Training Loss	Epoch	Step	Validation Loss	Accuracy
4.614	0.9829	43	4.6167	0.01
4.5907	1.9886	87	4.5880	0.0214
4.5382	2.9943	131	4.5343	0.105
4.4551	4.0	175	4.4235	0.3043
4.287	4.9829	218	4.2081	0.5043
3.885	5.9886	262	3.8133	0.5307
3.4412	6.9943	306	3.3131	0.4907
2.8825	8.0	350	2.8045	0.4871
2.5521	8.9829	393	2.3834	0.4671
2.2911	9.9886	437	2.0563	0.5186
2.0126	10.9943	481	1.8029	0.5679
1.8294	12.0	525	1.6611	0.605
1.745	12.9829	568	1.4988	0.6257
1.6162	13.9886	612	1.3868	0.62
1.5084	14.9943	656	1.2984	0.6429
1.4441	16.0	700	1.2081	0.6457
1.3625	16.9829	743	1.1554	0.6814
1.2752	17.9886	787	1.0955	0.6929
1.224	18.9943	831	1.0373	0.7164
1.2096	20.0	875	1.0375	0.7164
1.1551	20.9829	918	0.9842	0.7414
1.1079	21.9886	962	0.9645	0.7571
1.0669	22.9943	1006	0.9150	0.77
1.0206	24.0	1050	0.8508	0.7836
0.9963	24.9829	1093	0.8458	0.7743
0.9132	25.9886	1137	0.7838	0.7971
0.863	26.9943	1181	0.7590	0.8057
0.8669	28.0	1225	0.7646	0.785
0.8776	28.9829	1268	0.7084	0.8157
0.793	29.9886	1312	0.6862	0.82
0.7941	30.9943	1356	0.6971	0.8143
0.7863	32.0	1400	0.6135	0.8314
0.7344	32.9829	1443	0.5961	0.8407
0.6888	33.9886	1487	0.6304	0.845
0.6693	34.9943	1531	0.6011	0.8364
0.6736	36.0	1575	0.5917	0.8364
0.6739	36.9829	1618	0.5933	0.8336
0.6595	37.9886	1662	0.5824	0.8357
0.641	38.9943	1706	0.5232	0.8579
0.576	40.0	1750	0.5700	0.8393
0.6097	40.9829	1793	0.5384	0.8471
0.6016	41.9886	1837	0.5824	0.8379
0.6017	42.9943	1881	0.5511	0.8443
0.5937	44.0	1925	0.5095	0.8621
0.5674	44.9829	1968	0.5299	0.8536
0.5575	45.9886	2012	0.5106	0.8507
0.5709	46.9943	2056	0.5445	0.8507
0.5046	48.0	2100	0.4848	0.855
0.5485	48.9829	2143	0.5097	0.8564
0.4865	49.9886	2187	0.5227	0.8471
0.5505	50.9943	2231	0.5127	0.8507
0.4827	52.0	2275	0.5253	0.8493
0.5121	52.9829	2318	0.5095	0.8636
0.4879	53.9886	2362	0.5053	0.8621
0.5008	54.9943	2406	0.5196	0.8521
0.489	56.0	2450	0.4834	0.8657
0.5019	56.9829	2493	0.4714	0.8614
0.4828	57.9886	2537	0.5019	0.8571
0.4373	58.9943	2581	0.4894	0.8679
0.4444	60.0	2625	0.5093	0.8657
0.4178	60.9829	2668	0.5058	0.8614
0.4081	61.9886	2712	0.4996	0.8586
0.4311	62.9943	2756	0.4973	0.8557
0.425	64.0	2800	0.4627	0.8743
0.4147	64.9829	2843	0.4875	0.865
0.4505	65.9886	2887	0.4918	0.8636
0.3621	66.9943	2931	0.4903	0.86
0.4072	68.0	2975	0.4983	0.8564
0.3883	68.9829	3018	0.4635	0.8743
0.4284	69.9886	3062	0.4582	0.8686
0.3891	70.9943	3106	0.4456	0.8793
0.4255	72.0	3150	0.4760	0.87
0.425	72.9829	3193	0.4905	0.8721
0.4301	73.9886	3237	0.4942	0.8643
0.3666	74.9943	3281	0.4824	0.8629
0.4275	76.0	3325	0.4638	0.8671
0.4161	76.9829	3368	0.4859	0.8621
0.3773	77.9886	3412	0.4918	0.8521
0.3591	78.9943	3456	0.4881	0.8729
0.4018	80.0	3500	0.4681	0.8707
0.404	80.9829	3543	0.4882	0.86
0.3987	81.9886	3587	0.4796	0.8657
0.3546	82.9943	3631	0.4945	0.8643
0.3795	84.0	3675	0.4638	0.8679
0.4007	84.9829	3718	0.4624	0.8729
0.3783	85.9886	3762	0.4693	0.8729
0.3498	86.9943	3806	0.4980	0.8621
0.3477	88.0	3850	0.4705	0.8671
0.4022	88.9829	3893	0.4817	0.86
0.3697	89.9886	3937	0.4763	0.8629
0.3828	90.9943	3981	0.4867	0.8671
0.3842	92.0	4025	0.4911	0.865
0.3562	92.9829	4068	0.4562	0.875
0.3343	93.9886	4112	0.4573	0.8786
0.3521	94.9943	4156	0.4481	0.8843
0.3788	96.0	4200	0.4793	0.8721
0.3518	96.9829	4243	0.4802	0.8693
0.3491	97.9886	4287	0.4740	0.8686
0.4063	98.2857	4300	0.5132	0.8636

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Tr13/my_awesome_food_model

my_awesome_food_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Tr13/
my_awesome_food_model