.5555	0.5575	0.7651
0.1121	2.3438	3000	0.2308	0.6434	0.6269	0.6259	0.7284
0.064	3.125	4000	0.2097	0.6739	0.5911	0.6102	0.7503
0.0754	3.9062	5000	0.2096	0.6880	0.6116	0.6331	0.7550
0.043	4.6875	6000	0.2199	0.6600	0.6042	0.6193	0.7440
0.0339	5.4688	7000	0.2400	0.6427	0.6289	0.6301	0.7311
0.0229	6.25	8000	0.2384	0.6554	0.5982	0.6134	0.7470
0.0239	7.0312	9000	0.2401	0.6446	0.6180	0.6268	0.7423
0.0222	7.8125	10000	0.2902	0.6406	0.6020	0.6039	0.7003
0.0189	8.5938	11000	0.2368	0.6471	0.6025	0.6177	0.7368
0.0154	9.375	12000	0.2357	0.6453	0.6222	0.6284	0.7392
0.0115	10.1562	13000	0.2361	0.6856	0.5721	0.5974	0.7519
0.0117	10.9375	14000	0.2431	0.6438	0.6418	0.6413	0.7412
0.0126	11.7188	15000	0.2460	0.6443	0.6101	0.6184	0.7461
0.0079	12.5	16000	0.2571	0.6397	0.6446	0.6342	0.7325
0.006	13.2812	17000	0.2454	0.6572	0.6111	0.6244	0.7428
0.0055	14.0625	18000	0.2430	0.6588	0.6164	0.6246	0.7506
0.0044	14.8438	19000	0.2491	0.6504	0.6272	0.6333	0.7417
0.0052	15.625	20000	0.2445	0.6580	0.6095	0.6229	0.7486
0.0053	16.4062	21000	0.2429	0.6654	0.6054	0.6225	0.7496
0.0021	17.1875	22000	0.2485	0.6740	0.6172	0.6327	0.7496
0.0037	17.9688	23000	0.2432	0.6582	0.6198	0.6307	0.7523
0.0018	18.75	24000	0.2527	0.6575	0.6149	0.6275	0.7451
0.0026	19.5312	25000	0.2492	0.6627	0.6146	0.6294	0.7497

whoisjones/finerweb-multilabel-classifier-xlmr-gemma3

finerweb-multilabel-classifier-xlmr-gemma3