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kwwww/
bert-base-uncased-test_64_200

Name: bert-base-uncased-test_64_200
Availability: InStock

pytorch

generated_from_trainer

apache-2.0

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bert-base-uncased-test_64_200

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: 1.2900
F1: {'f1': 0.8454581203303184}
Accuracy: {'accuracy': 0.8428}

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	13	0.6838	{'f1': 0.4936336924583742}	{'accuracy': 0.5864}
No log	2.0	26	0.6797	{'f1': 0.6062630480167014}	{'accuracy': 0.6228}
No log	3.0	39	0.6750	{'f1': 0.6275320380322448}	{'accuracy': 0.6396}
No log	4.0	52	0.6665	{'f1': 0.5731764705882353}	{'accuracy': 0.6372}
No log	5.0	65	0.6516	{'f1': 0.6677165354330709}	{'accuracy': 0.6624}
No log	6.0	78	0.6311	{'f1': 0.6467304625199363}	{'accuracy': 0.6456}
No log	7.0	91	0.6141	{'f1': 0.6289969338589575}	{'accuracy': 0.6612}
No log	8.0	104	0.6083	{'f1': 0.7377521613832855}	{'accuracy': 0.6724}
No log	9.0	117	0.5541	{'f1': 0.7028571428571428}	{'accuracy': 0.7088}
No log	10.0	130	0.5549	{'f1': 0.7053223712678496}	{'accuracy': 0.7276}
No log	11.0	143	0.5078	{'f1': 0.7664205207928487}	{'accuracy': 0.7596}
No log	12.0	156	0.5045	{'f1': 0.7668179942220389}	{'accuracy': 0.774}
No log	13.0	169	0.6035	{'f1': 0.7178538390379279}	{'accuracy': 0.756}
No log	14.0	182	0.4684	{'f1': 0.8066090874953061}	{'accuracy': 0.794}
No log	15.0	195	0.5285	{'f1': 0.8026826685492411}	{'accuracy': 0.7764}
No log	16.0	208	0.5310	{'f1': 0.7957166392092258}	{'accuracy': 0.8016}
No log	17.0	221	0.5379	{'f1': 0.804374240583232}	{'accuracy': 0.8068}
No log	18.0	234	0.5588	{'f1': 0.8113509192645884}	{'accuracy': 0.8112}
No log	19.0	247	0.6324	{'f1': 0.8022118247554233}	{'accuracy': 0.814}
No log	20.0	260	0.6471	{'f1': 0.8049090139652983}	{'accuracy': 0.8156}
No log	21.0	273	0.6830	{'f1': 0.8250180766449747}	{'accuracy': 0.8064}
No log	22.0	286	0.6541	{'f1': 0.8073541167066347}	{'accuracy': 0.8072}
No log	23.0	299	0.7929	{'f1': 0.7772887323943662}	{'accuracy': 0.7976}
No log	24.0	312	0.7272	{'f1': 0.8254558987718645}	{'accuracy': 0.8124}
No log	25.0	325	0.7526	{'f1': 0.8225205070842654}	{'accuracy': 0.8096}
No log	26.0	338	0.7777	{'f1': 0.8013245033112583}	{'accuracy': 0.808}
No log	27.0	351	0.7733	{'f1': 0.8103025347506132}	{'accuracy': 0.8144}
No log	28.0	364	0.7493	{'f1': 0.8281733746130031}	{'accuracy': 0.8224}
No log	29.0	377	0.7909	{'f1': 0.8305524657026326}	{'accuracy': 0.8172}
No log	30.0	390	0.8281	{'f1': 0.8091797705057374}	{'accuracy': 0.8204}
No log	31.0	403	0.7747	{'f1': 0.8305214723926382}	{'accuracy': 0.8232}
No log	32.0	416	0.7940	{'f1': 0.8261904761904763}	{'accuracy': 0.8248}
No log	33.0	429	0.8295	{'f1': 0.8210101010101011}	{'accuracy': 0.8228}
No log	34.0	442	0.8768	{'f1': 0.8304832713754647}	{'accuracy': 0.8176}
No log	35.0	455	0.8526	{'f1': 0.8249902987970508}	{'accuracy': 0.8196}
No log	36.0	468	0.8708	{'f1': 0.8272692454998085}	{'accuracy': 0.8196}
No log	37.0	481	0.9140	{'f1': 0.8084929225645296}	{'accuracy': 0.816}
No log	38.0	494	0.9014	{'f1': 0.8198903680501174}	{'accuracy': 0.816}
0.2225	39.0	507	0.9189	{'f1': 0.8225806451612904}	{'accuracy': 0.8152}
0.2225	40.0	520	0.9125	{'f1': 0.8178528347406514}	{'accuracy': 0.8188}
0.2225	41.0	533	0.9240	{'f1': 0.8292867981790591}	{'accuracy': 0.82}
0.2225	42.0	546	1.0189	{'f1': 0.8013757523645744}	{'accuracy': 0.8152}
0.2225	43.0	559	0.9304	{'f1': 0.8307341194370484}	{'accuracy': 0.822}
0.2225	44.0	572	0.9441	{'f1': 0.8154541718043566}	{'accuracy': 0.8204}
0.2225	45.0	585	0.9274	{'f1': 0.8325652841781874}	{'accuracy': 0.8256}
0.2225	46.0	598	0.9291	{'f1': 0.820859872611465}	{'accuracy': 0.82}
0.2225	47.0	611	0.9480	{'f1': 0.8347826086956521}	{'accuracy': 0.8252}
0.2225	48.0	624	0.9415	{'f1': 0.8215999999999999}	{'accuracy': 0.8216}
0.2225	49.0	637	0.9453	{'f1': 0.8219395866454692}	{'accuracy': 0.8208}
0.2225	50.0	650	0.9554	{'f1': 0.8202247191011236}	{'accuracy': 0.8208}
0.2225	51.0	663	0.9543	{'f1': 0.8288499025341132}	{'accuracy': 0.8244}
0.2225	52.0	676	0.9627	{'f1': 0.8257605689450809}	{'accuracy': 0.8236}
0.2225	53.0	689	0.9995	{'f1': 0.8353955755530559}	{'accuracy': 0.8244}
0.2225	54.0	702	1.0324	{'f1': 0.8162411050648808}	{'accuracy': 0.8244}
0.2225	55.0	715	0.9892	{'f1': 0.8340360291299349}	{'accuracy': 0.8268}
0.2225	56.0	728	1.0115	{'f1': 0.8368421052631578}	{'accuracy': 0.8264}
0.2225	57.0	741	0.9859	{'f1': 0.8211284513805521}	{'accuracy': 0.8212}
0.2225	58.0	754	0.9863	{'f1': 0.8229208117787505}	{'accuracy': 0.822}
0.2225	59.0	767	0.9881	{'f1': 0.8242760809202697}	{'accuracy': 0.8228}
0.2225	60.0	780	1.0011	{'f1': 0.8231017770597738}	{'accuracy': 0.8248}
0.2225	61.0	793	1.0023	{'f1': 0.8230088495575221}	{'accuracy': 0.824}
0.2225	62.0	806	1.0396	{'f1': 0.8376768428890543}	{'accuracy': 0.8256}
0.2225	63.0	819	1.0426	{'f1': 0.8186314921681782}	{'accuracy': 0.824}
0.2225	64.0	832	1.0070	{'f1': 0.8289368505195842}	{'accuracy': 0.8288}
0.2225	65.0	845	1.0800	{'f1': 0.8133445945945947}	{'accuracy': 0.8232}
0.2225	66.0	858	1.0260	{'f1': 0.8400000000000001}	{'accuracy': 0.8304}
0.2225	67.0	871	0.9988	{'f1': 0.8431975403535741}	{'accuracy': 0.8368}
0.2225	68.0	884	0.9941	{'f1': 0.8359157727453318}	{'accuracy': 0.8348}
0.2225	69.0	897	0.9941	{'f1': 0.8361045130641329}	{'accuracy': 0.8344}
0.2225	70.0	910	0.9947	{'f1': 0.8414872798434442}	{'accuracy': 0.838}
0.2225	71.0	923	0.9967	{'f1': 0.8386336866902239}	{'accuracy': 0.8356}
0.2225	72.0	936	0.9986	{'f1': 0.8368794326241136}	{'accuracy': 0.8344}
0.2225	73.0	949	0.9997	{'f1': 0.8405797101449276}	{'accuracy': 0.8372}
0.2225	74.0	962	1.0061	{'f1': 0.843484965304549}	{'accuracy': 0.8376}
0.2225	75.0	975	1.0038	{'f1': 0.8365498227648681}	{'accuracy': 0.834}
0.2225	76.0	988	1.0058	{'f1': 0.8421875}	{'accuracy': 0.8384}
0.0026	77.0	1001	1.0094	{'f1': 0.8420644159875824}	{'accuracy': 0.8372}
0.0026	78.0	1014	1.0182	{'f1': 0.8348660535785687}	{'accuracy': 0.8348}
0.0026	79.0	1027	1.0331	{'f1': 0.8284789644012945}	{'accuracy': 0.8304}
0.0026	80.0	1040	1.0148	{'f1': 0.8374851720047448}	{'accuracy': 0.8356}
0.0026	81.0	1053	1.0145	{'f1': 0.8407843137254902}	{'accuracy': 0.8376}
0.0026	82.0	1066	1.0158	{'f1': 0.8442014837953924}	{'accuracy': 0.8404}
0.0026	83.0	1079	1.0175	{'f1': 0.8450155763239875}	{'accuracy': 0.8408}
0.0026	84.0	1092	1.0210	{'f1': 0.8445475638051044}	{'accuracy': 0.8392}
0.0026	85.0	1105	1.0216	{'f1': 0.8375394321766562}	{'accuracy': 0.8352}
0.0026	86.0	1118	1.0259	{'f1': 0.8445998445998446}	{'accuracy': 0.84}
0.0026	87.0	1131	1.0277	{'f1': 0.8435797665369649}	{'accuracy': 0.8392}
0.0026	88.0	1144	1.0295	{'f1': 0.8370808678500985}	{'accuracy': 0.8348}
0.0026	89.0	1157	1.0533	{'f1': 0.8439393939393939}	{'accuracy': 0.8352}
0.0026	90.0	1170	1.0387	{'f1': 0.8324022346368716}	{'accuracy': 0.832}
0.0026	91.0	1183	1.0292	{'f1': 0.8400469299960892}	{'accuracy': 0.8364}
0.0026	92.0	1196	1.0678	{'f1': 0.8426966292134832}	{'accuracy': 0.832}
0.0026	93.0	1209	1.1813	{'f1': 0.8108340498710231}	{'accuracy': 0.824}
0.0026	94.0	1222	1.0600	{'f1': 0.8343848580441642}	{'accuracy': 0.832}
0.0026	95.0	1235	1.0988	{'f1': 0.8407978923598042}	{'accuracy': 0.8308}
0.0026	96.0	1248	1.0773	{'f1': 0.8406020841373987}	{'accuracy': 0.8348}
0.0026	97.0	1261	1.0753	{'f1': 0.8380803745610613}	{'accuracy': 0.834}
0.0026	98.0	1274	1.0759	{'f1': 0.8375}	{'accuracy': 0.8336}
0.0026	99.0	1287	1.0779	{'f1': 0.8406359053896859}	{'accuracy': 0.8356}
0.0026	100.0	1300	1.0779	{'f1': 0.8404669260700389}	{'accuracy': 0.836}
0.0026	101.0	1313	1.0856	{'f1': 0.830488289003573}	{'accuracy': 0.8292}
0.0026	102.0	1326	1.1032	{'f1': 0.8284789644012945}	{'accuracy': 0.8304}
0.0026	103.0	1339	1.0956	{'f1': 0.8302788844621515}	{'accuracy': 0.8296}
0.0026	104.0	1352	1.0922	{'f1': 0.8408385093167702}	{'accuracy': 0.836}
0.0026	105.0	1365	1.1688	{'f1': 0.823185988323603}	{'accuracy': 0.8304}
0.0026	106.0	1378	1.1361	{'f1': 0.8336579664978575}	{'accuracy': 0.8292}
0.0026	107.0	1391	1.1736	{'f1': 0.8191489361702128}	{'accuracy': 0.8232}
0.0026	108.0	1404	1.1447	{'f1': 0.8337273443656423}	{'accuracy': 0.8312}
0.0026	109.0	1417	1.1207	{'f1': 0.8367906066536203}	{'accuracy': 0.8332}
0.0026	110.0	1430	1.1612	{'f1': 0.8235782482357824}	{'accuracy': 0.83}
0.0026	111.0	1443	1.1338	{'f1': 0.82429677945373}	{'accuracy': 0.8276}
0.0026	112.0	1456	1.1216	{'f1': 0.8268921095008052}	{'accuracy': 0.828}
0.0026	113.0	1469	1.1147	{'f1': 0.8370078740157479}	{'accuracy': 0.8344}
0.0026	114.0	1482	1.1215	{'f1': 0.8283881315156376}	{'accuracy': 0.8288}
0.0026	115.0	1495	1.1465	{'f1': 0.8249282492824928}	{'accuracy': 0.8292}
0.0011	116.0	1508	1.1374	{'f1': 0.8260692464358452}	{'accuracy': 0.8292}
0.0011	117.0	1521	1.1196	{'f1': 0.8351042896497441}	{'accuracy': 0.8324}
0.0011	118.0	1534	1.1283	{'f1': 0.8363496341932999}	{'accuracy': 0.83}
0.0011	119.0	1547	1.1356	{'f1': 0.8367658276125095}	{'accuracy': 0.8288}
0.0011	120.0	1560	1.1272	{'f1': 0.8368684920940996}	{'accuracy': 0.8308}
0.0011	121.0	1573	1.1234	{'f1': 0.8345834978286616}	{'accuracy': 0.8324}
0.0011	122.0	1586	1.1260	{'f1': 0.8347826086956521}	{'accuracy': 0.8328}
0.0011	123.0	1599	1.1276	{'f1': 0.8333333333333333}	{'accuracy': 0.832}
0.0011	124.0	1612	1.1281	{'f1': 0.8341907400079145}	{'accuracy': 0.8324}
0.0011	125.0	1625	1.1284	{'f1': 0.8352429869616753}	{'accuracy': 0.8332}
0.0011	126.0	1638	1.1284	{'f1': 0.838407494145199}	{'accuracy': 0.8344}
0.0011	127.0	1651	1.1387	{'f1': 0.8358208955223881}	{'accuracy': 0.8284}
0.0011	128.0	1664	1.1407	{'f1': 0.8376003056935423}	{'accuracy': 0.83}
0.0011	129.0	1677	1.1373	{'f1': 0.83531669865643}	{'accuracy': 0.8284}
0.0011	130.0	1690	1.1350	{'f1': 0.837962962962963}	{'accuracy': 0.832}
0.0011	131.0	1703	1.2047	{'f1': 0.8214585079631181}	{'accuracy': 0.8296}
0.0011	132.0	1716	1.2042	{'f1': 0.8209205020920501}	{'accuracy': 0.8288}
0.0011	133.0	1729	1.1449	{'f1': 0.8341232227488151}	{'accuracy': 0.832}
0.0011	134.0	1742	1.1475	{'f1': 0.8383326840670042}	{'accuracy': 0.834}
0.0011	135.0	1755	1.1501	{'f1': 0.8391608391608392}	{'accuracy': 0.8344}
0.0011	136.0	1768	1.1523	{'f1': 0.8395348837209303}	{'accuracy': 0.8344}
0.0011	137.0	1781	1.1525	{'f1': 0.8389600310438494}	{'accuracy': 0.834}
0.0011	138.0	1794	1.1521	{'f1': 0.8387096774193549}	{'accuracy': 0.834}
0.0011	139.0	1807	1.1515	{'f1': 0.8324066719618745}	{'accuracy': 0.8312}
0.0011	140.0	1820	1.1557	{'f1': 0.8304609218436874}	{'accuracy': 0.8308}
0.0011	141.0	1833	1.1555	{'f1': 0.8302642113690952}	{'accuracy': 0.8304}
0.0011	142.0	1846	1.1542	{'f1': 0.8335310399367338}	{'accuracy': 0.8316}
0.0011	143.0	1859	1.1551	{'f1': 0.8344527854602923}	{'accuracy': 0.8324}
0.0011	144.0	1872	1.1559	{'f1': 0.8345834978286616}	{'accuracy': 0.8324}
0.0011	145.0	1885	1.1564	{'f1': 0.8349743993698308}	{'accuracy': 0.8324}
0.0011	146.0	1898	1.1570	{'f1': 0.8343171979535616}	{'accuracy': 0.8316}
0.0011	147.0	1911	1.1580	{'f1': 0.8363351605324981}	{'accuracy': 0.8328}
0.0011	148.0	1924	1.1590	{'f1': 0.836591086786552}	{'accuracy': 0.8328}
0.0011	149.0	1937	1.1954	{'f1': 0.8246406570841888}	{'accuracy': 0.8292}
0.0011	150.0	1950	1.1790	{'f1': 0.8298217179902755}	{'accuracy': 0.832}
0.0011	151.0	1963	1.1656	{'f1': 0.8391337973704562}	{'accuracy': 0.8336}
0.0011	152.0	1976	1.1879	{'f1': 0.8270248270248269}	{'accuracy': 0.83}
0.0011	153.0	1989	1.2103	{'f1': 0.8394216133942161}	{'accuracy': 0.8312}
0.0001	154.0	2002	1.2977	{'f1': 0.8091216216216216}	{'accuracy': 0.8192}
0.0001	155.0	2015	1.3165	{'f1': 0.8057675996607293}	{'accuracy': 0.8168}
0.0001	156.0	2028	1.2406	{'f1': 0.8365019011406845}	{'accuracy': 0.828}
0.0001	157.0	2041	1.3378	{'f1': 0.7991360691144708}	{'accuracy': 0.814}
0.0001	158.0	2054	1.2219	{'f1': 0.8351987649556156}	{'accuracy': 0.8292}
0.0001	159.0	2067	1.2792	{'f1': 0.8146605581007913}	{'accuracy': 0.822}
0.0001	160.0	2080	1.2839	{'f1': 0.8284765769088898}	{'accuracy': 0.814}
0.0001	161.0	2093	1.1911	{'f1': 0.8387841477491342}	{'accuracy': 0.8324}
0.0001	162.0	2106	1.1718	{'f1': 0.8349056603773586}	{'accuracy': 0.832}
0.0001	163.0	2119	1.2825	{'f1': 0.8401323042998898}	{'accuracy': 0.826}
0.0001	164.0	2132	1.1686	{'f1': 0.838785046728972}	{'accuracy': 0.8344}
0.0001	165.0	2145	1.2342	{'f1': 0.8240200166805671}	{'accuracy': 0.8312}
0.0001	166.0	2158	1.1921	{'f1': 0.8321108394458028}	{'accuracy': 0.8352}
0.0001	167.0	2171	1.1739	{'f1': 0.838375796178344}	{'accuracy': 0.8376}
0.0001	168.0	2184	1.1728	{'f1': 0.8421468034727704}	{'accuracy': 0.84}
0.0001	169.0	2197	1.1747	{'f1': 0.8401253918495297}	{'accuracy': 0.8368}
0.0001	170.0	2210	1.1757	{'f1': 0.8410777040218664}	{'accuracy': 0.8372}
0.0001	171.0	2223	1.1773	{'f1': 0.8403426791277258}	{'accuracy': 0.836}
0.0001	172.0	2236	1.1820	{'f1': 0.8403100775193799}	{'accuracy': 0.8352}
0.0001	173.0	2249	1.1857	{'f1': 0.8406805877803558}	{'accuracy': 0.8352}
0.0001	174.0	2262	1.1852	{'f1': 0.8405572755417957}	{'accuracy': 0.8352}
0.0001	175.0	2275	1.2516	{'f1': 0.8230383973288814}	{'accuracy': 0.8304}
0.0001	176.0	2288	1.2024	{'f1': 0.8402321083172147}	{'accuracy': 0.8348}
0.0001	177.0	2301	1.2116	{'f1': 0.8333999200958849}	{'accuracy': 0.8332}
0.0001	178.0	2314	1.4179	{'f1': 0.8014028934677773}	{'accuracy': 0.8188}
0.0001	179.0	2327	2.1209	{'f1': 0.7948051948051948}	{'accuracy': 0.7472}
0.0001	180.0	2340	1.2614	{'f1': 0.8272425249169434}	{'accuracy': 0.8336}
0.0001	181.0	2353	1.4028	{'f1': 0.8334529791816224}	{'accuracy': 0.8144}
0.0001	182.0	2366	1.2493	{'f1': 0.8324324324324325}	{'accuracy': 0.8264}
0.0001	183.0	2379	1.2906	{'f1': 0.8196319018404908}	{'accuracy': 0.8236}
0.0001	184.0	2392	1.4300	{'f1': 0.7944492627927148}	{'accuracy': 0.8104}
0.0001	185.0	2405	1.3032	{'f1': 0.8229208117787505}	{'accuracy': 0.822}
0.0001	186.0	2418	1.3187	{'f1': 0.829754601226994}	{'accuracy': 0.8224}
0.0001	187.0	2431	1.3279	{'f1': 0.83015993907083}	{'accuracy': 0.8216}
0.0001	188.0	2444	1.3209	{'f1': 0.8294365657339977}	{'accuracy': 0.822}
0.0001	189.0	2457	1.3296	{'f1': 0.8148760330578513}	{'accuracy': 0.8208}
0.0001	190.0	2470	1.3729	{'f1': 0.8358764421287683}	{'accuracy': 0.8236}
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0.0	387.0	5031	1.2818	{'f1': 0.8433925049309664}	{'accuracy': 0.8412}
0.0	388.0	5044	1.2817	{'f1': 0.8438485804416405}	{'accuracy': 0.8416}
0.0	389.0	5057	1.2817	{'f1': 0.8431836091410561}	{'accuracy': 0.8408}
0.0	390.0	5070	1.2821	{'f1': 0.8435159637366968}	{'accuracy': 0.8412}
0.0	391.0	5083	1.2836	{'f1': 0.8428120063191155}	{'accuracy': 0.8408}
0.0	392.0	5096	1.2841	{'f1': 0.8428120063191155}	{'accuracy': 0.8408}
0.0	393.0	5109	1.2845	{'f1': 0.8428120063191155}	{'accuracy': 0.8408}
0.0	394.0	5122	1.2845	{'f1': 0.8429360694554064}	{'accuracy': 0.8408}
0.0	395.0	5135	1.2844	{'f1': 0.8433925049309664}	{'accuracy': 0.8412}
0.0	396.0	5148	1.3032	{'f1': 0.8459490300494484}	{'accuracy': 0.838}
0.0	397.0	5161	1.3234	{'f1': 0.8466165413533835}	{'accuracy': 0.8368}
0.0	398.0	5174	1.3252	{'f1': 0.8468468468468469}	{'accuracy': 0.8368}
0.0	399.0	5187	1.3222	{'f1': 0.8461827754795036}	{'accuracy': 0.8364}
0.0	400.0	5200	1.3187	{'f1': 0.8455162019593068}	{'accuracy': 0.836}
0.0	401.0	5213	1.3160	{'f1': 0.8457185967559412}	{'accuracy': 0.8364}
0.0	402.0	5226	1.3146	{'f1': 0.8457185967559412}	{'accuracy': 0.8364}
0.0	403.0	5239	1.3028	{'f1': 0.8475864690231851}	{'accuracy': 0.8396}
0.0	404.0	5252	1.2936	{'f1': 0.8491577335375191}	{'accuracy': 0.8424}
0.0	405.0	5265	1.2915	{'f1': 0.8481595092024541}	{'accuracy': 0.8416}
0.0	406.0	5278	1.2910	{'f1': 0.847717683160721}	{'accuracy': 0.8412}
0.0	407.0	5291	1.2908	{'f1': 0.8483685220729367}	{'accuracy': 0.842}
0.0	408.0	5304	1.2907	{'f1': 0.8483685220729367}	{'accuracy': 0.842}
0.0	409.0	5317	1.2902	{'f1': 0.847926267281106}	{'accuracy': 0.8416}
0.0	410.0	5330	1.2896	{'f1': 0.8482520169035727}	{'accuracy': 0.842}
0.0	411.0	5343	1.2869	{'f1': 0.8491147036181678}	{'accuracy': 0.8432}
0.0	412.0	5356	1.2865	{'f1': 0.8494416634578358}	{'accuracy': 0.8436}
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0.0	415.0	5395	1.2865	{'f1': 0.8494416634578358}	{'accuracy': 0.8436}
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0.0	420.0	5460	1.2855	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
0.0	421.0	5473	1.2856	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
0.0	422.0	5486	1.2855	{'f1': 0.8480865867800541}	{'accuracy': 0.8428}
0.0	423.0	5499	1.2854	{'f1': 0.8480865867800541}	{'accuracy': 0.8428}
0.0	424.0	5512	1.2855	{'f1': 0.8480865867800541}	{'accuracy': 0.8428}
0.0	425.0	5525	1.2853	{'f1': 0.8497288923315259}	{'accuracy': 0.8448}
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0.0	427.0	5551	1.2856	{'f1': 0.849283223556761}	{'accuracy': 0.8444}
0.0	428.0	5564	1.2870	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
0.0	429.0	5577	1.2878	{'f1': 0.8482039397450754}	{'accuracy': 0.8428}
0.0	430.0	5590	1.2882	{'f1': 0.8487654320987655}	{'accuracy': 0.8432}
0.0	431.0	5603	1.2882	{'f1': 0.8483211115399459}	{'accuracy': 0.8428}
0.0	432.0	5616	1.2883	{'f1': 0.8478764478764478}	{'accuracy': 0.8424}
0.0	433.0	5629	1.2884	{'f1': 0.8483211115399459}	{'accuracy': 0.8428}
0.0	434.0	5642	1.2885	{'f1': 0.8482039397450754}	{'accuracy': 0.8428}
0.0	435.0	5655	1.2884	{'f1': 0.8482039397450754}	{'accuracy': 0.8428}
0.0	436.0	5668	1.2882	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
0.0	437.0	5681	1.2883	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
0.0	438.0	5694	1.2882	{'f1': 0.8477588871715611}	{'accuracy': 0.8424}
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0.0	441.0	5733	1.2882	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
0.0	442.0	5746	1.2883	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
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0.0	451.0	5863	1.2895	{'f1': 0.848414539829853}	{'accuracy': 0.8432}
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0.0	454.0	5902	1.2896	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
0.0	455.0	5915	1.2897	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
0.0	456.0	5928	1.2898	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
0.0	457.0	5941	1.2897	{'f1': 0.8490712074303406}	{'accuracy': 0.844}
0.0	458.0	5954	1.2894	{'f1': 0.849283223556761}	{'accuracy': 0.8444}
0.0	459.0	5967	1.2893	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	460.0	5980	1.2893	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	461.0	5993	1.2894	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	462.0	6006	1.2895	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
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0.0	468.0	6084	1.2900	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	469.0	6097	1.2901	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	470.0	6110	1.2903	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	471.0	6123	1.2905	{'f1': 0.8496124031007752}	{'accuracy': 0.8448}
0.0	472.0	6136	1.2906	{'f1': 0.8496124031007752}	{'accuracy': 0.8448}
0.0	473.0	6149	1.2908	{'f1': 0.8496124031007752}	{'accuracy': 0.8448}
0.0	474.0	6162	1.2909	{'f1': 0.8493999225706542}	{'accuracy': 0.8444}
0.0	475.0	6175	1.2910	{'f1': 0.8493999225706542}	{'accuracy': 0.8444}
0.0	476.0	6188	1.2911	{'f1': 0.8493999225706542}	{'accuracy': 0.8444}
0.0	477.0	6201	1.2912	{'f1': 0.8493999225706542}	{'accuracy': 0.8444}
0.0	478.0	6214	1.2907	{'f1': 0.8496124031007752}	{'accuracy': 0.8448}
0.0	479.0	6227	1.2906	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	480.0	6240	1.2906	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	481.0	6253	1.2906	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	482.0	6266	1.2906	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	483.0	6279	1.2907	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	484.0	6292	1.2907	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	485.0	6305	1.2908	{'f1': 0.8487199379363848}	{'accuracy': 0.844}
0.0	486.0	6318	1.2908	{'f1': 0.8491663435440093}	{'accuracy': 0.8444}
0.0	487.0	6331	1.2891	{'f1': 0.8472059398202424}	{'accuracy': 0.8436}
0.0	488.0	6344	1.2894	{'f1': 0.8464860620337652}	{'accuracy': 0.8436}
0.0	489.0	6357	1.2898	{'f1': 0.8459119496855346}	{'accuracy': 0.8432}
0.0	490.0	6370	1.2899	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	491.0	6383	1.2899	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	492.0	6396	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	493.0	6409	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	494.0	6422	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	495.0	6435	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	496.0	6448	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	497.0	6461	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	498.0	6474	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	499.0	6487	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}
0.0	500.0	6500	1.2900	{'f1': 0.8454581203303184}	{'accuracy': 0.8428}

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_200

bert-base-uncased-test_64_200

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kwwww/
bert-base-uncased-test_64_200