SignBart-KArSL01-100

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

• Loss: 0.1267
• Accuracy: 0.9663
• Precision: 0.9610
• Recall: 0.9663

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
5.562	1.0	17	5.0472	0.0325	0.0052	0.0325
5.6133	2.0	34	5.0397	0.035	0.0065	0.035
5.5605	3.0	51	5.0278	0.0362	0.0070	0.0362
5.6136	4.0	68	5.0097	0.04	0.0096	0.04
5.6397	5.0	85	4.9882	0.0413	0.0105	0.0413
5.599	6.0	102	4.9619	0.0425	0.0118	0.0425
5.5459	7.0	119	4.9313	0.04	0.0125	0.04
5.5607	8.0	136	4.8986	0.0338	0.0114	0.0338
5.4964	9.0	153	4.8646	0.0275	0.0085	0.0275
5.4908	10.0	170	4.8291	0.0275	0.0109	0.0275
5.4351	11.0	187	4.7901	0.0238	0.0164	0.0238
5.4165	12.0	204	4.7462	0.025	0.0162	0.025
5.4048	13.0	221	4.7024	0.0312	0.0175	0.0312
5.4152	14.0	238	4.6599	0.0325	0.0085	0.0325
5.2864	15.0	255	4.6165	0.0362	0.0086	0.0362
5.3462	16.0	272	4.5754	0.0387	0.0086	0.0387
5.3518	17.0	289	4.5359	0.0413	0.0087	0.0413
5.2461	18.0	306	4.4933	0.0437	0.0120	0.0437
5.2394	19.0	323	4.4511	0.0437	0.0116	0.0437
5.2249	20.0	340	4.4105	0.0475	0.0314	0.0475
5.1508	21.0	357	4.3675	0.0537	0.0371	0.0537
5.1685	22.0	374	4.3265	0.0563	0.0400	0.0563
5.1817	23.0	391	4.2899	0.0575	0.0404	0.0575
5.0777	24.0	408	4.2504	0.0587	0.0404	0.0587
5.1599	25.0	425	4.2116	0.0625	0.0395	0.0625
5.0864	26.0	442	4.1719	0.065	0.0421	0.065
5.0515	27.0	459	4.1325	0.0612	0.0414	0.0612
4.9674	28.0	476	4.0913	0.0638	0.0370	0.0638
4.9617	29.0	493	4.0436	0.0712	0.0582	0.0712
4.9428	30.0	510	3.9988	0.09	0.0671	0.09
4.8818	31.0	527	3.9519	0.0988	0.0771	0.0988
4.992	32.0	544	3.9080	0.1113	0.0817	0.1113
4.839	33.0	561	3.8582	0.1275	0.0758	0.1275
4.8293	34.0	578	3.8096	0.155	0.1058	0.155
4.801	35.0	595	3.7588	0.1713	0.1260	0.1713
4.7965	36.0	612	3.7063	0.1713	0.1275	0.1713
4.7663	37.0	629	3.6581	0.1775	0.1425	0.1775
4.6925	38.0	646	3.6058	0.1913	0.1469	0.1913
4.6966	39.0	663	3.5562	0.215	0.1727	0.215
4.6187	40.0	680	3.4979	0.23	0.1745	0.23
4.5934	41.0	697	3.4369	0.2425	0.1973	0.2425
4.5583	42.0	714	3.3775	0.26	0.2028	0.26
4.6838	43.0	731	3.3287	0.275	0.2205	0.275
4.5448	44.0	748	3.2708	0.2825	0.2176	0.2825
4.4167	45.0	765	3.2056	0.2925	0.2313	0.2925
4.3508	46.0	782	3.1425	0.3275	0.2617	0.3275
4.3927	47.0	799	3.0862	0.3225	0.2552	0.3225
4.2605	48.0	816	3.0236	0.3237	0.2598	0.3237
4.3863	49.0	833	2.9706	0.3375	0.2663	0.3375
4.2121	50.0	850	2.9137	0.36	0.2973	0.36
4.3204	51.0	867	2.8582	0.3638	0.3125	0.3638
4.1366	52.0	884	2.8046	0.3837	0.3331	0.3837
3.9753	53.0	901	2.7434	0.4012	0.3544	0.4012
4.1597	54.0	918	2.6869	0.4213	0.3689	0.4213
4.1189	55.0	935	2.6435	0.4275	0.4326	0.4275
3.988	56.0	952	2.5908	0.4487	0.4253	0.4487
4.0295	57.0	969	2.5272	0.475	0.4672	0.475
3.8249	58.0	986	2.4764	0.475	0.4347	0.475
3.8159	59.0	1003	2.4203	0.5	0.4827	0.5
3.725	60.0	1020	2.3593	0.52	0.4976	0.52
3.8182	61.0	1037	2.3040	0.5413	0.5410	0.5413
3.6766	62.0	1054	2.2583	0.5587	0.5559	0.5587
3.7335	63.0	1071	2.2106	0.57	0.5683	0.57
3.6035	64.0	1088	2.1644	0.58	0.5744	0.58
3.4976	65.0	1105	2.1119	0.61	0.6085	0.61
3.6297	66.0	1122	2.0670	0.6112	0.6173	0.6112
3.5512	67.0	1139	2.0209	0.6338	0.6250	0.6338
3.5176	68.0	1156	1.9753	0.6388	0.6155	0.6388
3.4259	69.0	1173	1.9319	0.6425	0.6243	0.6425
3.3951	70.0	1190	1.8801	0.6787	0.6570	0.6787
3.3621	71.0	1207	1.8443	0.69	0.6552	0.69
3.1898	72.0	1224	1.8038	0.6963	0.6576	0.6963
3.1313	73.0	1241	1.7504	0.71	0.6990	0.71
3.4151	74.0	1258	1.7151	0.7137	0.7055	0.7137
3.2875	75.0	1275	1.6800	0.7212	0.7126	0.7212
3.1179	76.0	1292	1.6471	0.7338	0.7369	0.7338
2.974	77.0	1309	1.5931	0.7338	0.7348	0.7338
3.2277	78.0	1326	1.5625	0.7412	0.7330	0.7412
3.1368	79.0	1343	1.5227	0.7525	0.7465	0.7525
3.0165	80.0	1360	1.4812	0.7625	0.7572	0.7625
2.8019	81.0	1377	1.4457	0.775	0.7709	0.775
2.8394	82.0	1394	1.4034	0.775	0.7682	0.775
2.9471	83.0	1411	1.3699	0.7913	0.7917	0.7913
3.0726	84.0	1428	1.3390	0.7975	0.7928	0.7975
2.6878	85.0	1445	1.3017	0.805	0.7994	0.805
2.6069	86.0	1462	1.2671	0.81	0.8112	0.81
2.8084	87.0	1479	1.2331	0.8225	0.8221	0.8225
2.7011	88.0	1496	1.2090	0.8175	0.8163	0.8175
2.8013	89.0	1513	1.1766	0.8213	0.8138	0.8213
2.5563	90.0	1530	1.1542	0.83	0.8205	0.83
2.5028	91.0	1547	1.1131	0.84	0.8375	0.84
2.3931	92.0	1564	1.0849	0.8462	0.8385	0.8462
2.6034	93.0	1581	1.0612	0.8462	0.8441	0.8462
2.6746	94.0	1598	1.0428	0.8488	0.8474	0.8488
2.5679	95.0	1615	1.0171	0.8512	0.8521	0.8512
2.633	96.0	1632	0.9961	0.8488	0.8439	0.8488
2.3299	97.0	1649	0.9542	0.8575	0.8493	0.8575
2.1765	98.0	1666	0.9312	0.88	0.8691	0.88
2.2522	99.0	1683	0.9171	0.875	0.8643	0.875
2.2293	100.0	1700	0.8841	0.8975	0.8886	0.8975
2.2282	101.0	1717	0.8568	0.895	0.8869	0.895
2.0781	102.0	1734	0.8388	0.895	0.8855	0.895
2.2407	103.0	1751	0.8140	0.9025	0.8916	0.9025
1.9987	104.0	1768	0.7940	0.9	0.8904	0.9
2.0723	105.0	1785	0.7795	0.9038	0.8942	0.9038
2.1523	106.0	1802	0.7599	0.9	0.8907	0.9
2.4417	107.0	1819	0.7406	0.9038	0.8924	0.9038
2.2543	108.0	1836	0.7255	0.9062	0.8957	0.9062
2.2184	109.0	1853	0.7089	0.9075	0.8944	0.9075
1.8363	110.0	1870	0.6941	0.9075	0.8976	0.9075
2.1312	111.0	1887	0.6729	0.9137	0.9009	0.9137
2.0532	112.0	1904	0.6584	0.9113	0.8989	0.9113
1.7909	113.0	1921	0.6370	0.9125	0.9009	0.9125
1.8319	114.0	1938	0.6238	0.9163	0.9022	0.9163
1.9313	115.0	1955	0.6052	0.92	0.9069	0.92
1.8851	116.0	1972	0.5927	0.9225	0.9079	0.9225
1.7736	117.0	1989	0.5805	0.9213	0.9078	0.9213
1.7361	118.0	2006	0.5626	0.9275	0.9200	0.9275
1.6962	119.0	2023	0.5439	0.925	0.9101	0.925
1.8442	120.0	2040	0.5302	0.9263	0.9101	0.9263
1.6075	121.0	2057	0.5208	0.9275	0.9096	0.9275
1.6817	122.0	2074	0.5072	0.9287	0.9116	0.9287
1.7388	123.0	2091	0.4981	0.9263	0.9106	0.9263
1.9396	124.0	2108	0.4922	0.93	0.9123	0.93
1.4371	125.0	2125	0.4801	0.9263	0.9092	0.9263
1.5801	126.0	2142	0.4722	0.9287	0.9119	0.9287
1.9101	127.0	2159	0.4581	0.9287	0.9100	0.9287
1.475	128.0	2176	0.4507	0.93	0.9119	0.93
1.526	129.0	2193	0.4317	0.9325	0.9246	0.9325
1.7532	130.0	2210	0.4288	0.9337	0.9176	0.9337
1.5806	131.0	2227	0.4201	0.9337	0.9238	0.9337
1.6819	132.0	2244	0.4187	0.9337	0.9149	0.9337
1.6106	133.0	2261	0.4062	0.9363	0.9225	0.9363
1.3866	134.0	2278	0.3990	0.9375	0.9231	0.9375
1.473	135.0	2295	0.3893	0.9313	0.9116	0.9313
1.459	136.0	2312	0.3870	0.9363	0.9222	0.9363
1.407	137.0	2329	0.3661	0.9463	0.9420	0.9463
1.2272	138.0	2346	0.3601	0.9413	0.9256	0.9413
1.7148	139.0	2363	0.3543	0.9375	0.9176	0.9375
1.4561	140.0	2380	0.3493	0.9425	0.9294	0.9425
1.3476	141.0	2397	0.3488	0.9363	0.9134	0.9363
1.3905	142.0	2414	0.3362	0.9375	0.9178	0.9375
1.3024	143.0	2431	0.3316	0.945	0.9383	0.945
1.5121	144.0	2448	0.3243	0.9363	0.9162	0.9363
1.2562	145.0	2465	0.3140	0.9513	0.9420	0.9513
1.2734	146.0	2482	0.3087	0.9437	0.9279	0.9437
1.1334	147.0	2499	0.2961	0.9513	0.9409	0.9513
1.0911	148.0	2516	0.2969	0.9437	0.9381	0.9437
1.0968	149.0	2533	0.2873	0.9537	0.9427	0.9537
1.1078	150.0	2550	0.2827	0.9475	0.9408	0.9475
1.4172	151.0	2567	0.2772	0.9525	0.9418	0.9525
0.9421	152.0	2584	0.2775	0.95	0.9408	0.95
1.0524	153.0	2601	0.2668	0.9525	0.9426	0.9525
1.1119	154.0	2618	0.2583	0.9537	0.9485	0.9537
1.1373	155.0	2635	0.2555	0.9525	0.9432	0.9525
0.9327	156.0	2652	0.2519	0.9513	0.9426	0.9513
0.8905	157.0	2669	0.2480	0.9525	0.9426	0.9525
1.0791	158.0	2686	0.2375	0.9513	0.9424	0.9513
0.9374	159.0	2703	0.2356	0.9563	0.9537	0.9563
1.2411	160.0	2720	0.2321	0.9537	0.9426	0.9537
0.9479	161.0	2737	0.2265	0.955	0.9529	0.955
1.0939	162.0	2754	0.2172	0.9575	0.9542	0.9575
1.0225	163.0	2771	0.2219	0.9537	0.9527	0.9537
1.1896	164.0	2788	0.2125	0.9587	0.9545	0.9587
1.0017	165.0	2805	0.2181	0.9587	0.9550	0.9587
0.8495	166.0	2822	0.2094	0.9613	0.9573	0.9613
0.8661	167.0	2839	0.2077	0.96	0.9569	0.96
0.7736	168.0	2856	0.2042	0.9575	0.9549	0.9575
0.9625	169.0	2873	0.1955	0.96	0.9656	0.96
0.7691	170.0	2890	0.2015	0.9575	0.9553	0.9575
0.93	171.0	2907	0.1926	0.9613	0.9565	0.9613
0.8411	172.0	2924	0.1921	0.9587	0.9557	0.9587
0.8744	173.0	2941	0.1889	0.96	0.9557	0.96
0.8333	174.0	2958	0.1800	0.9625	0.9582	0.9625
0.6494	175.0	2975	0.1895	0.9587	0.9548	0.9587
0.7433	176.0	2992	0.1839	0.9575	0.9449	0.9575
0.7542	177.0	3009	0.1784	0.96	0.9564	0.96
0.7868	178.0	3026	0.1825	0.9625	0.9552	0.9625
0.8112	179.0	3043	0.1784	0.96	0.9559	0.96
0.5844	180.0	3060	0.1691	0.9613	0.9566	0.9613
0.7427	181.0	3077	0.1678	0.96	0.9559	0.96
0.7223	182.0	3094	0.1649	0.9613	0.9564	0.9613
0.9499	183.0	3111	0.1639	0.9637	0.9685	0.9637
0.7389	184.0	3128	0.1669	0.9625	0.9554	0.9625
0.6599	185.0	3145	0.1639	0.96	0.9559	0.96
0.801	186.0	3162	0.1655	0.9637	0.9588	0.9637
0.7727	187.0	3179	0.1583	0.9637	0.9584	0.9637
0.6516	188.0	3196	0.1518	0.9637	0.9578	0.9637
0.6005	189.0	3213	0.1607	0.96	0.9557	0.96
0.5379	190.0	3230	0.1515	0.9625	0.9579	0.9625
0.5983	191.0	3247	0.1586	0.96	0.9559	0.96
0.4489	192.0	3264	0.1537	0.9625	0.9575	0.9625
0.5773	193.0	3281	0.1484	0.9625	0.9582	0.9625
0.6644	194.0	3298	0.1503	0.9637	0.9595	0.9637
0.7538	195.0	3315	0.1489	0.9663	0.9702	0.9663
0.616	196.0	3332	0.1418	0.9613	0.9566	0.9613
0.6325	197.0	3349	0.1420	0.965	0.9693	0.965
0.6072	198.0	3366	0.1454	0.965	0.9595	0.965
0.8037	199.0	3383	0.1416	0.9637	0.9584	0.9637
0.6239	200.0	3400	0.1375	0.9637	0.9586	0.9637
0.651	201.0	3417	0.1395	0.965	0.9599	0.965
0.814	202.0	3434	0.1410	0.965	0.9689	0.965
0.6626	203.0	3451	0.1381	0.9637	0.9678	0.9637
0.4598	204.0	3468	0.1362	0.9663	0.9606	0.9663
0.4721	205.0	3485	0.1335	0.9675	0.9709	0.9675
0.5892	206.0	3502	0.1328	0.9663	0.9606	0.9663
0.5871	207.0	3519	0.1398	0.9637	0.9588	0.9637
0.5096	208.0	3536	0.1275	0.965	0.9595	0.965
0.6273	209.0	3553	0.1250	0.9663	0.9698	0.9663
0.6256	210.0	3570	0.1294	0.965	0.9694	0.965
0.5191	211.0	3587	0.1397	0.965	0.96	0.965
0.6444	212.0	3604	0.1235	0.9675	0.9619	0.9675
0.5163	213.0	3621	0.1313	0.9637	0.9588	0.9637
0.4952	214.0	3638	0.1315	0.9675	0.9711	0.9675
0.3925	215.0	3655	0.1254	0.965	0.9597	0.965
0.4847	216.0	3672	0.1311	0.9675	0.9711	0.9675
0.526	217.0	3689	0.1267	0.9663	0.9610	0.9663

Framework versions

• Transformers 4.45.1
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0