SignBart-KArSL02-100

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0508
• Accuracy: 0.9912
• Precision: 0.9923
• Recall: 0.9912

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: 0.0002
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.5
• num_epochs: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall
5.7871	1.0	17	5.1629	0.02	0.0008	0.02
5.7169	2.0	34	5.1508	0.02	0.0009	0.02
5.739	3.0	51	5.1318	0.02	0.0009	0.02
5.7289	4.0	68	5.1034	0.02	0.0009	0.02
5.6662	5.0	85	5.0714	0.02	0.0009	0.02
5.6519	6.0	102	5.0352	0.02	0.0009	0.02
5.6286	7.0	119	4.9922	0.0213	0.0029	0.0213
5.6152	8.0	136	4.9504	0.0225	0.0024	0.0225
5.5797	9.0	153	4.9085	0.025	0.0078	0.025
5.5328	10.0	170	4.8686	0.02	0.0061	0.02
5.5001	11.0	187	4.8314	0.02	0.0040	0.02
5.4829	12.0	204	4.7957	0.0213	0.0042	0.0213
5.4618	13.0	221	4.7627	0.0238	0.0041	0.0238
5.4656	14.0	238	4.7332	0.0225	0.0048	0.0225
5.3806	15.0	255	4.7045	0.0225	0.0045	0.0225
5.4618	16.0	272	4.6772	0.0238	0.0070	0.0238
5.4225	17.0	289	4.6513	0.0238	0.0119	0.0238
5.3706	18.0	306	4.6222	0.0225	0.0019	0.0225
5.2885	19.0	323	4.5912	0.0262	0.0023	0.0262
5.316	20.0	340	4.5590	0.03	0.0096	0.03
5.3013	21.0	357	4.5300	0.0288	0.0058	0.0288
5.2664	22.0	374	4.4993	0.0338	0.0105	0.0338
5.2623	23.0	391	4.4663	0.0375	0.0171	0.0375
5.2142	24.0	408	4.4297	0.0387	0.0369	0.0387
5.2536	25.0	425	4.3976	0.0488	0.0490	0.0488
5.1868	26.0	442	4.3633	0.06	0.0410	0.06
5.1528	27.0	459	4.3264	0.0712	0.0407	0.0712
5.0785	28.0	476	4.2810	0.0813	0.0425	0.0813
5.1118	29.0	493	4.2302	0.0862	0.0469	0.0862
5.0422	30.0	510	4.1868	0.0862	0.0445	0.0862
5.0228	31.0	527	4.1412	0.0988	0.0608	0.0988
5.0944	32.0	544	4.1014	0.0975	0.0526	0.0975
4.9469	33.0	561	4.0542	0.1013	0.0533	0.1013
4.9508	34.0	578	4.0033	0.1113	0.0529	0.1113
4.9432	35.0	595	3.9514	0.1212	0.0597	0.1212
4.892	36.0	612	3.9022	0.1388	0.0777	0.1388
4.9077	37.0	629	3.8533	0.1562	0.0879	0.1562
4.8586	38.0	646	3.7994	0.175	0.1025	0.175
4.7948	39.0	663	3.7473	0.18	0.1213	0.18
4.7762	40.0	680	3.6931	0.1837	0.1139	0.1837
4.7045	41.0	697	3.6364	0.1875	0.1480	0.1875
4.6735	42.0	714	3.5743	0.2125	0.1617	0.2125
4.8004	43.0	731	3.5214	0.2175	0.1536	0.2175
4.7021	44.0	748	3.4643	0.23	0.1594	0.23
4.571	45.0	765	3.3994	0.2425	0.1847	0.2425
4.5619	46.0	782	3.3430	0.2525	0.1824	0.2525
4.5763	47.0	799	3.2773	0.2662	0.2000	0.2662
4.4688	48.0	816	3.2089	0.3	0.2327	0.3
4.5268	49.0	833	3.1535	0.325	0.2499	0.325
4.3921	50.0	850	3.0959	0.3312	0.2618	0.3312
4.4951	51.0	867	3.0386	0.3425	0.2850	0.3425
4.3291	52.0	884	2.9729	0.3613	0.3232	0.3613
4.2217	53.0	901	2.9069	0.38	0.3356	0.38
4.3163	54.0	918	2.8450	0.395	0.3729	0.395
4.3142	55.0	935	2.7950	0.4012	0.3691	0.4012
4.2009	56.0	952	2.7433	0.425	0.4236	0.425
4.2264	57.0	969	2.6843	0.435	0.4290	0.435
4.0387	58.0	986	2.6333	0.4537	0.4389	0.4537
4.0198	59.0	1003	2.5689	0.4637	0.4604	0.4637
3.923	60.0	1020	2.5003	0.4988	0.4808	0.4988
4.0025	61.0	1037	2.4454	0.5075	0.5031	0.5075
3.9244	62.0	1054	2.3870	0.515	0.5155	0.515
3.9832	63.0	1071	2.3416	0.5125	0.5405	0.5125
3.8662	64.0	1088	2.2830	0.5475	0.5521	0.5475
3.7148	65.0	1105	2.2209	0.5775	0.5938	0.5775
3.8023	66.0	1122	2.1719	0.585	0.5923	0.585
3.7288	67.0	1139	2.1306	0.6025	0.6299	0.6025
3.7234	68.0	1156	2.0984	0.6162	0.6104	0.6162
3.5865	69.0	1173	2.0414	0.6288	0.6568	0.6288
3.5879	70.0	1190	2.0039	0.635	0.6527	0.635
3.5789	71.0	1207	1.9412	0.6575	0.6526	0.6575
3.3691	72.0	1224	1.9034	0.6538	0.6749	0.6538
3.3963	73.0	1241	1.8521	0.6925	0.7185	0.6925
3.6271	74.0	1258	1.8206	0.7037	0.7403	0.7037
3.4418	75.0	1275	1.7760	0.7025	0.7585	0.7025
3.3528	76.0	1292	1.7394	0.7288	0.7863	0.7288
3.2153	77.0	1309	1.6872	0.7538	0.7944	0.7538
3.3631	78.0	1326	1.6494	0.7588	0.7888	0.7588
3.2948	79.0	1343	1.6112	0.7738	0.8118	0.7738
3.2066	80.0	1360	1.5629	0.7875	0.8460	0.7875
3.0508	81.0	1377	1.5229	0.7925	0.8178	0.7925
2.9769	82.0	1394	1.4812	0.7937	0.8358	0.7937
3.1443	83.0	1411	1.4413	0.8063	0.8357	0.8063
3.1914	84.0	1428	1.4119	0.8175	0.8509	0.8175
2.9267	85.0	1445	1.3753	0.8263	0.8577	0.8263
2.8616	86.0	1462	1.3347	0.8387	0.8672	0.8387
3.0522	87.0	1479	1.2967	0.835	0.8587	0.835
2.8968	88.0	1496	1.2570	0.8425	0.8626	0.8425
2.9723	89.0	1513	1.2420	0.8375	0.8727	0.8375
2.778	90.0	1530	1.2004	0.8462	0.8753	0.8462
2.6671	91.0	1547	1.1612	0.8625	0.8926	0.8625
2.569	92.0	1564	1.1356	0.8625	0.8865	0.8625
2.7768	93.0	1581	1.0915	0.8825	0.8972	0.8825
2.806	94.0	1598	1.0656	0.8825	0.9016	0.8825
2.7033	95.0	1615	1.0468	0.8862	0.9016	0.8862
2.7603	96.0	1632	1.0127	0.8875	0.9068	0.8875
2.4748	97.0	1649	0.9826	0.8988	0.9102	0.8988
2.3029	98.0	1666	0.9434	0.8975	0.9085	0.8975
2.3899	99.0	1683	0.9169	0.9	0.9120	0.9
2.3816	100.0	1700	0.8892	0.9137	0.9220	0.9137
2.4072	101.0	1717	0.8636	0.92	0.9294	0.92
2.2589	102.0	1734	0.8336	0.915	0.9238	0.915
2.3817	103.0	1751	0.8176	0.9137	0.9274	0.9137
2.1376	104.0	1768	0.7884	0.9175	0.9329	0.9175
2.1221	105.0	1785	0.7571	0.9337	0.9421	0.9337
2.271	106.0	1802	0.7340	0.9337	0.9418	0.9337
2.5291	107.0	1819	0.7216	0.9387	0.9501	0.9387
2.4104	108.0	1836	0.6987	0.9463	0.9560	0.9463
2.3537	109.0	1853	0.6863	0.9375	0.9485	0.9375
1.9392	110.0	1870	0.6611	0.9425	0.9500	0.9425
2.156	111.0	1887	0.6403	0.9513	0.9574	0.9513
2.1531	112.0	1904	0.6223	0.9437	0.9507	0.9437
1.8689	113.0	1921	0.5984	0.9537	0.9626	0.9537
1.931	114.0	1938	0.5825	0.96	0.9662	0.96
2.0533	115.0	1955	0.5672	0.9563	0.9629	0.9563
2.0569	116.0	1972	0.5536	0.96	0.9661	0.96
1.889	117.0	1989	0.5308	0.9625	0.9688	0.9625
1.88	118.0	2006	0.5142	0.9663	0.9714	0.9663
1.8098	119.0	2023	0.4982	0.9625	0.9695	0.9625
1.8574	120.0	2040	0.4769	0.9675	0.9731	0.9675
1.702	121.0	2057	0.4624	0.9675	0.9727	0.9675
1.79	122.0	2074	0.4519	0.9738	0.9775	0.9738
1.8933	123.0	2091	0.4434	0.975	0.9788	0.975
2.0305	124.0	2108	0.4380	0.9688	0.9731	0.9688
1.5548	125.0	2125	0.4202	0.9688	0.9731	0.9688
1.6695	126.0	2142	0.4081	0.975	0.9780	0.975
1.8997	127.0	2159	0.3962	0.9738	0.9774	0.9738
1.5928	128.0	2176	0.3821	0.9775	0.9803	0.9775
1.5947	129.0	2193	0.3687	0.9775	0.9812	0.9775
1.8926	130.0	2210	0.3627	0.9775	0.9802	0.9775
1.6848	131.0	2227	0.3546	0.975	0.9782	0.975
1.6906	132.0	2244	0.3428	0.9812	0.9837	0.9812
1.7161	133.0	2261	0.3409	0.9775	0.9800	0.9775
1.4407	134.0	2278	0.3260	0.9775	0.9802	0.9775
1.5738	135.0	2295	0.3204	0.9775	0.9804	0.9775
1.4839	136.0	2312	0.3122	0.9738	0.9774	0.9738
1.4471	137.0	2329	0.3048	0.975	0.9788	0.975
1.3152	138.0	2346	0.2877	0.98	0.9830	0.98
1.7391	139.0	2363	0.2864	0.9812	0.9835	0.9812
1.4966	140.0	2380	0.2740	0.9812	0.9841	0.9812
1.4046	141.0	2397	0.2671	0.9825	0.9847	0.9825
1.4232	142.0	2414	0.2584	0.9838	0.9856	0.9838
1.4188	143.0	2431	0.2547	0.9788	0.9817	0.9788
1.5474	144.0	2448	0.2466	0.9788	0.9820	0.9788
1.2907	145.0	2465	0.2350	0.985	0.9870	0.985
1.3849	146.0	2482	0.2326	0.98	0.9828	0.98
1.235	147.0	2499	0.2302	0.9838	0.9857	0.9838
1.1808	148.0	2516	0.2206	0.98	0.9826	0.98
1.112	149.0	2533	0.2135	0.9825	0.9845	0.9825
1.1792	150.0	2550	0.2058	0.9812	0.9841	0.9812
1.4527	151.0	2567	0.2029	0.98	0.9828	0.98
1.0377	152.0	2584	0.1990	0.985	0.9868	0.985
1.1226	153.0	2601	0.1918	0.98	0.9825	0.98
1.1953	154.0	2618	0.1884	0.9825	0.9847	0.9825
1.2166	155.0	2635	0.1819	0.9862	0.9878	0.9862
0.9648	156.0	2652	0.1743	0.985	0.9866	0.985
0.9714	157.0	2669	0.1687	0.9825	0.9846	0.9825
1.1184	158.0	2686	0.1647	0.9838	0.9857	0.9838
1.0298	159.0	2703	0.1614	0.9862	0.9879	0.9862
1.3791	160.0	2720	0.1591	0.985	0.9866	0.985
1.0656	161.0	2737	0.1584	0.985	0.9866	0.985
1.0959	162.0	2754	0.1579	0.9788	0.9816	0.9788
1.1084	163.0	2771	0.1505	0.9838	0.9853	0.9838
1.2448	164.0	2788	0.1478	0.9862	0.9877	0.9862
1.1163	165.0	2805	0.1399	0.9862	0.9877	0.9862
0.9339	166.0	2822	0.1379	0.985	0.9866	0.985
0.9877	167.0	2839	0.1360	0.985	0.9865	0.985
0.8369	168.0	2856	0.1337	0.985	0.9866	0.985
0.9798	169.0	2873	0.1280	0.9862	0.9877	0.9862
0.8334	170.0	2890	0.1264	0.985	0.9865	0.985
1.0522	171.0	2907	0.1246	0.9838	0.9853	0.9838
0.8917	172.0	2924	0.1220	0.9838	0.9853	0.9838
0.8981	173.0	2941	0.1190	0.9862	0.9877	0.9862
0.8627	174.0	2958	0.1171	0.9862	0.9878	0.9862
0.6803	175.0	2975	0.1129	0.9862	0.9877	0.9862
0.7849	176.0	2992	0.1074	0.9888	0.9899	0.9888
0.786	177.0	3009	0.1085	0.9862	0.9877	0.9862
0.8007	178.0	3026	0.1067	0.9875	0.9892	0.9875
0.9066	179.0	3043	0.1043	0.985	0.9866	0.985
0.6402	180.0	3060	0.1035	0.9862	0.9877	0.9862
0.7828	181.0	3077	0.0989	0.9862	0.9877	0.9862
0.7827	182.0	3094	0.0947	0.99	0.9912	0.99
0.9835	183.0	3111	0.0984	0.9862	0.9877	0.9862
0.813	184.0	3128	0.0929	0.9888	0.9901	0.9888
0.6951	185.0	3145	0.0947	0.985	0.9865	0.985
0.9447	186.0	3162	0.0902	0.9875	0.9890	0.9875
0.8786	187.0	3179	0.0892	0.9875	0.9890	0.9875
0.7047	188.0	3196	0.0867	0.99	0.9912	0.99
0.6314	189.0	3213	0.0848	0.9875	0.9890	0.9875
0.5659	190.0	3230	0.0850	0.9875	0.9890	0.9875
0.6939	191.0	3247	0.0827	0.9888	0.9901	0.9888
0.4906	192.0	3264	0.0799	0.9875	0.9890	0.9875
0.6737	193.0	3281	0.0767	0.9875	0.9890	0.9875
0.6346	194.0	3298	0.0768	0.9888	0.9901	0.9888
0.7578	195.0	3315	0.0787	0.9888	0.9901	0.9888
0.6656	196.0	3332	0.0782	0.9888	0.9901	0.9888
0.6678	197.0	3349	0.0745	0.9888	0.9901	0.9888
0.7711	198.0	3366	0.0730	0.99	0.9912	0.99
0.8162	199.0	3383	0.0715	0.99	0.9912	0.99
0.7412	200.0	3400	0.0699	0.9912	0.9923	0.9912
0.653	201.0	3417	0.0723	0.99	0.9912	0.99
0.8121	202.0	3434	0.0678	0.9925	0.9934	0.9925
0.7341	203.0	3451	0.0689	0.99	0.9912	0.99
0.512	204.0	3468	0.0651	0.9875	0.9890	0.9875
0.5349	205.0	3485	0.0657	0.9888	0.9901	0.9888
0.5944	206.0	3502	0.0630	0.9925	0.9934	0.9925
0.5799	207.0	3519	0.0630	0.9912	0.9923	0.9912
0.5224	208.0	3536	0.0632	0.9912	0.9923	0.9912
0.7081	209.0	3553	0.0605	0.9925	0.9934	0.9925
0.5899	210.0	3570	0.0618	0.9912	0.9925	0.9912
0.5593	211.0	3587	0.0608	0.99	0.9912	0.99
0.6614	212.0	3604	0.0574	0.9912	0.9923	0.9912
0.5756	213.0	3621	0.0609	0.9925	0.9934	0.9925
0.6005	214.0	3638	0.0585	0.9912	0.9923	0.9912
0.4096	215.0	3655	0.0571	0.9912	0.9923	0.9912
0.5616	216.0	3672	0.0592	0.9938	0.9945	0.9938
0.536	217.0	3689	0.0553	0.9938	0.9945	0.9938
0.4769	218.0	3706	0.0563	0.9925	0.9934	0.9925
0.4125	219.0	3723	0.0561	0.9938	0.9945	0.9938
0.5308	220.0	3740	0.0537	0.9912	0.9923	0.9912
0.3784	221.0	3757	0.0528	0.9888	0.9901	0.9888
0.4812	222.0	3774	0.0505	0.9938	0.9945	0.9938
0.4321	223.0	3791	0.0477	0.9912	0.9923	0.9912
0.4818	224.0	3808	0.0544	0.99	0.9912	0.99
0.5246	225.0	3825	0.0536	0.9925	0.9934	0.9925
0.3163	226.0	3842	0.0558	0.9912	0.9923	0.9912
0.4867	227.0	3859	0.0530	0.99	0.9912	0.99
0.5169	228.0	3876	0.0508	0.9912	0.9923	0.9912

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1