bert-base-uncased-test_16_535

This model is a fine-tuned version of bert-base-uncased on the glue dataset. It achieves the following results on the evaluation set:

• Loss: 3.4602
• F1: {'f1': 0.8391959798994975}
• Accuracy: {'accuracy': 0.7545541706615532}

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-05
• 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: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
No log	1.0	34	0.6290	{'f1': 0.8167895632444696}	{'accuracy': 0.6903163950143816}
No log	2.0	68	0.6274	{'f1': 0.8167895632444696}	{'accuracy': 0.6903163950143816}
No log	3.0	102	0.6252	{'f1': 0.8167895632444696}	{'accuracy': 0.6903163950143816}
No log	4.0	136	0.6251	{'f1': 0.8179239931934204}	{'accuracy': 0.6922339405560882}
No log	5.0	170	0.6246	{'f1': 0.8179239931934204}	{'accuracy': 0.6922339405560882}
No log	6.0	204	0.6136	{'f1': 0.8179239931934204}	{'accuracy': 0.6922339405560882}
No log	7.0	238	0.6178	{'f1': 0.81838819523269}	{'accuracy': 0.6931927133269415}
No log	8.0	272	0.6112	{'f1': 0.8179749715585892}	{'accuracy': 0.6931927133269415}
No log	9.0	306	0.6028	{'f1': 0.8173515981735159}	{'accuracy': 0.6931927133269415}
No log	10.0	340	0.5813	{'f1': 0.8187683284457478}	{'accuracy': 0.7037392138063279}
No log	11.0	374	0.5890	{'f1': 0.8234610917537746}	{'accuracy': 0.7085330776605945}
No log	12.0	408	0.5631	{'f1': 0.8330319469559975}	{'accuracy': 0.7344199424736337}
No log	13.0	442	0.6507	{'f1': 0.8307873090481785}	{'accuracy': 0.7238734419942474}
No log	14.0	476	0.6269	{'f1': 0.8347205707491082}	{'accuracy': 0.7334611697027804}
0.5155	15.0	510	0.5856	{'f1': 0.8366093366093366}	{'accuracy': 0.7449664429530202}
0.5155	16.0	544	0.6232	{'f1': 0.8405620036652413}	{'accuracy': 0.7497603068072867}
0.5155	17.0	578	0.6568	{'f1': 0.8360556563823351}	{'accuracy': 0.7401725790987536}
0.5155	18.0	612	0.6778	{'f1': 0.8416565164433618}	{'accuracy': 0.75071907957814}
0.5155	19.0	646	0.7373	{'f1': 0.8395657418576598}	{'accuracy': 0.7449664429530202}
0.5155	20.0	680	0.6615	{'f1': 0.8373531230674086}	{'accuracy': 0.74784276126558}
0.5155	21.0	714	0.8051	{'f1': 0.8383594692400483}	{'accuracy': 0.7430488974113135}
0.5155	22.0	748	0.7765	{'f1': 0.8386308068459658}	{'accuracy': 0.7468839884947267}
0.5155	23.0	782	0.7695	{'f1': 0.839073262366938}	{'accuracy': 0.7535953978907}
0.5155	24.0	816	0.7644	{'f1': 0.843297671491504}	{'accuracy': 0.7612655800575263}
0.5155	25.0	850	0.8090	{'f1': 0.844002486016159}	{'accuracy': 0.7593480345158198}
0.5155	26.0	884	0.8430	{'f1': 0.8419737663960025}	{'accuracy': 0.7574304889741131}
0.5155	27.0	918	0.8578	{'f1': 0.8393194706994328}	{'accuracy': 0.7555129434324065}
0.5155	28.0	952	0.8818	{'f1': 0.8409805153991201}	{'accuracy': 0.7574304889741131}
0.5155	29.0	986	0.9517	{'f1': 0.8421052631578948}	{'accuracy': 0.7555129434324065}
0.2477	30.0	1020	1.1130	{'f1': 0.8364741641337385}	{'accuracy': 0.7420901246404602}
0.2477	31.0	1054	1.0485	{'f1': 0.837954405422058}	{'accuracy': 0.74784276126558}
0.2477	32.0	1088	1.0609	{'f1': 0.8381070983810709}	{'accuracy': 0.75071907957814}
0.2477	33.0	1122	0.9798	{'f1': 0.8361290322580646}	{'accuracy': 0.7564717162032598}
0.2477	34.0	1156	1.0213	{'f1': 0.843650287172942}	{'accuracy': 0.7651006711409396}
0.2477	35.0	1190	1.1001	{'f1': 0.8452830188679246}	{'accuracy': 0.7641418983700863}
0.2477	36.0	1224	1.1814	{'f1': 0.8402734617775015}	{'accuracy': 0.7535953978907}
0.2477	37.0	1258	1.1825	{'f1': 0.8431618569636137}	{'accuracy': 0.7603068072866731}
0.2477	38.0	1292	1.2292	{'f1': 0.8449466415568111}	{'accuracy': 0.763183125599233}
0.2477	39.0	1326	1.1645	{'f1': 0.8372395833333335}	{'accuracy': 0.7603068072866731}
0.2477	40.0	1360	1.3783	{'f1': 0.8405438813349814}	{'accuracy': 0.7526366251198466}
0.2477	41.0	1394	1.4388	{'f1': 0.8418461538461539}	{'accuracy': 0.7535953978907}
0.2477	42.0	1428	1.4159	{'f1': 0.8412600370599136}	{'accuracy': 0.7535953978907}
0.2477	43.0	1462	1.4079	{'f1': 0.8421052631578948}	{'accuracy': 0.7583892617449665}
0.2477	44.0	1496	1.3515	{'f1': 0.839821314613912}	{'accuracy': 0.7593480345158198}
0.0896	45.0	1530	1.5365	{'f1': 0.8408113091579594}	{'accuracy': 0.7516778523489933}
0.0896	46.0	1564	1.5318	{'f1': 0.8394296342219467}	{'accuracy': 0.7516778523489933}
0.0896	47.0	1598	1.4766	{'f1': 0.843297671491504}	{'accuracy': 0.7612655800575263}
0.0896	48.0	1632	1.4258	{'f1': 0.8437900128040973}	{'accuracy': 0.7660594439117929}
0.0896	49.0	1666	1.4192	{'f1': 0.845518118245391}	{'accuracy': 0.7670182166826462}
0.0896	50.0	1700	1.4329	{'f1': 0.8419023136246786}	{'accuracy': 0.7641418983700863}
0.0896	51.0	1734	1.6575	{'f1': 0.8423645320197044}	{'accuracy': 0.7545541706615532}
0.0896	52.0	1768	1.5449	{'f1': 0.8377358490566037}	{'accuracy': 0.7526366251198466}
0.0896	53.0	1802	1.5137	{'f1': 0.8401534526854221}	{'accuracy': 0.7603068072866731}
0.0896	54.0	1836	1.5939	{'f1': 0.8385876418663304}	{'accuracy': 0.7545541706615532}
0.0896	55.0	1870	1.6615	{'f1': 0.8384279475982533}	{'accuracy': 0.7516778523489933}
0.0896	56.0	1904	1.6269	{'f1': 0.8382261086820737}	{'accuracy': 0.7516778523489933}
0.0896	57.0	1938	1.7063	{'f1': 0.8371517027863778}	{'accuracy': 0.74784276126558}
0.0896	58.0	1972	1.6755	{'f1': 0.837468982630273}	{'accuracy': 0.7488015340364333}
0.0471	59.0	2006	1.8673	{'f1': 0.833029751062538}	{'accuracy': 0.7363374880153404}
0.0471	60.0	2040	1.6474	{'f1': 0.8434343434343433}	{'accuracy': 0.7622243528283796}
0.0471	61.0	2074	1.6060	{'f1': 0.8400770712909442}	{'accuracy': 0.7612655800575263}
0.0471	62.0	2108	1.6977	{'f1': 0.8417721518987342}	{'accuracy': 0.7603068072866731}
0.0471	63.0	2142	1.8631	{'f1': 0.8386699507389164}	{'accuracy': 0.7488015340364333}
0.0471	64.0	2176	1.7244	{'f1': 0.8383005707038681}	{'accuracy': 0.7555129434324065}
0.0471	65.0	2210	1.7629	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0471	66.0	2244	1.8566	{'f1': 0.8362282878411912}	{'accuracy': 0.7468839884947267}
0.0471	67.0	2278	1.9643	{'f1': 0.8364972443355787}	{'accuracy': 0.7440076701821668}
0.0471	68.0	2312	1.9447	{'f1': 0.8346650276582667}	{'accuracy': 0.7420901246404602}
0.0471	69.0	2346	1.7232	{'f1': 0.8421725239616613}	{'accuracy': 0.763183125599233}
0.0471	70.0	2380	1.7151	{'f1': 0.8384615384615385}	{'accuracy': 0.7583892617449665}
0.0471	71.0	2414	1.7411	{'f1': 0.8368253968253969}	{'accuracy': 0.7535953978907}
0.0471	72.0	2448	1.7967	{'f1': 0.8404522613065327}	{'accuracy': 0.7564717162032598}
0.0471	73.0	2482	1.8218	{'f1': 0.8423028785982478}	{'accuracy': 0.7583892617449665}
0.0278	74.0	2516	1.7536	{'f1': 0.8346153846153845}	{'accuracy': 0.7526366251198466}
0.0278	75.0	2550	1.8290	{'f1': 0.8415779586725111}	{'accuracy': 0.7574304889741131}
0.0278	76.0	2584	1.9225	{'f1': 0.8405438813349814}	{'accuracy': 0.7526366251198466}
0.0278	77.0	2618	2.0916	{'f1': 0.8350578210590383}	{'accuracy': 0.7401725790987536}
0.0278	78.0	2652	1.7630	{'f1': 0.8355899419729207}	{'accuracy': 0.7555129434324065}
0.0278	79.0	2686	1.9106	{'f1': 0.8421052631578948}	{'accuracy': 0.7583892617449665}
0.0278	80.0	2720	1.9050	{'f1': 0.8446115288220551}	{'accuracy': 0.7622243528283796}
0.0278	81.0	2754	1.9276	{'f1': 0.8450000000000001}	{'accuracy': 0.7622243528283796}
0.0278	82.0	2788	1.9414	{'f1': 0.8442776735459662}	{'accuracy': 0.7612655800575263}
0.0278	83.0	2822	2.0313	{'f1': 0.8419094854308741}	{'accuracy': 0.7555129434324065}
0.0278	84.0	2856	2.0073	{'f1': 0.8432835820895522}	{'accuracy': 0.7583892617449665}
0.0278	85.0	2890	1.9976	{'f1': 0.8434782608695651}	{'accuracy': 0.7583892617449665}
0.0278	86.0	2924	1.8581	{'f1': 0.8375080282594733}	{'accuracy': 0.7574304889741131}
0.0278	87.0	2958	1.9573	{'f1': 0.8411801632140615}	{'accuracy': 0.7574304889741131}
0.0278	88.0	2992	1.9758	{'f1': 0.8456712672521958}	{'accuracy': 0.7641418983700863}
0.0146	89.0	3026	2.1224	{'f1': 0.8405438813349814}	{'accuracy': 0.7526366251198466}
0.0146	90.0	3060	2.0026	{'f1': 0.8418399495904222}	{'accuracy': 0.7593480345158198}
0.0146	91.0	3094	2.1244	{'f1': 0.8402220851326342}	{'accuracy': 0.7516778523489933}
0.0146	92.0	3128	2.0497	{'f1': 0.8402734617775015}	{'accuracy': 0.7535953978907}
0.0146	93.0	3162	1.8572	{'f1': 0.8289473684210527}	{'accuracy': 0.75071907957814}
0.0146	94.0	3196	2.0854	{'f1': 0.8418430884184309}	{'accuracy': 0.7564717162032598}
0.0146	95.0	3230	2.0927	{'f1': 0.8417132216014898}	{'accuracy': 0.7555129434324065}
0.0146	96.0	3264	2.0346	{'f1': 0.8404522613065327}	{'accuracy': 0.7564717162032598}
0.0146	97.0	3298	1.9551	{'f1': 0.8385449904275686}	{'accuracy': 0.7574304889741131}
0.0146	98.0	3332	1.9431	{'f1': 0.8416347381864623}	{'accuracy': 0.7622243528283796}
0.0146	99.0	3366	2.1328	{'f1': 0.8427594779366064}	{'accuracy': 0.7574304889741131}
0.0146	100.0	3400	1.9871	{'f1': 0.8400254939451881}	{'accuracy': 0.7593480345158198}
0.0146	101.0	3434	2.0223	{'f1': 0.8399746995572422}	{'accuracy': 0.7574304889741131}
0.0146	102.0	3468	2.0622	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0203	103.0	3502	2.0059	{'f1': 0.8420382165605096}	{'accuracy': 0.7622243528283796}
0.0203	104.0	3536	2.1729	{'f1': 0.8399503722084367}	{'accuracy': 0.7526366251198466}
0.0203	105.0	3570	1.9968	{'f1': 0.8386683738796414}	{'accuracy': 0.7583892617449665}
0.0203	106.0	3604	2.1000	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0203	107.0	3638	1.9776	{'f1': 0.8380462724935733}	{'accuracy': 0.7583892617449665}
0.0203	108.0	3672	2.1636	{'f1': 0.842170929507174}	{'accuracy': 0.7574304889741131}
0.0203	109.0	3706	2.0026	{'f1': 0.8353619474695709}	{'accuracy': 0.7535953978907}
0.0203	110.0	3740	2.0159	{'f1': 0.8389560789306174}	{'accuracy': 0.7574304889741131}
0.0203	111.0	3774	2.0359	{'f1': 0.8349267049075845}	{'accuracy': 0.7516778523489933}
0.0203	112.0	3808	2.1721	{'f1': 0.8409937888198759}	{'accuracy': 0.7545541706615532}
0.0203	113.0	3842	2.1287	{'f1': 0.840251572327044}	{'accuracy': 0.7564717162032598}
0.0203	114.0	3876	2.1114	{'f1': 0.8405797101449275}	{'accuracy': 0.7574304889741131}
0.0203	115.0	3910	2.1644	{'f1': 0.841708542713568}	{'accuracy': 0.7583892617449665}
0.0203	116.0	3944	2.2183	{'f1': 0.839030453697949}	{'accuracy': 0.7516778523489933}
0.0203	117.0	3978	2.0891	{'f1': 0.8385053831538949}	{'accuracy': 0.7555129434324065}
0.0155	118.0	4012	2.0074	{'f1': 0.8385012919896642}	{'accuracy': 0.7603068072866731}
0.0155	119.0	4046	2.1334	{'f1': 0.8401253918495297}	{'accuracy': 0.7555129434324065}
0.0155	120.0	4080	2.0441	{'f1': 0.8395378690629013}	{'accuracy': 0.7603068072866731}
0.0155	121.0	4114	2.0961	{'f1': 0.8376830044557607}	{'accuracy': 0.7555129434324065}
0.0155	122.0	4148	2.1073	{'f1': 0.8384223918575064}	{'accuracy': 0.7564717162032598}
0.0155	123.0	4182	2.1261	{'f1': 0.8384223918575064}	{'accuracy': 0.7564717162032598}
0.0155	124.0	4216	2.2520	{'f1': 0.8381897086174829}	{'accuracy': 0.7497603068072867}
0.0155	125.0	4250	2.0553	{'f1': 0.8349641226353556}	{'accuracy': 0.7574304889741131}
0.0155	126.0	4284	2.2367	{'f1': 0.8381070983810709}	{'accuracy': 0.75071907957814}
0.0155	127.0	4318	2.3076	{'f1': 0.8393846153846154}	{'accuracy': 0.7497603068072867}
0.0155	128.0	4352	2.1961	{'f1': 0.835538752362949}	{'accuracy': 0.7497603068072867}
0.0155	129.0	4386	2.0988	{'f1': 0.8360337005832793}	{'accuracy': 0.7574304889741131}
0.0155	130.0	4420	2.1998	{'f1': 0.842039018250472}	{'accuracy': 0.7593480345158198}
0.0155	131.0	4454	2.1090	{'f1': 0.8377504848093084}	{'accuracy': 0.7593480345158198}
0.0155	132.0	4488	2.1998	{'f1': 0.8417721518987342}	{'accuracy': 0.7603068072866731}
0.0106	133.0	4522	2.1535	{'f1': 0.839821314613912}	{'accuracy': 0.7593480345158198}
0.0106	134.0	4556	2.2231	{'f1': 0.8437499999999999}	{'accuracy': 0.7603068072866731}
0.0106	135.0	4590	2.1219	{'f1': 0.834190231362468}	{'accuracy': 0.7526366251198466}
0.0106	136.0	4624	2.1088	{'f1': 0.8332251784555484}	{'accuracy': 0.7535953978907}
0.0106	137.0	4658	2.2496	{'f1': 0.8395522388059702}	{'accuracy': 0.7526366251198466}
0.0106	138.0	4692	2.1308	{'f1': 0.8385449904275686}	{'accuracy': 0.7574304889741131}
0.0106	139.0	4726	2.2872	{'f1': 0.8399503722084367}	{'accuracy': 0.7526366251198466}
0.0106	140.0	4760	2.1879	{'f1': 0.8409090909090908}	{'accuracy': 0.7583892617449665}
0.0106	141.0	4794	2.1646	{'f1': 0.841300191204589}	{'accuracy': 0.7612655800575263}
0.0106	142.0	4828	2.1105	{'f1': 0.8339869281045752}	{'accuracy': 0.7564717162032598}
0.0106	143.0	4862	2.2799	{'f1': 0.8411801632140615}	{'accuracy': 0.7574304889741131}
0.0106	144.0	4896	2.1721	{'f1': 0.8330084470435347}	{'accuracy': 0.7535953978907}
0.0106	145.0	4930	2.2596	{'f1': 0.8400503778337531}	{'accuracy': 0.7564717162032598}
0.0106	146.0	4964	2.2096	{'f1': 0.8394113883557262}	{'accuracy': 0.7593480345158198}
0.0106	147.0	4998	2.1835	{'f1': 0.8390433096315449}	{'accuracy': 0.7612655800575263}
0.0098	148.0	5032	2.2462	{'f1': 0.844106463878327}	{'accuracy': 0.7641418983700863}
0.0098	149.0	5066	2.1821	{'f1': 0.8371187540558079}	{'accuracy': 0.7593480345158198}
0.0098	150.0	5100	2.2533	{'f1': 0.846641318124208}	{'accuracy': 0.7679769894534996}
0.0098	151.0	5134	2.3029	{'f1': 0.8427672955974843}	{'accuracy': 0.7603068072866731}
0.0098	152.0	5168	2.2849	{'f1': 0.8412397216951296}	{'accuracy': 0.7593480345158198}
0.0098	153.0	5202	2.3098	{'f1': 0.8446115288220551}	{'accuracy': 0.7622243528283796}
0.0098	154.0	5236	2.3253	{'f1': 0.8446115288220551}	{'accuracy': 0.7622243528283796}
0.0098	155.0	5270	2.3003	{'f1': 0.8444165621079048}	{'accuracy': 0.7622243528283796}
0.0098	156.0	5304	2.3194	{'f1': 0.8455790784557908}	{'accuracy': 0.7622243528283796}
0.0098	157.0	5338	2.3352	{'f1': 0.8471615720524018}	{'accuracy': 0.7651006711409396}
0.0098	158.0	5372	2.2277	{'f1': 0.8406169665809768}	{'accuracy': 0.7622243528283796}
0.0098	159.0	5406	2.3006	{'f1': 0.8426395939086295}	{'accuracy': 0.7622243528283796}
0.0098	160.0	5440	2.2040	{'f1': 0.8339869281045752}	{'accuracy': 0.7564717162032598}
0.0098	161.0	5474	2.1621	{'f1': 0.8347485303723058}	{'accuracy': 0.7574304889741131}
0.0109	162.0	5508	2.2833	{'f1': 0.8419047619047619}	{'accuracy': 0.7612655800575263}
0.0109	163.0	5542	2.3822	{'f1': 0.8446662507797879}	{'accuracy': 0.7612655800575263}
0.0109	164.0	5576	2.4010	{'f1': 0.8452454940957116}	{'accuracy': 0.7612655800575263}
0.0109	165.0	5610	2.2678	{'f1': 0.8393316195372751}	{'accuracy': 0.7603068072866731}
0.0109	166.0	5644	2.3334	{'f1': 0.8429752066115702}	{'accuracy': 0.763183125599233}
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0.0035	1021.0	34714	3.0977	{'f1': 0.8389993585631816}	{'accuracy': 0.7593480345158198}
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0.0035	1024.0	34816	3.0620	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0035	1025.0	34850	3.0627	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0035	1026.0	34884	3.0639	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0035	1027.0	34918	3.0649	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0035	1028.0	34952	3.0655	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0035	1029.0	34986	3.0662	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0005	1030.0	35020	3.0774	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
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0.0014	1089.0	37026	3.3823	{'f1': 0.8382716049382717}	{'accuracy': 0.7488015340364333}
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0.0002	1250.0	42500	3.6318	{'f1': 0.8372670807453417}	{'accuracy': 0.7488015340364333}
0.0002	1251.0	42534	3.5856	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1252.0	42568	3.5843	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1253.0	42602	3.5852	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1254.0	42636	3.5853	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1255.0	42670	3.5857	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1256.0	42704	3.5859	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1257.0	42738	3.5858	{'f1': 0.8359326263256395}	{'accuracy': 0.74784276126558}
0.0002	1258.0	42772	3.5470	{'f1': 0.8383084577114429}	{'accuracy': 0.75071907957814}
0.0002	1259.0	42806	3.2429	{'f1': 0.8412184057031757}	{'accuracy': 0.7651006711409396}
0.0002	1260.0	42840	3.3302	{'f1': 0.8425096030729833}	{'accuracy': 0.7641418983700863}
0.0002	1261.0	42874	3.4607	{'f1': 0.8379396984924623}	{'accuracy': 0.7526366251198466}
0.0002	1262.0	42908	3.4639	{'f1': 0.8379396984924623}	{'accuracy': 0.7526366251198466}
0.0002	1263.0	42942	3.2307	{'f1': 0.8427835051546391}	{'accuracy': 0.7660594439117929}
0.0002	1264.0	42976	3.1433	{'f1': 0.8409387222946545}	{'accuracy': 0.7660594439117929}
0.0034	1265.0	43010	3.1429	{'f1': 0.8409387222946545}	{'accuracy': 0.7660594439117929}
0.0034	1266.0	43044	3.1814	{'f1': 0.8405422853453841}	{'accuracy': 0.763183125599233}
0.0034	1267.0	43078	3.1860	{'f1': 0.8405422853453841}	{'accuracy': 0.763183125599233}
0.0034	1268.0	43112	3.1868	{'f1': 0.8405422853453841}	{'accuracy': 0.763183125599233}
0.0034	1269.0	43146	3.1928	{'f1': 0.8407479045776919}	{'accuracy': 0.763183125599233}
0.0034	1270.0	43180	3.1940	{'f1': 0.8407479045776919}	{'accuracy': 0.763183125599233}
0.0034	1271.0	43214	3.1958	{'f1': 0.8402061855670104}	{'accuracy': 0.7622243528283796}
0.0034	1272.0	43248	3.1996	{'f1': 0.8402061855670104}	{'accuracy': 0.7622243528283796}
0.0034	1273.0	43282	3.2036	{'f1': 0.840952994204765}	{'accuracy': 0.763183125599233}
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0.0034	1275.0	43350	3.2090	{'f1': 0.840952994204765}	{'accuracy': 0.763183125599233}
0.0034	1276.0	43384	3.2103	{'f1': 0.8416988416988417}	{'accuracy': 0.7641418983700863}
0.0034	1277.0	43418	3.2108	{'f1': 0.8416988416988417}	{'accuracy': 0.7641418983700863}
0.0034	1278.0	43452	3.2112	{'f1': 0.8416988416988417}	{'accuracy': 0.7641418983700863}
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0.0	1864.0	63376	3.4897	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1865.0	63410	3.4900	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1866.0	63444	3.4905	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1867.0	63478	3.4906	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1868.0	63512	3.4908	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1869.0	63546	3.4910	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1870.0	63580	3.4914	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1871.0	63614	3.4916	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1872.0	63648	3.4918	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1873.0	63682	3.4920	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1874.0	63716	3.4922	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1875.0	63750	3.4926	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1876.0	63784	3.4936	{'f1': 0.8380237648530332}	{'accuracy': 0.7516778523489933}
0.0	1877.0	63818	3.5059	{'f1': 0.8375}	{'accuracy': 0.75071907957814}
0.0	1878.0	63852	3.5066	{'f1': 0.8382261086820737}	{'accuracy': 0.7516778523489933}
0.0	1879.0	63886	3.5068	{'f1': 0.8382261086820737}	{'accuracy': 0.7516778523489933}
0.0	1880.0	63920	3.5058	{'f1': 0.8375}	{'accuracy': 0.75071907957814}
0.0	1881.0	63954	3.5058	{'f1': 0.8375}	{'accuracy': 0.75071907957814}
0.0	1882.0	63988	3.4003	{'f1': 0.8397720075997466}	{'accuracy': 0.7574304889741131}
0.0001	1883.0	64022	3.3884	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1884.0	64056	3.3882	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1885.0	64090	3.3883	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1886.0	64124	3.3884	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1887.0	64158	3.3885	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1888.0	64192	3.3885	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1889.0	64226	3.3886	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1890.0	64260	3.3891	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1891.0	64294	3.3892	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1892.0	64328	3.3897	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1893.0	64362	3.3898	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1894.0	64396	3.3897	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1895.0	64430	3.3899	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1896.0	64464	3.3900	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0001	1897.0	64498	3.3901	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0	1898.0	64532	3.3902	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0	1899.0	64566	3.3904	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0	1900.0	64600	3.3906	{'f1': 0.8373570520965693}	{'accuracy': 0.7545541706615532}
0.0	1901.0	64634	3.4909	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1902.0	64668	3.4949	{'f1': 0.8392745465916197}	{'accuracy': 0.7535953978907}
0.0	1903.0	64702	3.4952	{'f1': 0.8392745465916197}	{'accuracy': 0.7535953978907}
0.0	1904.0	64736	3.4926	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1905.0	64770	3.4924	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1906.0	64804	3.4926	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1907.0	64838	3.4927	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1908.0	64872	3.4928	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1909.0	64906	3.4928	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1910.0	64940	3.4929	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1911.0	64974	3.4930	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1912.0	65008	3.4932	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1913.0	65042	3.4933	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1914.0	65076	3.4934	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1915.0	65110	3.4935	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1916.0	65144	3.4935	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1917.0	65178	3.4935	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1918.0	65212	3.4936	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1919.0	65246	3.4938	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1920.0	65280	3.4939	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1921.0	65314	3.4940	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1922.0	65348	3.4941	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1923.0	65382	3.4942	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1924.0	65416	3.4942	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1925.0	65450	3.4943	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1926.0	65484	3.4944	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1927.0	65518	3.4945	{'f1': 0.8397997496871088}	{'accuracy': 0.7545541706615532}
0.0	1928.0	65552	3.4926	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1929.0	65586	3.4925	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1930.0	65620	3.4926	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1931.0	65654	3.4928	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1932.0	65688	3.4929	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1933.0	65722	3.4930	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1934.0	65756	3.4931	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1935.0	65790	3.4932	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1936.0	65824	3.4932	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1937.0	65858	3.4933	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1938.0	65892	3.4934	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1939.0	65926	3.4935	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1940.0	65960	3.4936	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1941.0	65994	3.4937	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1942.0	66028	3.4937	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1943.0	66062	3.4938	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1944.0	66096	3.4939	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1945.0	66130	3.4939	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1946.0	66164	3.4940	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1947.0	66198	3.4941	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1948.0	66232	3.4941	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1949.0	66266	3.4942	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1950.0	66300	3.4943	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1951.0	66334	3.4948	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1952.0	66368	3.4948	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1953.0	66402	3.4949	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1954.0	66436	3.4950	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1955.0	66470	3.4952	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1956.0	66504	3.4953	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1957.0	66538	3.4953	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1958.0	66572	3.4954	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1959.0	66606	3.4955	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1960.0	66640	3.4956	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1961.0	66674	3.4956	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1962.0	66708	3.4957	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1963.0	66742	3.4958	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1964.0	66776	3.4959	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1965.0	66810	3.4960	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1966.0	66844	3.4961	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1967.0	66878	3.4962	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1968.0	66912	3.4962	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1969.0	66946	3.4974	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0	1970.0	66980	3.4976	{'f1': 0.8403256105197245}	{'accuracy': 0.7555129434324065}
0.0011	1971.0	67014	3.4573	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1972.0	67048	3.4534	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1973.0	67082	3.4533	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1974.0	67116	3.4534	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1975.0	67150	3.4532	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1976.0	67184	3.4532	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1977.0	67218	3.4533	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1978.0	67252	3.4533	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1979.0	67286	3.4491	{'f1': 0.8395217117684078}	{'accuracy': 0.7555129434324065}
0.0011	1980.0	67320	3.4555	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1981.0	67354	3.4592	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1982.0	67388	3.4601	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1983.0	67422	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1984.0	67456	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0011	1985.0	67490	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1986.0	67524	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1987.0	67558	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1988.0	67592	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1989.0	67626	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1990.0	67660	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1991.0	67694	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1992.0	67728	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1993.0	67762	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1994.0	67796	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1995.0	67830	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1996.0	67864	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1997.0	67898	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1998.0	67932	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0001	1999.0	67966	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}
0.0	2000.0	68000	3.4602	{'f1': 0.8391959798994975}	{'accuracy': 0.7545541706615532}

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.3