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kwwww/
bert-base-uncased-test_64_100

Name: bert-base-uncased-test_64_100
Availability: InStock

pytorch

generated_from_trainer

apache-2.0

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bert-base-uncased-test_64_100

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.5041
F1: {'f1': 0.796440489432703}
Accuracy: {'accuracy': 0.7804}

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: 16
eval_batch_size: 16
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	F1	Accuracy
No log	1.0	7	0.6982	{'f1': 0.004784688995215311}	{'accuracy': 0.5008}
No log	2.0	14	0.6885	{'f1': 0.655367231638418}	{'accuracy': 0.5364}
No log	3.0	21	0.6874	{'f1': 0.6617354196301565}	{'accuracy': 0.5244}
No log	4.0	28	0.6822	{'f1': 0.6497193793331132}	{'accuracy': 0.5756}
No log	5.0	35	0.6786	{'f1': 0.5570291777188329}	{'accuracy': 0.5992}
No log	6.0	42	0.6741	{'f1': 0.6100355590675621}	{'accuracy': 0.6052}
No log	7.0	49	0.6706	{'f1': 0.4816700610997964}	{'accuracy': 0.5928}
No log	8.0	56	0.6654	{'f1': 0.48136645962732916}	{'accuracy': 0.5992}
No log	9.0	63	0.6520	{'f1': 0.564531104921077}	{'accuracy': 0.6248}
No log	10.0	70	0.6316	{'f1': 0.669652855543113}	{'accuracy': 0.646}
No log	11.0	77	0.6364	{'f1': 0.5811885821244736}	{'accuracy': 0.642}
No log	12.0	84	0.5995	{'f1': 0.6873545384018619}	{'accuracy': 0.6776}
No log	13.0	91	0.5920	{'f1': 0.6943784639746634}	{'accuracy': 0.6912}
No log	14.0	98	0.6104	{'f1': 0.673913043478261}	{'accuracy': 0.688}
No log	15.0	105	0.5986	{'f1': 0.7101621194147885}	{'accuracy': 0.7068}
No log	16.0	112	0.6088	{'f1': 0.7119205298013244}	{'accuracy': 0.7216}
No log	17.0	119	0.6098	{'f1': 0.7373776908023484}	{'accuracy': 0.7316}
No log	18.0	126	0.6355	{'f1': 0.7393403057119871}	{'accuracy': 0.7408}
No log	19.0	133	0.6611	{'f1': 0.7576766555678875}	{'accuracy': 0.738}
No log	20.0	140	0.6914	{'f1': 0.7586469023185101}	{'accuracy': 0.746}
No log	21.0	147	0.7386	{'f1': 0.7356042173560423}	{'accuracy': 0.7392}
No log	22.0	154	0.7983	{'f1': 0.7291054178916423}	{'accuracy': 0.742}
No log	23.0	161	0.7992	{'f1': 0.7565485362095531}	{'accuracy': 0.7472}
No log	24.0	168	0.8528	{'f1': 0.7316455696202532}	{'accuracy': 0.7456}
No log	25.0	175	0.8415	{'f1': 0.759592795614722}	{'accuracy': 0.7544}
No log	26.0	182	0.8850	{'f1': 0.770764119601329}	{'accuracy': 0.7516}
No log	27.0	189	0.8879	{'f1': 0.7490759753593429}	{'accuracy': 0.7556}
No log	28.0	196	0.9013	{'f1': 0.7703533026113671}	{'accuracy': 0.7608}
No log	29.0	203	1.0318	{'f1': 0.7750791974656812}	{'accuracy': 0.7444}
No log	30.0	210	0.9493	{'f1': 0.7475165562913907}	{'accuracy': 0.756}
No log	31.0	217	0.9683	{'f1': 0.7755102040816327}	{'accuracy': 0.7624}
No log	32.0	224	1.0889	{'f1': 0.7715091678420309}	{'accuracy': 0.7408}
No log	33.0	231	1.0272	{'f1': 0.7374631268436579}	{'accuracy': 0.7508}
No log	34.0	238	1.0058	{'f1': 0.7601121345614739}	{'accuracy': 0.7604}
No log	35.0	245	1.0153	{'f1': 0.7748165314793356}	{'accuracy': 0.7668}
No log	36.0	252	1.0568	{'f1': 0.7759197324414715}	{'accuracy': 0.7588}
No log	37.0	259	1.1076	{'f1': 0.7738910926794085}	{'accuracy': 0.7492}
No log	38.0	266	1.0427	{'f1': 0.7558666117743927}	{'accuracy': 0.7628}
No log	39.0	273	1.0863	{'f1': 0.7808269301134284}	{'accuracy': 0.7604}
No log	40.0	280	1.1665	{'f1': 0.7790738776952988}	{'accuracy': 0.75}
No log	41.0	287	1.0454	{'f1': 0.7766692248656946}	{'accuracy': 0.7672}
No log	42.0	294	1.0836	{'f1': 0.7832840236686389}	{'accuracy': 0.7656}
No log	43.0	301	1.1531	{'f1': 0.7792486583184258}	{'accuracy': 0.7532}
No log	44.0	308	1.0566	{'f1': 0.773688332028191}	{'accuracy': 0.7688}
No log	45.0	315	1.3225	{'f1': 0.7732971873941037}	{'accuracy': 0.7324}
No log	46.0	322	1.1126	{'f1': 0.7831192660550459}	{'accuracy': 0.7636}
No log	47.0	329	1.0526	{'f1': 0.7771653543307088}	{'accuracy': 0.7736}
No log	48.0	336	1.0584	{'f1': 0.7826777910210567}	{'accuracy': 0.7812}
No log	49.0	343	1.0694	{'f1': 0.7797527047913447}	{'accuracy': 0.772}
No log	50.0	350	1.1168	{'f1': 0.7819940476190476}	{'accuracy': 0.7656}
No log	51.0	357	1.0804	{'f1': 0.7815028901734103}	{'accuracy': 0.7732}
No log	52.0	364	1.2250	{'f1': 0.7811280595956013}	{'accuracy': 0.7532}
No log	53.0	371	1.1277	{'f1': 0.7861356932153392}	{'accuracy': 0.768}
No log	54.0	378	1.0646	{'f1': 0.7837190742218675}	{'accuracy': 0.7832}
No log	55.0	385	1.1001	{'f1': 0.7902439024390244}	{'accuracy': 0.7764}
No log	56.0	392	1.2776	{'f1': 0.7834907310248339}	{'accuracy': 0.7524}
No log	57.0	399	1.2173	{'f1': 0.7844118698605649}	{'accuracy': 0.7588}
No log	58.0	406	1.0998	{'f1': 0.7883880825057296}	{'accuracy': 0.7784}
No log	59.0	413	1.0903	{'f1': 0.7832730197024528}	{'accuracy': 0.7844}
No log	60.0	420	1.0943	{'f1': 0.7891050583657588}	{'accuracy': 0.7832}
No log	61.0	427	1.1423	{'f1': 0.7883971736705095}	{'accuracy': 0.7724}
No log	62.0	434	1.1620	{'f1': 0.7876106194690266}	{'accuracy': 0.7696}
No log	63.0	441	1.1627	{'f1': 0.7887740029542099}	{'accuracy': 0.7712}
No log	64.0	448	1.1474	{'f1': 0.7914798206278026}	{'accuracy': 0.7768}
No log	65.0	455	1.1109	{'f1': 0.7910447761194029}	{'accuracy': 0.7872}
No log	66.0	462	1.1295	{'f1': 0.7908221797323136}	{'accuracy': 0.7812}
No log	67.0	469	1.1479	{'f1': 0.7931947069943289}	{'accuracy': 0.7812}
No log	68.0	476	1.1624	{'f1': 0.7941397445529677}	{'accuracy': 0.7808}
No log	69.0	483	1.1639	{'f1': 0.7942835652500941}	{'accuracy': 0.7812}
No log	70.0	490	1.1614	{'f1': 0.7933509633547412}	{'accuracy': 0.7812}
No log	71.0	497	1.1656	{'f1': 0.7939622641509434}	{'accuracy': 0.7816}
0.1405	72.0	504	1.1664	{'f1': 0.7933509633547412}	{'accuracy': 0.7812}
0.1405	73.0	511	1.1431	{'f1': 0.7895760404511865}	{'accuracy': 0.7836}
0.1405	74.0	518	1.1435	{'f1': 0.7889763779527559}	{'accuracy': 0.7856}
0.1405	75.0	525	1.1507	{'f1': 0.7900852052672346}	{'accuracy': 0.7832}
0.1405	76.0	532	1.1787	{'f1': 0.7936627687665032}	{'accuracy': 0.7812}
0.1405	77.0	539	1.2244	{'f1': 0.7876561351947098}	{'accuracy': 0.7688}
0.1405	78.0	546	1.2429	{'f1': 0.7886297376093294}	{'accuracy': 0.768}
0.1405	79.0	553	1.2167	{'f1': 0.7912495365220615}	{'accuracy': 0.7748}
0.1405	80.0	560	1.1840	{'f1': 0.7912087912087912}	{'accuracy': 0.7796}
0.1405	81.0	567	1.1756	{'f1': 0.7903963414634146}	{'accuracy': 0.78}
0.1405	82.0	574	1.1744	{'f1': 0.7900383141762451}	{'accuracy': 0.7808}
0.1405	83.0	581	1.1755	{'f1': 0.7894131185270427}	{'accuracy': 0.7804}
0.1405	84.0	588	1.1790	{'f1': 0.7898966704936855}	{'accuracy': 0.7804}
0.1405	85.0	595	1.1849	{'f1': 0.7897943640517898}	{'accuracy': 0.7792}
0.1405	86.0	602	1.1950	{'f1': 0.7901328273244782}	{'accuracy': 0.7788}
0.1405	87.0	609	1.2082	{'f1': 0.79155672823219}	{'accuracy': 0.7788}
0.1405	88.0	616	1.2207	{'f1': 0.7940074906367041}	{'accuracy': 0.78}
0.1405	89.0	623	1.2201	{'f1': 0.7935435435435436}	{'accuracy': 0.78}
0.1405	90.0	630	1.2179	{'f1': 0.7930775018811136}	{'accuracy': 0.78}
0.1405	91.0	637	1.2169	{'f1': 0.7918552036199096}	{'accuracy': 0.7792}
0.1405	92.0	644	1.2162	{'f1': 0.7916981132075471}	{'accuracy': 0.7792}
0.1405	93.0	651	1.2158	{'f1': 0.791981845688351}	{'accuracy': 0.78}
0.1405	94.0	658	1.2570	{'f1': 0.7619453924914678}	{'accuracy': 0.7768}
0.1405	95.0	665	1.2645	{'f1': 0.7930780559646539}	{'accuracy': 0.7752}
0.1405	96.0	672	1.3050	{'f1': 0.7886710239651417}	{'accuracy': 0.7672}
0.1405	97.0	679	1.2435	{'f1': 0.7745713090757006}	{'accuracy': 0.7844}
0.1405	98.0	686	1.2083	{'f1': 0.7878548895899053}	{'accuracy': 0.7848}
0.1405	99.0	693	1.2364	{'f1': 0.7959028831562974}	{'accuracy': 0.7848}
0.1405	100.0	700	1.2611	{'f1': 0.7940074906367041}	{'accuracy': 0.78}
0.1405	101.0	707	1.2721	{'f1': 0.7926920208799404}	{'accuracy': 0.7776}
0.1405	102.0	714	1.2760	{'f1': 0.7919762258543834}	{'accuracy': 0.776}
0.1405	103.0	721	1.2535	{'f1': 0.7950263752825923}	{'accuracy': 0.7824}
0.1405	104.0	728	1.2403	{'f1': 0.7968036529680366}	{'accuracy': 0.7864}
0.1405	105.0	735	1.2373	{'f1': 0.7934200459066564}	{'accuracy': 0.784}
0.1405	106.0	742	1.2427	{'f1': 0.7963456414160639}	{'accuracy': 0.786}
0.1405	107.0	749	1.2564	{'f1': 0.7965269913174783}	{'accuracy': 0.7844}
0.1405	108.0	756	1.2666	{'f1': 0.7950450450450449}	{'accuracy': 0.7816}
0.1405	109.0	763	1.2703	{'f1': 0.7944486121530383}	{'accuracy': 0.7808}
0.1405	110.0	770	1.2737	{'f1': 0.7946026986506746}	{'accuracy': 0.7808}
0.1405	111.0	777	1.2735	{'f1': 0.7944486121530383}	{'accuracy': 0.7808}
0.1405	112.0	784	1.2749	{'f1': 0.7944486121530383}	{'accuracy': 0.7808}
0.1405	113.0	791	1.2706	{'f1': 0.7959413754227733}	{'accuracy': 0.7828}
0.1405	114.0	798	1.2579	{'f1': 0.7962049335863377}	{'accuracy': 0.7852}
0.1405	115.0	805	1.2497	{'f1': 0.7955589586523738}	{'accuracy': 0.7864}
0.1405	116.0	812	1.2481	{'f1': 0.79293123319247}	{'accuracy': 0.7844}
0.1405	117.0	819	1.2496	{'f1': 0.7785069729286301}	{'accuracy': 0.784}
0.1405	118.0	826	1.2386	{'f1': 0.7910447761194029}	{'accuracy': 0.7872}
0.1405	119.0	833	1.3391	{'f1': 0.7925493060628196}	{'accuracy': 0.7728}
0.1405	120.0	840	1.4218	{'f1': 0.7887424296401851}	{'accuracy': 0.7628}
0.1405	121.0	847	1.2557	{'f1': 0.79155260070395}	{'accuracy': 0.7868}
0.1405	122.0	854	1.2889	{'f1': 0.7707032875572202}	{'accuracy': 0.7796}
0.1405	123.0	861	1.3041	{'f1': 0.7664570230607967}	{'accuracy': 0.7772}
0.1405	124.0	868	1.4518	{'f1': 0.7878359264497878}	{'accuracy': 0.76}
0.1405	125.0	875	1.3888	{'f1': 0.7893792608539649}	{'accuracy': 0.7652}
0.1405	126.0	882	1.2520	{'f1': 0.793196752995748}	{'accuracy': 0.786}
0.1405	127.0	889	1.2419	{'f1': 0.7889245585874799}	{'accuracy': 0.7896}
0.1405	128.0	896	1.2470	{'f1': 0.7842660178426601}	{'accuracy': 0.7872}
0.1405	129.0	903	1.2449	{'f1': 0.7887323943661971}	{'accuracy': 0.79}
0.1405	130.0	910	1.2444	{'f1': 0.7938021454112039}	{'accuracy': 0.7924}
0.1405	131.0	917	1.2492	{'f1': 0.795133437990581}	{'accuracy': 0.7912}
0.1405	132.0	924	1.2570	{'f1': 0.7928765001935733}	{'accuracy': 0.786}
0.1405	133.0	931	1.2660	{'f1': 0.7923224568138196}	{'accuracy': 0.7836}
0.1405	134.0	938	1.2749	{'f1': 0.7928271652041207}	{'accuracy': 0.7828}
0.1405	135.0	945	1.2832	{'f1': 0.7934700075930143}	{'accuracy': 0.7824}
0.1405	136.0	952	1.2702	{'f1': 0.7940161104718065}	{'accuracy': 0.7852}
0.1405	137.0	959	1.2620	{'f1': 0.7939464493597206}	{'accuracy': 0.7876}
0.1405	138.0	966	1.2597	{'f1': 0.7929549902152642}	{'accuracy': 0.7884}
0.1405	139.0	973	1.3074	{'f1': 0.7920270778488154}	{'accuracy': 0.7788}
0.1405	140.0	980	1.3511	{'f1': 0.7921742340346991}	{'accuracy': 0.7748}
0.1405	141.0	987	1.4323	{'f1': 0.7901146131805157}	{'accuracy': 0.7656}
0.1405	142.0	994	1.5756	{'f1': 0.7842866988283942}	{'accuracy': 0.7496}
0.0009	143.0	1001	1.5489	{'f1': 0.7862595419847328}	{'accuracy': 0.7536}
0.0009	144.0	1008	1.3006	{'f1': 0.7898187427689936}	{'accuracy': 0.782}
0.0009	145.0	1015	1.5527	{'f1': 0.7151348879743941}	{'accuracy': 0.7508}
0.0009	146.0	1022	1.3876	{'f1': 0.751592356687898}	{'accuracy': 0.766}
0.0009	147.0	1029	1.3456	{'f1': 0.7812752219531881}	{'accuracy': 0.7832}
0.0009	148.0	1036	1.3554	{'f1': 0.7858255451713395}	{'accuracy': 0.78}
0.0009	149.0	1043	1.3710	{'f1': 0.787201233616037}	{'accuracy': 0.7792}
0.0009	150.0	1050	1.3786	{'f1': 0.7864823348694318}	{'accuracy': 0.7776}
0.0009	151.0	1057	1.3805	{'f1': 0.7863444572305331}	{'accuracy': 0.7772}
0.0009	152.0	1064	1.3809	{'f1': 0.7869481765834931}	{'accuracy': 0.778}
0.0009	153.0	1071	1.3814	{'f1': 0.7869481765834931}	{'accuracy': 0.778}
0.0009	154.0	1078	1.3823	{'f1': 0.7869481765834931}	{'accuracy': 0.778}
0.0009	155.0	1085	1.3839	{'f1': 0.7866462010744435}	{'accuracy': 0.7776}
0.0009	156.0	1092	1.3770	{'f1': 0.787037037037037}	{'accuracy': 0.7792}
0.0009	157.0	1099	1.3703	{'f1': 0.7863777089783281}	{'accuracy': 0.7792}
0.0009	158.0	1106	1.3701	{'f1': 0.7862122385747482}	{'accuracy': 0.7792}
0.0009	159.0	1113	1.3721	{'f1': 0.7862122385747482}	{'accuracy': 0.7792}
0.0009	160.0	1120	1.3742	{'f1': 0.7856037151702787}	{'accuracy': 0.7784}
0.0009	161.0	1127	1.3755	{'f1': 0.786073500967118}	{'accuracy': 0.7788}
0.0009	162.0	1134	1.3754	{'f1': 0.785907859078591}	{'accuracy': 0.7788}
0.0009	163.0	1141	1.3756	{'f1': 0.785907859078591}	{'accuracy': 0.7788}
0.0009	164.0	1148	1.3778	{'f1': 0.785907859078591}	{'accuracy': 0.7788}
0.0009	165.0	1155	1.3824	{'f1': 0.7868725868725869}	{'accuracy': 0.7792}
0.0009	166.0	1162	1.4059	{'f1': 0.7896930655551344}	{'accuracy': 0.778}
0.0009	167.0	1169	1.4486	{'f1': 0.7876534027519524}	{'accuracy': 0.7716}
0.0009	168.0	1176	1.4664	{'f1': 0.787878787878788}	{'accuracy': 0.7704}
0.0009	169.0	1183	1.4689	{'f1': 0.7880354505169868}	{'accuracy': 0.7704}
0.0009	170.0	1190	1.4654	{'f1': 0.7881700554528651}	{'accuracy': 0.7708}
0.0009	171.0	1197	1.4576	{'f1': 0.7882832777159807}	{'accuracy': 0.7716}
0.0009	172.0	1204	1.4436	{'f1': 0.7855277881387541}	{'accuracy': 0.77}
0.0009	173.0	1211	1.4326	{'f1': 0.7857410881801125}	{'accuracy': 0.7716}
0.0009	174.0	1218	1.4210	{'f1': 0.7891361750282913}	{'accuracy': 0.7764}
0.0009	175.0	1225	1.4110	{'f1': 0.790909090909091}	{'accuracy': 0.7792}
0.0009	176.0	1232	1.4059	{'f1': 0.7894937190711839}	{'accuracy': 0.7788}
0.0009	177.0	1239	1.4015	{'f1': 0.7909992372234935}	{'accuracy': 0.7808}
0.0009	178.0	1246	1.3992	{'f1': 0.7900763358778626}	{'accuracy': 0.78}
0.0009	179.0	1253	1.3985	{'f1': 0.7892925430210325}	{'accuracy': 0.7796}
0.0009	180.0	1260	1.3968	{'f1': 0.7905017234775948}	{'accuracy': 0.7812}
0.0009	181.0	1267	1.4007	{'f1': 0.7900763358778626}	{'accuracy': 0.78}
0.0009	182.0	1274	1.4019	{'f1': 0.7900763358778626}	{'accuracy': 0.78}
0.0009	183.0	1281	1.3990	{'f1': 0.7918898240244836}	{'accuracy': 0.7824}
0.0009	184.0	1288	1.3984	{'f1': 0.7921928817451205}	{'accuracy': 0.7828}
0.0009	185.0	1295	1.3986	{'f1': 0.7915708812260537}	{'accuracy': 0.7824}
0.0009	186.0	1302	1.3988	{'f1': 0.7917146144994246}	{'accuracy': 0.7828}
0.0009	187.0	1309	1.4010	{'f1': 0.7918898240244836}	{'accuracy': 0.7824}
0.0009	188.0	1316	1.4036	{'f1': 0.7923518164435946}	{'accuracy': 0.7828}
0.0009	189.0	1323	1.3966	{'f1': 0.7923076923076923}	{'accuracy': 0.784}
0.0009	190.0	1330	1.3964	{'f1': 0.7926125432858792}	{'accuracy': 0.7844}
0.0009	191.0	1337	1.3972	{'f1': 0.7926125432858792}	{'accuracy': 0.7844}
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0.0001	238.0	1666	1.3903	{'f1': 0.800599700149925}	{'accuracy': 0.7872}
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0.0001	248.0	1736	1.4153	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
0.0001	249.0	1743	1.4170	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
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0.0001	251.0	1757	1.4181	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
0.0001	252.0	1764	1.4187	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
0.0001	253.0	1771	1.4193	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
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0.0001	256.0	1792	1.4207	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
0.0001	257.0	1799	1.4206	{'f1': 0.8004459308807135}	{'accuracy': 0.7852}
0.0001	258.0	1806	1.4306	{'f1': 0.7999999999999999}	{'accuracy': 0.784}
0.0001	259.0	1813	1.4348	{'f1': 0.7994078460399704}	{'accuracy': 0.7832}
0.0001	260.0	1820	1.4273	{'f1': 0.8005941329372447}	{'accuracy': 0.7852}
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0.0001	264.0	1848	1.4270	{'f1': 0.7991071428571429}	{'accuracy': 0.784}
0.0001	265.0	1855	1.4266	{'f1': 0.7986602158541125}	{'accuracy': 0.7836}
0.0001	266.0	1862	1.4246	{'f1': 0.7986577181208054}	{'accuracy': 0.784}
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0.0001	270.0	1890	1.4295	{'f1': 0.7986602158541125}	{'accuracy': 0.7836}
0.0001	271.0	1897	1.4313	{'f1': 0.7991071428571429}	{'accuracy': 0.784}
0.0001	272.0	1904	1.4335	{'f1': 0.7992565055762082}	{'accuracy': 0.784}
0.0001	273.0	1911	1.4373	{'f1': 0.7998514667656889}	{'accuracy': 0.7844}
0.0001	274.0	1918	1.4406	{'f1': 0.8000000000000002}	{'accuracy': 0.7844}
0.0001	275.0	1925	1.4433	{'f1': 0.799110452186805}	{'accuracy': 0.7832}
0.0001	276.0	1932	1.4454	{'f1': 0.7997038134024435}	{'accuracy': 0.7836}
0.0001	277.0	1939	1.4466	{'f1': 0.7997038134024435}	{'accuracy': 0.7836}
0.0001	278.0	1946	1.4425	{'f1': 0.7995545657015591}	{'accuracy': 0.784}
0.0001	279.0	1953	1.4293	{'f1': 0.7991038088125467}	{'accuracy': 0.7848}
0.0001	280.0	1960	1.4180	{'f1': 0.7995495495495496}	{'accuracy': 0.7864}
0.0001	281.0	1967	1.4141	{'f1': 0.8004518072289157}	{'accuracy': 0.788}
0.0001	282.0	1974	1.4138	{'f1': 0.8}	{'accuracy': 0.7876}
0.0001	283.0	1981	1.4172	{'f1': 0.7999999999999999}	{'accuracy': 0.7872}
0.0001	284.0	1988	1.4209	{'f1': 0.7995495495495496}	{'accuracy': 0.7864}
0.0001	285.0	1995	1.4232	{'f1': 0.7996999249812453}	{'accuracy': 0.7864}
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0.0009	287.0	2009	1.4226	{'f1': 0.8}	{'accuracy': 0.7868}
0.0009	288.0	2016	1.4258	{'f1': 0.7994000749906262}	{'accuracy': 0.786}
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0.0003	374.0	2618	1.4711	{'f1': 0.7977653631284917}	{'accuracy': 0.7828}
0.0003	375.0	2625	1.4710	{'f1': 0.7977653631284917}	{'accuracy': 0.7828}
0.0003	376.0	2632	1.4713	{'f1': 0.7977653631284917}	{'accuracy': 0.7828}
0.0003	377.0	2639	1.4730	{'f1': 0.7974683544303798}	{'accuracy': 0.7824}
0.0003	378.0	2646	1.4732	{'f1': 0.7974683544303798}	{'accuracy': 0.7824}
0.0003	379.0	2653	1.4746	{'f1': 0.7974683544303798}	{'accuracy': 0.7824}
0.0003	380.0	2660	1.4789	{'f1': 0.7965786537746373}	{'accuracy': 0.7812}
0.0003	381.0	2667	1.4810	{'f1': 0.7967298402081011}	{'accuracy': 0.7812}
0.0003	382.0	2674	1.4821	{'f1': 0.7971768202080238}	{'accuracy': 0.7816}
0.0003	383.0	2681	1.4820	{'f1': 0.7971768202080238}	{'accuracy': 0.7816}
0.0003	384.0	2688	1.4843	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	385.0	2695	1.4862	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	386.0	2702	1.4866	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	387.0	2709	1.4868	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	388.0	2716	1.4878	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0003	389.0	2723	1.4885	{'f1': 0.7958503149314561}	{'accuracy': 0.7796}
0.0003	390.0	2730	1.4889	{'f1': 0.7958503149314561}	{'accuracy': 0.7796}
0.0003	391.0	2737	1.4888	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	392.0	2744	1.4904	{'f1': 0.7957068837897854}	{'accuracy': 0.7792}
0.0003	393.0	2751	1.4930	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	394.0	2758	1.4942	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	395.0	2765	1.4946	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	396.0	2772	1.4941	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	397.0	2779	1.4940	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	398.0	2786	1.4942	{'f1': 0.7954125046244912}	{'accuracy': 0.7788}
0.0003	399.0	2793	1.4822	{'f1': 0.7973224246931945}	{'accuracy': 0.782}
0.0003	400.0	2800	1.4770	{'f1': 0.797317436661699}	{'accuracy': 0.7824}
0.0003	401.0	2807	1.4748	{'f1': 0.7965658827920866}	{'accuracy': 0.782}
0.0003	402.0	2814	1.4739	{'f1': 0.7961165048543689}	{'accuracy': 0.7816}
0.0003	403.0	2821	1.4738	{'f1': 0.7961165048543689}	{'accuracy': 0.7816}
0.0003	404.0	2828	1.4735	{'f1': 0.7964138961524094}	{'accuracy': 0.782}
0.0003	405.0	2835	1.4730	{'f1': 0.7964138961524094}	{'accuracy': 0.782}
0.0003	406.0	2842	1.4736	{'f1': 0.7964138961524094}	{'accuracy': 0.782}
0.0003	407.0	2849	1.4725	{'f1': 0.7967115097159939}	{'accuracy': 0.7824}
0.0003	408.0	2856	1.4748	{'f1': 0.7961165048543689}	{'accuracy': 0.7816}
0.0003	409.0	2863	1.4769	{'f1': 0.7970149253731343}	{'accuracy': 0.7824}
0.0003	410.0	2870	1.4782	{'f1': 0.7974636329727713}	{'accuracy': 0.7828}
0.0003	411.0	2877	1.4788	{'f1': 0.7971662938105891}	{'accuracy': 0.7824}
0.0003	412.0	2884	1.4792	{'f1': 0.7971662938105891}	{'accuracy': 0.7824}
0.0003	413.0	2891	1.4871	{'f1': 0.7974730583426236}	{'accuracy': 0.782}
0.0003	414.0	2898	1.4949	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	415.0	2905	1.4969	{'f1': 0.7958503149314561}	{'accuracy': 0.7796}
0.0003	416.0	2912	1.4979	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0003	417.0	2919	1.4989	{'f1': 0.7949666913397484}	{'accuracy': 0.7784}
0.0003	418.0	2926	1.4976	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0003	419.0	2933	1.4964	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	420.0	2940	1.4958	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	421.0	2947	1.4959	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	422.0	2954	1.4963	{'f1': 0.7959940652818991}	{'accuracy': 0.78}
0.0003	423.0	2961	1.4968	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	424.0	2968	1.4971	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	425.0	2975	1.4971	{'f1': 0.7956989247311829}	{'accuracy': 0.7796}
0.0003	426.0	2982	1.4980	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0003	427.0	2989	1.4986	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0003	428.0	2996	1.4990	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	429.0	3003	1.4993	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	430.0	3010	1.4997	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	431.0	3017	1.5001	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	432.0	3024	1.5005	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	433.0	3031	1.5009	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	434.0	3038	1.5007	{'f1': 0.7955555555555556}	{'accuracy': 0.7792}
0.0	435.0	3045	1.4997	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	436.0	3052	1.4990	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	437.0	3059	1.4991	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	438.0	3066	1.4995	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	439.0	3073	1.4997	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	440.0	3080	1.4998	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	441.0	3087	1.4999	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	442.0	3094	1.5000	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	443.0	3101	1.5003	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	444.0	3108	1.5005	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	445.0	3115	1.5005	{'f1': 0.7961452928094884}	{'accuracy': 0.78}
0.0	446.0	3122	1.5002	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	447.0	3129	1.5000	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	448.0	3136	1.4999	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	449.0	3143	1.4999	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	450.0	3150	1.5001	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	451.0	3157	1.5003	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	452.0	3164	1.5006	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	453.0	3171	1.5007	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	454.0	3178	1.5007	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	455.0	3185	1.5007	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	456.0	3192	1.4999	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	457.0	3199	1.4997	{'f1': 0.7968808020794653}	{'accuracy': 0.7812}
0.0	458.0	3206	1.4995	{'f1': 0.7968808020794653}	{'accuracy': 0.7812}
0.0	459.0	3213	1.4996	{'f1': 0.7971768202080238}	{'accuracy': 0.7816}
0.0	460.0	3220	1.4997	{'f1': 0.7968808020794653}	{'accuracy': 0.7812}
0.0	461.0	3227	1.4999	{'f1': 0.7968808020794653}	{'accuracy': 0.7812}
0.0	462.0	3234	1.5001	{'f1': 0.7968808020794653}	{'accuracy': 0.7812}
0.0	463.0	3241	1.5006	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	464.0	3248	1.5008	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	465.0	3255	1.5010	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	466.0	3262	1.5010	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	467.0	3269	1.5012	{'f1': 0.7962894248608535}	{'accuracy': 0.7804}
0.0	468.0	3276	1.5014	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	469.0	3283	1.5015	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	470.0	3290	1.5017	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	471.0	3297	1.5020	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	472.0	3304	1.5022	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	473.0	3311	1.5023	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	474.0	3318	1.5020	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	475.0	3325	1.5020	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	476.0	3332	1.5021	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	477.0	3339	1.5020	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	478.0	3346	1.5025	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	479.0	3353	1.5029	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	480.0	3360	1.5032	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	481.0	3367	1.5035	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	482.0	3374	1.5036	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	483.0	3381	1.5036	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	484.0	3388	1.5037	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	485.0	3395	1.5037	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	486.0	3402	1.5037	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	487.0	3409	1.5036	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	488.0	3416	1.5036	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	489.0	3423	1.5030	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	490.0	3430	1.5028	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	491.0	3437	1.5027	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	492.0	3444	1.5026	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	493.0	3451	1.5026	{'f1': 0.7967359050445103}	{'accuracy': 0.7808}
0.0	494.0	3458	1.5033	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	495.0	3465	1.5041	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	496.0	3472	1.5042	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	497.0	3479	1.5041	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	498.0	3486	1.5041	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	499.0	3493	1.5041	{'f1': 0.796440489432703}	{'accuracy': 0.7804}
0.0	500.0	3500	1.5041	{'f1': 0.796440489432703}	{'accuracy': 0.7804}

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

kwwww/bert-base-uncased-test_64_100

bert-base-uncased-test_64_100

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kwwww/
bert-base-uncased-test_64_100