learning_rate: 0.001
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 10
training_steps: 30000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
2.3995	0.0167	500	2.0015	0.5145	0.4412	0.5145	0.4517
1.3282	0.0334	1000	1.6467	0.5688	0.4908	0.5688	0.5080
1.1086	0.0502	1500	1.5051	0.6304	0.5784	0.6304	0.5766
0.9882	0.0669	2000	1.4518	0.6268	0.6374	0.6268	0.5891
0.9187	0.0836	2500	1.3470	0.6522	0.6245	0.6522	0.6093
0.8546	0.1003	3000	1.3747	0.6159	0.5871	0.6159	0.5760
0.8214	0.1170	3500	1.2708	0.6703	0.6316	0.6703	0.6323
0.7843	0.1338	4000	1.1659	0.6848	0.6639	0.6848	0.6461
0.7470	0.1505	4500	1.1969	0.6848	0.6534	0.6848	0.6491
0.7299	0.1672	5000	1.0592	0.7101	0.7030	0.7101	0.6748
0.7041	0.1839	5500	1.0536	0.6848	0.6728	0.6848	0.6534
0.6755	0.2006	6000	1.0265	0.7138	0.7298	0.7138	0.6852
0.6683	0.2174	6500	1.0049	0.7428	0.7403	0.7428	0.7089
0.6573	0.2341	7000	1.0702	0.7029	0.7052	0.7029	0.6764
0.6372	0.2508	7500	1.0260	0.7210	0.7143	0.7210	0.6998
0.6173	0.2675	8000	0.9654	0.7428	0.7492	0.7428	0.7141
0.6009	0.2842	8500	1.0185	0.7464	0.7504	0.7464	0.7167
0.5924	0.3010	9000	1.0028	0.7283	0.7652	0.7283	0.7052
0.5916	0.3177	9500	0.9581	0.7174	0.7217	0.7174	0.6893
0.5806	0.3344	10000	1.0011	0.7355	0.7618	0.7355	0.7149
0.5672	0.3511	10500	0.8978	0.7572	0.7429	0.7572	0.7307
0.5580	0.3678	11000	0.9525	0.7210	0.7308	0.7210	0.7013
0.5520	0.3846	11500	0.8647	0.7645	0.7695	0.7645	0.7391
0.5552	0.4013	12000	0.8977	0.7536	0.7698	0.7536	0.7358
0.5341	0.4180	12500	0.8526	0.7536	0.7625	0.7536	0.7305
0.5284	0.4347	13000	0.8496	0.7464	0.7310	0.7464	0.7166
0.5322	0.4514	13500	0.7672	0.8007	0.8006	0.8007	0.7827
0.5229	0.4681	14000	0.8253	0.7754	0.7698	0.7754	0.7515
0.5007	0.4849	14500	0.8496	0.7826	0.7649	0.7826	0.7547
0.5109	0.5016	15000	0.7700	0.7754	0.7767	0.7754	0.7518
0.4989	0.5183	15500	0.8338	0.7645	0.7741	0.7645	0.7419
0.4991	0.5350	16000	0.7927	0.7754	0.7928	0.7754	0.7625
0.4977	0.5517	16500	0.7859	0.7790	0.7670	0.7790	0.7551
0.4854	0.5685	17000	0.7915	0.7862	0.7907	0.7862	0.7630
0.4826	0.5852	17500	0.7628	0.8043	0.7964	0.8043	0.7846
0.4765	0.6019	18000	0.7632	0.7971	0.8008	0.7971	0.7791
0.4641	0.6186	18500	0.7722	0.7935	0.7660	0.7935	0.7670
0.4783	0.6353	19000	0.7046	0.7899	0.8111	0.7899	0.7773
0.4745	0.6521	19500	0.7342	0.7899	0.8044	0.7899	0.7726
0.4555	0.6688	20000	0.7116	0.7862	0.7853	0.7862	0.7662
0.4530	0.6855	20500	0.7385	0.7754	0.7658	0.7754	0.7557
0.4565	0.7022	21000	0.7651	0.7899	0.8132	0.7899	0.7770
0.4555	0.7189	21500	0.7902	0.7681	0.7812	0.7681	0.7569
0.4485	0.7357	22000	0.7613	0.7862	0.7962	0.7862	0.7686
0.4518	0.7524	22500	0.7544	0.7862	0.7944	0.7862	0.7676
0.4508	0.7691	23000	0.7296	0.8043	0.8110	0.8043	0.7907
0.4418	0.7858	23500	0.7293	0.8261	0.8527	0.8261	0.8137
0.4365	0.8025	24000	0.7370	0.8043	0.8217	0.8043	0.7928
0.4353	0.8193	24500	0.7100	0.8188	0.8274	0.8188	0.8049
0.4240	0.8360	25000	0.7273	0.7862	0.7857	0.7862	0.7697
0.4205	0.8527	25500	0.7297	0.8225	0.8351	0.8225	0.8059
0.4316	0.8694	26000	0.7204	0.8116	0.8066	0.8116	0.7911
0.4176	0.8861	26500	0.7340	0.8080	0.8184	0.8080	0.7922
0.4240	0.9029	27000	0.7298	0.8116	0.8223	0.8116	0.7964
0.4149	0.9196	27500	0.7410	0.8188	0.8185	0.8188	0.8023
0.4159	0.9363	28000	0.7303	0.8152	0.8388	0.8152	0.8069
0.4068	0.9530	28500	0.7220	0.8043	0.8209	0.8043	0.7955
0.4135	0.9697	29000	0.7313	0.8188	0.8238	0.8188	0.8055
0.4130	0.9865	29500	0.7221	0.8225	0.8320	0.8225	0.8095
0.4213	1.0032	30000	0.7212	0.8297	0.8471	0.8297	0.8191

Framework versions

Transformers 5.8.0
Pytorch 2.11.0+cu130
Datasets 4.8.5
Tokenizers 0.22.2

Sunbird/sunflower_language_classification

sunflower_language_classification_v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sunbird/
sunflower_language_classification