learning_rate: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	Precision Macro	Precision Micro	Precision Weighted	Recall Macro	Recall Micro	Recall Weighted	Accuracy
1.9373	1.0	15	1.8818	0.0464	0.1894	0.0615	0.0277	0.1894	0.0367	0.1429	0.1894	0.1894	0.1894
1.869	2.0	30	1.8642	0.1100	0.2652	0.1418	0.075	0.2652	0.0968	0.2063	0.2652	0.2652	0.2652
1.9218	3.0	45	1.8754	0.1163	0.2576	0.1460	0.1316	0.2576	0.1566	0.1905	0.2576	0.2576	0.2576
1.6733	4.0	60	1.6881	0.2445	0.3864	0.3053	0.2427	0.3864	0.2917	0.2992	0.3864	0.3864	0.3864
1.54	5.0	75	1.5528	0.3252	0.4242	0.3856	0.3429	0.4242	0.4101	0.3570	0.4242	0.4242	0.4242
1.4418	6.0	90	1.5737	0.2858	0.3864	0.3213	0.2846	0.3864	0.3243	0.3398	0.3864	0.3864	0.3864
0.8592	7.0	105	1.5408	0.3444	0.4394	0.3965	0.3208	0.4394	0.3674	0.3791	0.4394	0.4394	0.4394
1.1427	8.0	120	1.2804	0.4638	0.5606	0.5317	0.4698	0.5606	0.5280	0.4831	0.5606	0.5606	0.5606
0.7849	9.0	135	1.2880	0.4649	0.5530	0.5291	0.4804	0.5530	0.5401	0.4823	0.5530	0.5530	0.5530
0.6846	10.0	150	1.3130	0.4298	0.5152	0.4811	0.4404	0.5152	0.5005	0.4671	0.5152	0.5152	0.5152
0.4006	11.0	165	1.2958	0.4931	0.5833	0.5598	0.4983	0.5833	0.5756	0.5229	0.5833	0.5833	0.5833
0.4329	12.0	180	1.2990	0.5062	0.5530	0.5562	0.5315	0.5530	0.5874	0.5133	0.5530	0.5530	0.5530
0.482	13.0	195	1.3831	0.4842	0.5152	0.5233	0.5517	0.5152	0.5803	0.4839	0.5152	0.5152	0.5152
0.6409	14.0	210	1.4066	0.5081	0.5985	0.5765	0.5194	0.5985	0.5820	0.5232	0.5985	0.5985	0.5985
0.3206	15.0	225	1.3690	0.5155	0.5606	0.5520	0.6158	0.5606	0.5890	0.5170	0.5606	0.5606	0.5606
0.1773	16.0	240	1.2568	0.5920	0.6515	0.6408	0.6894	0.6515	0.6623	0.5843	0.6515	0.6515	0.6515
0.3259	17.0	255	1.3406	0.5467	0.6061	0.5961	0.5615	0.6061	0.6033	0.5467	0.6061	0.6061	0.6061
0.1123	18.0	270	1.3767	0.5868	0.6364	0.6306	0.6258	0.6364	0.6413	0.5785	0.6364	0.6364	0.6364
0.1129	19.0	285	1.4680	0.5879	0.6439	0.6306	0.6809	0.6439	0.6933	0.5806	0.6439	0.6439	0.6439
0.0651	20.0	300	1.4981	0.6655	0.6894	0.6876	0.7115	0.6894	0.7224	0.6511	0.6894	0.6894	0.6894
0.0685	21.0	315	1.4621	0.6091	0.6515	0.6494	0.6303	0.6515	0.6641	0.6040	0.6515	0.6515	0.6515
0.1469	22.0	330	1.5347	0.5330	0.6212	0.6040	0.5477	0.6212	0.6149	0.5440	0.6212	0.6212	0.6212
0.0289	23.0	345	1.5417	0.5466	0.6288	0.6180	0.5409	0.6288	0.6108	0.5549	0.6288	0.6288	0.6288
0.01	24.0	360	1.5670	0.5475	0.6364	0.6187	0.5435	0.6364	0.6104	0.5594	0.6364	0.6364	0.6364
0.035	25.0	375	1.6037	0.5529	0.6364	0.6209	0.5470	0.6364	0.6156	0.5679	0.6364	0.6364	0.6364
0.0109	26.0	390	1.6752	0.5897	0.6212	0.6203	0.6145	0.6212	0.6527	0.6000	0.6212	0.6212	0.6212
0.038	27.0	405	1.6724	0.5344	0.6136	0.6008	0.5332	0.6136	0.6005	0.5468	0.6136	0.6136	0.6136
0.0116	28.0	420	1.6252	0.5384	0.6212	0.6090	0.5337	0.6212	0.6033	0.5491	0.6212	0.6212	0.6212
0.006	29.0	435	1.5980	0.5572	0.6364	0.6294	0.5529	0.6364	0.6246	0.5634	0.6364	0.6364	0.6364
0.0046	30.0	450	1.5939	0.5605	0.6439	0.6342	0.5546	0.6439	0.6269	0.5687	0.6439	0.6439	0.6439

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

corranm/square_run_32_batch

square_run_32_batch

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

corranm/
square_run_32_batch