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snousias/
bert-base-greek-uncased-v3-finetuned-polylex

Name: bert-base-greek-uncased-v3-finetuned-polylex
Availability: InStock

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-greek-uncased-v3-finetuned-polylex

This model is a fine-tuned version of nlpaueb/bert-base-greek-uncased-v1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.2388

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: 5e-06
train_batch_size: 512
eval_batch_size: 512
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss
2.7244	1.0	13	2.6291
2.694	2.0	26	2.6311
2.7107	3.0	39	2.4722
2.6558	4.0	52	2.5651
2.6605	5.0	65	2.5038
2.6573	6.0	78	2.4791
2.6467	7.0	91	2.4887
2.5708	8.0	104	2.4863
2.5622	9.0	117	2.3389
2.5644	10.0	130	2.4384
2.5504	11.0	143	2.4352
2.5182	12.0	156	2.4158
2.5046	13.0	169	2.4234
2.4485	14.0	182	2.4289
2.473	15.0	195	2.3559
2.4475	16.0	208	2.3746
2.4487	17.0	221	2.3665
2.428	18.0	234	2.2715
2.4131	19.0	247	2.3679
2.3936	20.0	260	2.2356
2.3946	21.0	273	2.3008
2.3449	22.0	286	2.3134
2.3708	23.0	299	2.3177
2.3152	24.0	312	2.2009
2.3192	25.0	325	2.2506
2.3304	26.0	338	2.2458
2.2691	27.0	351	2.2125
2.2592	28.0	364	2.1254
2.2714	29.0	377	2.1649
2.3054	30.0	390	2.1633
2.2601	31.0	403	2.1433
2.2445	32.0	416	2.1790
2.2251	33.0	429	2.1874
2.2402	34.0	442	2.0976
2.2485	35.0	455	2.2506
2.2158	36.0	468	2.1411
2.1912	37.0	481	2.1640
2.1883	38.0	494	2.1221
2.1869	39.0	507	2.1404
2.1613	40.0	520	2.1048
2.1562	41.0	533	2.1490
2.1135	42.0	546	2.1360
2.1588	43.0	559	2.1085
2.1245	44.0	572	2.0455
2.1256	45.0	585	2.0955
2.1175	46.0	598	2.0554
2.0986	47.0	611	2.1592
2.0672	48.0	624	2.0547
2.0834	49.0	637	2.1018
2.0637	50.0	650	1.9672
2.0934	51.0	663	2.0244
2.0624	52.0	676	2.0206
2.0654	53.0	689	2.0019
2.0375	54.0	702	1.9709
2.0247	55.0	715	2.0573
2.0051	56.0	728	2.0118
2.0272	57.0	741	1.9811
1.9794	58.0	754	2.0686
2.0075	59.0	767	1.9908
1.9903	60.0	780	1.9719
1.9861	61.0	793	1.8938
1.9723	62.0	806	1.9435
1.9757	63.0	819	1.9853
1.9652	64.0	832	1.9546
1.9082	65.0	845	1.9496
1.9565	66.0	858	2.0053
1.9461	67.0	871	1.9123
1.9348	68.0	884	1.9159
1.9582	69.0	897	1.9521
1.9226	70.0	910	1.8827
1.8822	71.0	923	1.8913
1.9009	72.0	936	1.9483
1.8754	73.0	949	1.8115
1.8989	74.0	962	1.8791
1.9258	75.0	975	1.8749
1.8691	76.0	988	1.7972
1.8842	77.0	1001	1.8793
1.8788	78.0	1014	1.8586
1.8319	79.0	1027	1.8892
1.8608	80.0	1040	1.9646
1.8318	81.0	1053	1.9309
1.8437	82.0	1066	1.8566
1.8203	83.0	1079	1.8284
1.824	84.0	1092	1.7816
1.8182	85.0	1105	1.9069
1.8179	86.0	1118	1.9081
1.7929	87.0	1131	1.8425
1.8193	88.0	1144	1.8563
1.7839	89.0	1157	1.8833
1.7921	90.0	1170	1.9352
1.815	91.0	1183	1.7930
1.7462	92.0	1196	1.8891
1.7404	93.0	1209	1.7958
1.7678	94.0	1222	1.8088
1.7639	95.0	1235	1.8375
1.7631	96.0	1248	1.7708
1.766	97.0	1261	1.7981
1.7695	98.0	1274	1.7941
1.7176	99.0	1287	1.8595
1.7293	100.0	1300	1.7562
1.7483	101.0	1313	1.7352
1.7082	102.0	1326	1.8223
1.7325	103.0	1339	1.6402
1.7099	104.0	1352	1.7554
1.721	105.0	1365	1.7200
1.7241	106.0	1378	1.6550
1.6731	107.0	1391	1.7392
1.6811	108.0	1404	1.8979
1.7082	109.0	1417	1.7811
1.6629	110.0	1430	1.6550
1.6965	111.0	1443	1.7140
1.7202	112.0	1456	1.6252
1.6947	113.0	1469	1.6977
1.6858	114.0	1482	1.7298
1.6741	115.0	1495	1.7912
1.6703	116.0	1508	1.7231
1.6155	117.0	1521	1.7453
1.6872	118.0	1534	1.7383
1.6271	119.0	1547	1.7250
1.6729	120.0	1560	1.6696
1.6422	121.0	1573	1.6866
1.6668	122.0	1586	1.7051
1.5937	123.0	1599	1.7192
1.651	124.0	1612	1.7196
1.6286	125.0	1625	1.7460
1.6103	126.0	1638	1.6972
1.6341	127.0	1651	1.6315
1.6032	128.0	1664	1.7183
1.6089	129.0	1677	1.6223
1.6019	130.0	1690	1.6137
1.5891	131.0	1703	1.7023
1.597	132.0	1716	1.6243
1.5559	133.0	1729	1.6536
1.5882	134.0	1742	1.6405
1.5728	135.0	1755	1.6444
1.6019	136.0	1768	1.6513
1.571	137.0	1781	1.6124
1.5488	138.0	1794	1.6870
1.5376	139.0	1807	1.6583
1.5641	140.0	1820	1.6581
1.5722	141.0	1833	1.6245
1.5446	142.0	1846	1.5859
1.5441	143.0	1859	1.6329
1.56	144.0	1872	1.6646
1.5571	145.0	1885	1.6170
1.5331	146.0	1898	1.6471
1.5476	147.0	1911	1.6206
1.5573	148.0	1924	1.6275
1.5182	149.0	1937	1.6181
1.4818	150.0	1950	1.6150
1.5521	151.0	1963	1.5718
1.5039	152.0	1976	1.6621
1.5349	153.0	1989	1.5775
1.4856	154.0	2002	1.5395
1.5132	155.0	2015	1.6016
1.4865	156.0	2028	1.6300
1.4883	157.0	2041	1.6204
1.4763	158.0	2054	1.6024
1.497	159.0	2067	1.5947
1.4897	160.0	2080	1.5532
1.4797	161.0	2093	1.5129
1.4714	162.0	2106	1.5091
1.5007	163.0	2119	1.4499
1.5007	164.0	2132	1.5607
1.4778	165.0	2145	1.5594
1.4871	166.0	2158	1.5438
1.4718	167.0	2171	1.6030
1.487	168.0	2184	1.5506
1.464	169.0	2197	1.5435
1.4691	170.0	2210	1.5132
1.4438	171.0	2223	1.5154
1.461	172.0	2236	1.5698
1.4319	173.0	2249	1.6740
1.4798	174.0	2262	1.5586
1.4681	175.0	2275	1.5489
1.4587	176.0	2288	1.5589
1.4543	177.0	2301	1.5321
1.4611	178.0	2314	1.5495
1.4155	179.0	2327	1.5491
1.4228	180.0	2340	1.4871
1.4345	181.0	2353	1.5680
1.4325	182.0	2366	1.5306
1.404	183.0	2379	1.5051
1.4216	184.0	2392	1.5427
1.4229	185.0	2405	1.4961
1.4293	186.0	2418	1.4781
1.4318	187.0	2431	1.4869
1.4135	188.0	2444	1.5986
1.3873	189.0	2457	1.5333
1.394	190.0	2470	1.4695
1.4098	191.0	2483	1.5061
1.4605	192.0	2496	1.5591
1.3858	193.0	2509	1.4991
1.4076	194.0	2522	1.4924
1.3853	195.0	2535	1.4368
1.3562	196.0	2548	1.4973
1.3752	197.0	2561	1.4802
1.396	198.0	2574	1.5606
1.3836	199.0	2587	1.4912
1.3682	200.0	2600	1.5184
1.3821	201.0	2613	1.4448
1.3792	202.0	2626	1.4727
1.3755	203.0	2639	1.4996
1.3664	204.0	2652	1.4469
1.3574	205.0	2665	1.4670
1.3948	206.0	2678	1.5356
1.3378	207.0	2691	1.4756
1.3546	208.0	2704	1.4507
1.3547	209.0	2717	1.4986
1.3551	210.0	2730	1.4478
1.3262	211.0	2743	1.4450
1.3577	212.0	2756	1.4713
1.3698	213.0	2769	1.4404
1.3604	214.0	2782	1.3906
1.3733	215.0	2795	1.4844
1.3574	216.0	2808	1.4104
1.3227	217.0	2821	1.4512
1.3409	218.0	2834	1.4661
1.3411	219.0	2847	1.4515
1.3258	220.0	2860	1.5193
1.3194	221.0	2873	1.3303
1.332	222.0	2886	1.5037
1.3477	223.0	2899	1.4095
1.3182	224.0	2912	1.5021
1.3456	225.0	2925	1.4410
1.3082	226.0	2938	1.5183
1.3253	227.0	2951	1.3419
1.3171	228.0	2964	1.4621
1.2994	229.0	2977	1.4418
1.3039	230.0	2990	1.4845
1.3418	231.0	3003	1.5488
1.2917	232.0	3016	1.4748
1.3174	233.0	3029	1.4301
1.2929	234.0	3042	1.3867
1.3301	235.0	3055	1.5229
1.3154	236.0	3068	1.5099
1.316	237.0	3081	1.3725
1.3278	238.0	3094	1.4296
1.311	239.0	3107	1.3594
1.3149	240.0	3120	1.4675
1.3151	241.0	3133	1.3854
1.2792	242.0	3146	1.3681
1.2755	243.0	3159	1.3866
1.2748	244.0	3172	1.3623
1.2969	245.0	3185	1.3822
1.2839	246.0	3198	1.4040
1.2728	247.0	3211	1.4649
1.2853	248.0	3224	1.3486
1.3091	249.0	3237	1.3852
1.2677	250.0	3250	1.4326
1.2627	251.0	3263	1.3952
1.2794	252.0	3276	1.3576
1.2893	253.0	3289	1.3751
1.2483	254.0	3302	1.3861
1.2799	255.0	3315	1.3946
1.2774	256.0	3328	1.5043
1.2501	257.0	3341	1.3320
1.2491	258.0	3354	1.2201
1.2587	259.0	3367	1.3994
1.2771	260.0	3380	1.4088
1.2635	261.0	3393	1.3706
1.2805	262.0	3406	1.3330
1.2196	263.0	3419	1.3849
1.2485	264.0	3432	1.3607
1.2259	265.0	3445	1.3942
1.2653	266.0	3458	1.4019
1.2355	267.0	3471	1.2494
1.2687	268.0	3484	1.4765
1.2547	269.0	3497	1.3954
1.2613	270.0	3510	1.4220
1.2216	271.0	3523	1.4526
1.2494	272.0	3536	1.3749
1.2324	273.0	3549	1.4229
1.2415	274.0	3562	1.3641
1.244	275.0	3575	1.2835
1.2287	276.0	3588	1.3544
1.2151	277.0	3601	1.3578
1.2219	278.0	3614	1.3710
1.2077	279.0	3627	1.3806
1.2186	280.0	3640	1.3459
1.2053	281.0	3653	1.3691
1.2268	282.0	3666	1.2611
1.2174	283.0	3679	1.3259
1.2263	284.0	3692	1.3702
1.214	285.0	3705	1.2818
1.226	286.0	3718	1.2994
1.2331	287.0	3731	1.3583
1.2301	288.0	3744	1.3379
1.212	289.0	3757	1.3326
1.1784	290.0	3770	1.3129
1.2246	291.0	3783	1.3665
1.2156	292.0	3796	1.3132
1.2281	293.0	3809	1.3519
1.1816	294.0	3822	1.2700
1.2088	295.0	3835	1.3741
1.2216	296.0	3848	1.3393
1.2214	297.0	3861	1.3340
1.1606	298.0	3874	1.4059
1.1932	299.0	3887	1.4301
1.1879	300.0	3900	1.4612
1.2086	301.0	3913	1.2934
1.2026	302.0	3926	1.3369
1.171	303.0	3939	1.4190
1.1941	304.0	3952	1.3881
1.1981	305.0	3965	1.3244
1.1938	306.0	3978	1.3505
1.2111	307.0	3991	1.3938
1.2041	308.0	4004	1.2997
1.2012	309.0	4017	1.3563
1.1794	310.0	4030	1.3185
1.2042	311.0	4043	1.3803
1.171	312.0	4056	1.2954
1.1567	313.0	4069	1.3422
1.1743	314.0	4082	1.2974
1.205	315.0	4095	1.3749
1.1745	316.0	4108	1.4046
1.1917	317.0	4121	1.3082
1.1749	318.0	4134	1.3277
1.1897	319.0	4147	1.2651
1.1862	320.0	4160	1.3003
1.158	321.0	4173	1.3515
1.1488	322.0	4186	1.3738
1.1961	323.0	4199	1.2749
1.1884	324.0	4212	1.2602
1.1475	325.0	4225	1.2695
1.1769	326.0	4238	1.2700
1.1798	327.0	4251	1.3313
1.1643	328.0	4264	1.3272
1.1788	329.0	4277	1.2919
1.1696	330.0	4290	1.3841
1.1562	331.0	4303	1.2884
1.1734	332.0	4316	1.2844
1.1519	333.0	4329	1.3076
1.1685	334.0	4342	1.3387
1.1687	335.0	4355	1.2754
1.1349	336.0	4368	1.2932
1.1367	337.0	4381	1.2461
1.1429	338.0	4394	1.3838
1.1656	339.0	4407	1.3135
1.149	340.0	4420	1.2941
1.1412	341.0	4433	1.3608
1.1375	342.0	4446	1.2460
1.1527	343.0	4459	1.2603
1.17	344.0	4472	1.2844
1.1512	345.0	4485	1.2820
1.1404	346.0	4498	1.1912
1.167	347.0	4511	1.3074
1.1748	348.0	4524	1.3113
1.149	349.0	4537	1.2842
1.1907	350.0	4550	1.2868
1.1344	351.0	4563	1.3040
1.1368	352.0	4576	1.3053
1.1519	353.0	4589	1.2947
1.1439	354.0	4602	1.2751
1.1459	355.0	4615	1.2740
1.1673	356.0	4628	1.1941
1.1415	357.0	4641	1.2766
1.1384	358.0	4654	1.2161
1.1411	359.0	4667	1.3156
1.1362	360.0	4680	1.3086
1.1317	361.0	4693	1.3608
1.1168	362.0	4706	1.2478
1.1347	363.0	4719	1.2681
1.1553	364.0	4732	1.2650
1.1487	365.0	4745	1.3514
1.1135	366.0	4758	1.3292
1.1388	367.0	4771	1.3192
1.1818	368.0	4784	1.2937
1.1256	369.0	4797	1.3446
1.1284	370.0	4810	1.2609
1.1352	371.0	4823	1.2070
1.1151	372.0	4836	1.2035
1.1148	373.0	4849	1.3513
1.124	374.0	4862	1.2842
1.1213	375.0	4875	1.2513
1.1305	376.0	4888	1.2974
1.1271	377.0	4901	1.2245
1.1085	378.0	4914	1.2812
1.1383	379.0	4927	1.2253
1.1319	380.0	4940	1.3116
1.1268	381.0	4953	1.3139
1.1302	382.0	4966	1.2785
1.1197	383.0	4979	1.1841
1.1167	384.0	4992	1.2763
1.1279	385.0	5005	1.2248
1.1426	386.0	5018	1.1960
1.0997	387.0	5031	1.2735
1.1212	388.0	5044	1.3007
1.108	389.0	5057	1.2739
1.1189	390.0	5070	1.2446
1.1156	391.0	5083	1.2815
1.1342	392.0	5096	1.2405
1.1082	393.0	5109	1.2778
1.1091	394.0	5122	1.2683
1.1516	395.0	5135	1.1883
1.1136	396.0	5148	1.3215
1.1115	397.0	5161	1.2671
1.1356	398.0	5174	1.1809
1.1281	399.0	5187	1.2935
1.1241	400.0	5200	1.2595
1.0893	401.0	5213	1.2746
1.1045	402.0	5226	1.2215
1.1184	403.0	5239	1.1524
1.1088	404.0	5252	1.3651
1.1331	405.0	5265	1.1774
1.1151	406.0	5278	1.3032
1.1061	407.0	5291	1.3317
1.1103	408.0	5304	1.3300
1.1343	409.0	5317	1.2831
1.1056	410.0	5330	1.2202
1.1037	411.0	5343	1.3218
1.1131	412.0	5356	1.2714
1.1237	413.0	5369	1.2526
1.1241	414.0	5382	1.2328
1.1332	415.0	5395	1.2634
1.128	416.0	5408	1.3259
1.0864	417.0	5421	1.3554
1.1214	418.0	5434	1.2347
1.1177	419.0	5447	1.3417
1.0893	420.0	5460	1.2682
1.0809	421.0	5473	1.2308
1.1074	422.0	5486	1.3069
1.1101	423.0	5499	1.2414
1.1052	424.0	5512	1.2131
1.1036	425.0	5525	1.2598
1.1041	426.0	5538	1.2435
1.0916	427.0	5551	1.3050
1.1182	428.0	5564	1.2315
1.1401	429.0	5577	1.2985
1.0783	430.0	5590	1.2561
1.1023	431.0	5603	1.2018
1.0812	432.0	5616	1.2776
1.0972	433.0	5629	1.3098
1.0974	434.0	5642	1.2912
1.1034	435.0	5655	1.2527
1.1113	436.0	5668	1.3305
1.1026	437.0	5681	1.2507
1.1173	438.0	5694	1.1933
1.1059	439.0	5707	1.2326
1.1059	440.0	5720	1.3398
1.0857	441.0	5733	1.2865
1.1101	442.0	5746	1.2175
1.0909	443.0	5759	1.2195
1.0842	444.0	5772	1.1917
1.1266	445.0	5785	1.3260
1.1003	446.0	5798	1.1981
1.1132	447.0	5811	1.3115
1.1144	448.0	5824	1.2113
1.0909	449.0	5837	1.2275
1.0901	450.0	5850	1.3206
1.0966	451.0	5863	1.2821
1.1113	452.0	5876	1.2246
1.1166	453.0	5889	1.2174
1.1008	454.0	5902	1.2261
1.0867	455.0	5915	1.2382
1.0946	456.0	5928	1.2275
1.0806	457.0	5941	1.2702
1.092	458.0	5954	1.2788
1.0781	459.0	5967	1.1919
1.0708	460.0	5980	1.2266
1.0716	461.0	5993	1.2876
1.087	462.0	6006	1.2632
1.0925	463.0	6019	1.1550
1.0998	464.0	6032	1.2398
1.0816	465.0	6045	1.2901
1.0661	466.0	6058	1.2204
1.0896	467.0	6071	1.2587
1.1085	468.0	6084	1.2251
1.0629	469.0	6097	1.2259
1.0755	470.0	6110	1.1535
1.1145	471.0	6123	1.2034
1.0803	472.0	6136	1.2616
1.0787	473.0	6149	1.2261
1.1043	474.0	6162	1.2071
1.091	475.0	6175	1.2788
1.0938	476.0	6188	1.3360
1.0846	477.0	6201	1.1932
1.1042	478.0	6214	1.2023
1.0737	479.0	6227	1.2668
1.0857	480.0	6240	1.2893
1.0677	481.0	6253	1.1550
1.0779	482.0	6266	1.2404
1.0847	483.0	6279	1.2260
1.064	484.0	6292	1.2843
1.1016	485.0	6305	1.2789
1.088	486.0	6318	1.2207
1.0721	487.0	6331	1.2481
1.0622	488.0	6344	1.2128
1.0849	489.0	6357	1.2319
1.0665	490.0	6370	1.2880
1.0823	491.0	6383	1.2155
1.0956	492.0	6396	1.2121
1.0685	493.0	6409	1.1844
1.0871	494.0	6422	1.3001
1.0755	495.0	6435	1.2396
1.064	496.0	6448	1.1924
1.0898	497.0	6461	1.2475
1.0737	498.0	6474	1.2293
1.0828	499.0	6487	1.2668
1.0921	500.0	6500	1.2141

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

snousias/bert-base-greek-uncased-v3-finetuned-polylex

bert-base-greek-uncased-v3-finetuned-polylex

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

snousias/
bert-base-greek-uncased-v3-finetuned-polylex