dit-base_tobacco-tiny_tobacco3482_hint

This model is a fine-tuned version of WinKawaks/vit-tiny-patch16-224 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.1196
Accuracy: 0.805
Brier Loss: 0.3299
Nll: 1.3687
F1 Micro: 0.805
F1 Macro: 0.7917
Ece: 0.1606
Aurc: 0.0741

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
No log	1.0	7	3.7183	0.225	0.9191	7.9210	0.225	0.1037	0.3197	0.7539
No log	2.0	14	3.2602	0.365	0.7808	4.4817	0.3650	0.2558	0.2908	0.5116
No log	3.0	21	2.8475	0.53	0.6305	2.7026	0.53	0.4263	0.2496	0.2812
No log	4.0	28	2.6118	0.575	0.5382	1.8007	0.575	0.5478	0.2435	0.2022
No log	5.0	35	2.4289	0.66	0.4404	1.6436	0.66	0.6317	0.2124	0.1236
No log	6.0	42	2.4007	0.69	0.4123	1.9255	0.69	0.6896	0.2038	0.1195
No log	7.0	49	2.3239	0.745	0.3775	1.9428	0.745	0.7365	0.1838	0.0957
No log	8.0	56	2.3109	0.735	0.3660	1.6472	0.735	0.7370	0.1729	0.0842
No log	9.0	63	2.3263	0.74	0.3867	1.5935	0.74	0.7422	0.1803	0.0944
No log	10.0	70	2.3878	0.735	0.4037	1.6604	0.735	0.7039	0.2057	0.0909
No log	11.0	77	2.5009	0.715	0.4220	1.8526	0.715	0.6918	0.2184	0.1017
No log	12.0	84	2.5428	0.72	0.4169	1.8799	0.72	0.6894	0.2000	0.0979
No log	13.0	91	2.5197	0.765	0.3884	1.8802	0.765	0.7268	0.1895	0.0933
No log	14.0	98	2.4928	0.725	0.4147	1.6096	0.7250	0.6923	0.2078	0.0940
No log	15.0	105	2.4172	0.765	0.3748	1.6754	0.765	0.7317	0.1874	0.0824
No log	16.0	112	2.4116	0.76	0.3873	1.4155	0.76	0.7280	0.1972	0.0890
No log	17.0	119	2.9196	0.73	0.4500	1.8493	0.7300	0.7220	0.2290	0.1657
No log	18.0	126	2.5815	0.765	0.4175	2.0326	0.765	0.7467	0.2125	0.0990
No log	19.0	133	2.7076	0.735	0.4475	1.8526	0.735	0.7152	0.2062	0.1049
No log	20.0	140	2.6951	0.71	0.4709	2.0521	0.7100	0.7258	0.2380	0.1188
No log	21.0	147	2.4037	0.765	0.4013	1.8740	0.765	0.7691	0.2009	0.0949
No log	22.0	154	2.6585	0.73	0.4303	2.0299	0.7300	0.7170	0.2110	0.1004
No log	23.0	161	2.4320	0.75	0.3950	1.8720	0.75	0.7340	0.2004	0.0895
No log	24.0	168	2.4891	0.74	0.4199	1.6458	0.74	0.7458	0.2227	0.1128
No log	25.0	175	2.6550	0.705	0.4833	1.7755	0.705	0.7042	0.2478	0.1145
No log	26.0	182	2.3191	0.765	0.3965	1.6941	0.765	0.7415	0.1954	0.0731
No log	27.0	189	2.3000	0.785	0.3763	1.3143	0.785	0.7416	0.2026	0.0712
No log	28.0	196	2.2047	0.78	0.3409	1.4818	0.78	0.7694	0.1759	0.0688
No log	29.0	203	2.3587	0.77	0.3781	1.6779	0.7700	0.7571	0.1937	0.0766
No log	30.0	210	2.5027	0.75	0.4400	1.8454	0.75	0.7338	0.2232	0.0817
No log	31.0	217	2.4092	0.77	0.3899	1.5010	0.7700	0.7498	0.1987	0.0710
No log	32.0	224	2.7655	0.74	0.4520	2.0720	0.74	0.7177	0.2266	0.0855
No log	33.0	231	2.3814	0.76	0.3979	1.4053	0.76	0.7352	0.1982	0.0754
No log	34.0	238	2.3946	0.775	0.3790	1.6969	0.775	0.7387	0.1908	0.0876
No log	35.0	245	2.5158	0.775	0.4064	1.4329	0.775	0.7428	0.2068	0.0929
No log	36.0	252	2.4920	0.75	0.4281	1.5724	0.75	0.7470	0.2161	0.1028
No log	37.0	259	2.4541	0.765	0.3842	1.5272	0.765	0.7163	0.1918	0.0853
No log	38.0	266	2.3785	0.82	0.3319	1.6360	0.82	0.7944	0.1732	0.0811
No log	39.0	273	2.3721	0.77	0.3831	1.2724	0.7700	0.7649	0.1880	0.0818
No log	40.0	280	2.5684	0.795	0.3494	1.7291	0.795	0.7724	0.1771	0.1126
No log	41.0	287	2.4835	0.78	0.3799	1.7700	0.78	0.7594	0.2024	0.0821
No log	42.0	294	2.4690	0.795	0.3678	1.6379	0.795	0.7804	0.1836	0.0840
No log	43.0	301	2.3069	0.77	0.3809	1.4970	0.7700	0.7621	0.1807	0.0784
No log	44.0	308	2.4424	0.795	0.3558	1.7162	0.795	0.7857	0.1879	0.0667
No log	45.0	315	2.0018	0.86	0.2387	1.4924	0.8600	0.8558	0.1180	0.0526
No log	46.0	322	2.4174	0.785	0.3593	1.7688	0.785	0.7662	0.1859	0.0773
No log	47.0	329	2.1816	0.84	0.2862	1.5093	0.8400	0.8230	0.1459	0.0679
No log	48.0	336	2.3006	0.78	0.3601	1.7675	0.78	0.7496	0.1847	0.0752
No log	49.0	343	2.2952	0.81	0.3231	1.4851	0.81	0.7865	0.1691	0.0656
No log	50.0	350	2.1346	0.8	0.3132	1.4625	0.8000	0.7937	0.1660	0.0789
No log	51.0	357	2.2935	0.81	0.3383	1.5764	0.81	0.7914	0.1770	0.0717
No log	52.0	364	2.1792	0.825	0.3116	1.5652	0.825	0.8163	0.1454	0.0654
No log	53.0	371	2.1231	0.81	0.3066	1.3012	0.81	0.8062	0.1552	0.0604
No log	54.0	378	1.9712	0.825	0.2854	1.2891	0.825	0.8137	0.1430	0.0521
No log	55.0	385	2.0133	0.825	0.2839	1.3994	0.825	0.8086	0.1433	0.0560
No log	56.0	392	1.9978	0.835	0.2800	1.4348	0.835	0.8232	0.1415	0.0573
No log	57.0	399	1.9847	0.83	0.2825	1.3907	0.83	0.8153	0.1421	0.0560
No log	58.0	406	1.9892	0.83	0.2832	1.4502	0.83	0.8153	0.1503	0.0566
No log	59.0	413	1.9848	0.83	0.2851	1.4506	0.83	0.8156	0.1462	0.0560
No log	60.0	420	1.9871	0.835	0.2910	1.4527	0.835	0.8191	0.1608	0.0566
No log	61.0	427	1.9914	0.825	0.2932	1.4490	0.825	0.8095	0.1464	0.0587
No log	62.0	434	1.9908	0.825	0.2958	1.4459	0.825	0.8095	0.1493	0.0597
No log	63.0	441	1.9954	0.825	0.3012	1.4480	0.825	0.8095	0.1469	0.0606
No log	64.0	448	2.0111	0.82	0.3050	1.4487	0.82	0.8026	0.1507	0.0619
No log	65.0	455	2.0212	0.82	0.3046	1.4469	0.82	0.8026	0.1604	0.0634
No log	66.0	462	2.0170	0.82	0.3059	1.4443	0.82	0.8040	0.1539	0.0639
No log	67.0	469	2.0170	0.815	0.3056	1.4496	0.815	0.8019	0.1534	0.0643
No log	68.0	476	2.0316	0.82	0.3115	1.4522	0.82	0.8026	0.1606	0.0645
No log	69.0	483	2.0335	0.805	0.3132	1.3831	0.805	0.7855	0.1607	0.0654
No log	70.0	490	2.0362	0.815	0.3106	1.3834	0.815	0.7989	0.1614	0.0655
No log	71.0	497	2.0318	0.815	0.3105	1.3893	0.815	0.7947	0.1541	0.0662
1.3661	72.0	504	2.0434	0.815	0.3135	1.4473	0.815	0.7955	0.1579	0.0653
1.3661	73.0	511	2.0517	0.81	0.3139	1.3838	0.81	0.7917	0.1564	0.0680
1.3661	74.0	518	2.0594	0.82	0.3162	1.3783	0.82	0.7975	0.1626	0.0681
1.3661	75.0	525	2.0628	0.815	0.3210	1.3752	0.815	0.7944	0.1598	0.0706
1.3661	76.0	532	2.0605	0.81	0.3158	1.3711	0.81	0.7886	0.1639	0.0684
1.3661	77.0	539	2.0718	0.815	0.3187	1.3860	0.815	0.7944	0.1710	0.0705
1.3661	78.0	546	2.0749	0.815	0.3168	1.3658	0.815	0.7958	0.1569	0.0713
1.3661	79.0	553	2.0796	0.83	0.3188	1.3016	0.83	0.8147	0.1646	0.0722
1.3661	80.0	560	2.0746	0.81	0.3210	1.3758	0.81	0.7916	0.1580	0.0729
1.3661	81.0	567	2.0819	0.815	0.3194	1.3686	0.815	0.7913	0.1576	0.0722
1.3661	82.0	574	2.0866	0.825	0.3182	1.3627	0.825	0.8085	0.1602	0.0718
1.3661	83.0	581	2.0942	0.815	0.3246	1.3249	0.815	0.8008	0.1591	0.0727
1.3661	84.0	588	2.0938	0.815	0.3246	1.3680	0.815	0.7984	0.1848	0.0727
1.3661	85.0	595	2.0912	0.82	0.3222	1.3662	0.82	0.8012	0.1594	0.0702
1.3661	86.0	602	2.0941	0.82	0.3234	1.3764	0.82	0.8012	0.1576	0.0738
1.3661	87.0	609	2.1037	0.8	0.3304	1.3821	0.8000	0.7821	0.1599	0.0740
1.3661	88.0	616	2.1098	0.805	0.3288	1.3587	0.805	0.7932	0.1678	0.0718
1.3661	89.0	623	2.1119	0.81	0.3276	1.3636	0.81	0.7945	0.1622	0.0728
1.3661	90.0	630	2.1078	0.805	0.3279	1.3641	0.805	0.7914	0.1734	0.0737
1.3661	91.0	637	2.1110	0.8	0.3296	1.3686	0.8000	0.7879	0.1636	0.0744
1.3661	92.0	644	2.1150	0.8	0.3317	1.3685	0.8000	0.7879	0.1730	0.0742
1.3661	93.0	651	2.1146	0.8	0.3303	1.3693	0.8000	0.7881	0.1631	0.0742
1.3661	94.0	658	2.1153	0.805	0.3292	1.3657	0.805	0.7917	0.1676	0.0734
1.3661	95.0	665	2.1188	0.805	0.3298	1.3683	0.805	0.7917	0.1690	0.0735
1.3661	96.0	672	2.1183	0.805	0.3291	1.3691	0.805	0.7914	0.1687	0.0742
1.3661	97.0	679	2.1155	0.81	0.3271	1.3664	0.81	0.7942	0.1599	0.0743
1.3661	98.0	686	2.1183	0.805	0.3285	1.3673	0.805	0.7914	0.1638	0.0740
1.3661	99.0	693	2.1179	0.805	0.3297	1.3686	0.805	0.7917	0.1613	0.0741
1.3661	100.0	700	2.1196	0.805	0.3299	1.3687	0.805	0.7917	0.1606	0.0741

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.2.0.dev20231112+cu118
Datasets 2.14.5
Tokenizers 0.14.1

jordyvl/dit-base_tobacco-tiny_tobacco3482_hint

dit-base_tobacco-tiny_tobacco3482_hint

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions