README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - glue
metrics:
  - f1
  - accuracy
model-index:
  - name: bert-base-uncased-test_16_535
    results: []

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: 4.2260
• F1: {'f1': 0.7565495207667732}
• Accuracy: {'accuracy': 0.6347075743048898}

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: 0.001
• 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	0.6031	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
No log	2.0	68	0.5685	{'f1': 0.8203661327231121}	{'accuracy': 0.6989453499520614}
No log	3.0	102	0.6256	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
No log	4.0	136	0.5539	{'f1': 0.7933042212518194}	{'accuracy': 0.7277085330776606}
No log	5.0	170	0.6268	{'f1': 0.8310214375788146}	{'accuracy': 0.7430488974113135}
No log	6.0	204	0.9841	{'f1': 0.8037653874004345}	{'accuracy': 0.7401725790987536}
No log	7.0	238	0.9858	{'f1': 0.8460620525059666}	{'accuracy': 0.7526366251198466}
No log	8.0	272	1.4189	{'f1': 0.8485221674876847}	{'accuracy': 0.7641418983700863}
No log	9.0	306	1.0672	{'f1': 0.8197831978319783}	{'accuracy': 0.7449664429530202}
No log	10.0	340	1.6573	{'f1': 0.8440366972477065}	{'accuracy': 0.7555129434324065}
No log	11.0	374	1.3649	{'f1': 0.8562259306803593}	{'accuracy': 0.785234899328859}
No log	12.0	408	1.3585	{'f1': 0.8506900878293602}	{'accuracy': 0.7718120805369127}
No log	13.0	442	1.5549	{'f1': 0.8430054978619427}	{'accuracy': 0.7535953978907}
No log	14.0	476	1.8373	{'f1': 0.8437306979617049}	{'accuracy': 0.7574304889741131}
0.2394	15.0	510	1.6879	{'f1': 0.8426461143224149}	{'accuracy': 0.7651006711409396}
0.2394	16.0	544	1.9573	{'f1': 0.8462484624846248}	{'accuracy': 0.7603068072866731}
0.2394	17.0	578	1.8418	{'f1': 0.8555486670799751}	{'accuracy': 0.7766059443911792}
0.2394	18.0	612	1.7456	{'f1': 0.8501567398119122}	{'accuracy': 0.7708533077660594}
0.2394	19.0	646	2.0146	{'f1': 0.8465286236297199}	{'accuracy': 0.7583892617449665}
0.2394	20.0	680	1.2021	{'f1': 0.8192602930914167}	{'accuracy': 0.7516778523489933}
0.2394	21.0	714	1.3768	{'f1': 0.8271604938271605}	{'accuracy': 0.7583892617449665}
0.2394	22.0	748	1.5238	{'f1': 0.8382541720154044}	{'accuracy': 0.7583892617449665}
0.2394	23.0	782	2.0285	{'f1': 0.8427594779366064}	{'accuracy': 0.7574304889741131}
0.2394	24.0	816	2.2004	{'f1': 0.8384332925336598}	{'accuracy': 0.7468839884947267}
0.2394	25.0	850	2.1115	{'f1': 0.8486720197652872}	{'accuracy': 0.7651006711409396}
0.2394	26.0	884	2.2066	{'f1': 0.8394753279200501}	{'accuracy': 0.7535953978907}
0.2394	27.0	918	1.8350	{'f1': 0.840446487196323}	{'accuracy': 0.7670182166826462}
0.2394	28.0	952	2.1543	{'f1': 0.8421717171717172}	{'accuracy': 0.7603068072866731}
0.2394	29.0	986	1.9542	{'f1': 0.8352201257861636}	{'accuracy': 0.7488015340364333}
0.0285	30.0	1020	2.2269	{'f1': 0.8398058252427185}	{'accuracy': 0.7468839884947267}
0.0285	31.0	1054	2.3350	{'f1': 0.8308270676691729}	{'accuracy': 0.7411313518696069}
0.0285	32.0	1088	1.9383	{'f1': 0.8372395833333335}	{'accuracy': 0.7603068072866731}
0.0285	33.0	1122	1.2112	{'f1': 0.8478260869565217}	{'accuracy': 0.7718120805369127}
0.0285	34.0	1156	2.2343	{'f1': 0.8423645320197044}	{'accuracy': 0.7545541706615532}
0.0285	35.0	1190	2.3211	{'f1': 0.8412397216951296}	{'accuracy': 0.7593480345158198}
0.0285	36.0	1224	2.6982	{'f1': 0.8338480543545398}	{'accuracy': 0.7420901246404602}
0.0285	37.0	1258	2.4827	{'f1': 0.8375404530744337}	{'accuracy': 0.7593480345158198}
0.0285	38.0	1292	2.2638	{'f1': 0.844106463878327}	{'accuracy': 0.7641418983700863}
0.0285	39.0	1326	1.6533	{'f1': 0.8357843137254902}	{'accuracy': 0.7430488974113135}
0.0285	40.0	1360	1.8294	{'f1': 0.8339483394833949}	{'accuracy': 0.7411313518696069}
0.0285	41.0	1394	2.0741	{'f1': 0.8326073428749221}	{'accuracy': 0.7420901246404602}
0.0285	42.0	1428	1.6316	{'f1': 0.8345230369889682}	{'accuracy': 0.7555129434324065}
0.0285	43.0	1462	1.6026	{'f1': 0.8335403726708075}	{'accuracy': 0.7430488974113135}
0.0285	44.0	1496	1.7854	{'f1': 0.8333333333333333}	{'accuracy': 0.7430488974113135}
0.0288	45.0	1530	2.0844	{'f1': 0.8268090154211151}	{'accuracy': 0.7200383509108341}
0.0288	46.0	1564	1.8915	{'f1': 0.8291250733998825}	{'accuracy': 0.7209971236816874}
0.0288	47.0	1598	1.8459	{'f1': 0.8361581920903955}	{'accuracy': 0.7497603068072867}
0.0288	48.0	1632	2.1841	{'f1': 0.8361370716510904}	{'accuracy': 0.74784276126558}
0.0288	49.0	1666	1.8331	{'f1': 0.8408812729498164}	{'accuracy': 0.75071907957814}
0.0288	50.0	1700	2.1669	{'f1': 0.8375229920294298}	{'accuracy': 0.7459252157238735}
0.0288	51.0	1734	1.9448	{'f1': 0.8217687074829932}	{'accuracy': 0.7488015340364333}
0.0288	52.0	1768	2.3248	{'f1': 0.8369565217391304}	{'accuracy': 0.7411313518696069}
0.0288	53.0	1802	1.8249	{'f1': 0.845360824742268}	{'accuracy': 0.7698945349952061}
0.0288	54.0	1836	1.5104	{'f1': 0.8302606484424666}	{'accuracy': 0.7440076701821668}
0.0288	55.0	1870	1.7967	{'f1': 0.8403247631935047}	{'accuracy': 0.7737296260786194}
0.0288	56.0	1904	1.7794	{'f1': 0.8362068965517242}	{'accuracy': 0.7449664429530202}
0.0288	57.0	1938	1.9612	{'f1': 0.8421052631578948}	{'accuracy': 0.7583892617449665}
0.0288	58.0	1972	2.3249	{'f1': 0.8403465346534654}	{'accuracy': 0.7526366251198466}
0.0243	59.0	2006	2.6953	{'f1': 0.8364741641337385}	{'accuracy': 0.7420901246404602}
0.0243	60.0	2040	2.6241	{'f1': 0.8381538461538462}	{'accuracy': 0.74784276126558}
0.0243	61.0	2074	2.6045	{'f1': 0.8385899814471244}	{'accuracy': 0.7497603068072867}
0.0243	62.0	2108	2.4844	{'f1': 0.8401215805471125}	{'accuracy': 0.74784276126558}
0.0243	63.0	2142	1.6972	{'f1': 0.840418977202711}	{'accuracy': 0.7516778523489933}
0.0243	64.0	2176	1.9711	{'f1': 0.8387925497752088}	{'accuracy': 0.7593480345158198}
0.0243	65.0	2210	1.9891	{'f1': 0.8404907975460122}	{'accuracy': 0.75071907957814}
0.0243	66.0	2244	2.1286	{'f1': 0.8380024360535933}	{'accuracy': 0.7449664429530202}
0.0243	67.0	2278	1.9863	{'f1': 0.8261964735516374}	{'accuracy': 0.7353787152444871}
0.0243	68.0	2312	2.3933	{'f1': 0.7949080622347949}	{'accuracy': 0.7219558964525408}
0.0243	69.0	2346	1.6662	{'f1': 0.8370895041854476}	{'accuracy': 0.7574304889741131}
0.0243	70.0	2380	1.7256	{'f1': 0.8430268918073796}	{'accuracy': 0.7593480345158198}
0.0243	71.0	2414	1.5242	{'f1': 0.8314754098360656}	{'accuracy': 0.7535953978907}
0.0243	72.0	2448	1.6175	{'f1': 0.8375080282594733}	{'accuracy': 0.7574304889741131}
0.0243	73.0	2482	2.5471	{'f1': 0.833535844471446}	{'accuracy': 0.7372962607861937}
0.0247	74.0	2516	1.9411	{'f1': 0.841708542713568}	{'accuracy': 0.7583892617449665}
0.0247	75.0	2550	2.0580	{'f1': 0.8373831775700934}	{'accuracy': 0.7497603068072867}
0.0247	76.0	2584	2.3411	{'f1': 0.8323057953144266}	{'accuracy': 0.7392138063279002}
0.0247	77.0	2618	2.3319	{'f1': 0.8364099299809039}	{'accuracy': 0.7535953978907}
0.0247	78.0	2652	2.4792	{'f1': 0.8362720403022671}	{'accuracy': 0.75071907957814}
0.0247	79.0	2686	3.1382	{'f1': 0.8284023668639053}	{'accuracy': 0.7219558964525408}
0.0247	80.0	2720	2.8825	{'f1': 0.8352306770521271}	{'accuracy': 0.7363374880153404}
0.0247	81.0	2754	2.4851	{'f1': 0.8287937743190662}	{'accuracy': 0.7468839884947267}
0.0247	82.0	2788	2.7800	{'f1': 0.8391866913123845}	{'accuracy': 0.7497603068072867}
0.0247	83.0	2822	2.5317	{'f1': 0.838390092879257}	{'accuracy': 0.7497603068072867}
0.0247	84.0	2856	2.3079	{'f1': 0.8323057953144266}	{'accuracy': 0.7392138063279002}
0.0247	85.0	2890	2.0362	{'f1': 0.8239074550128535}	{'accuracy': 0.7372962607861937}
0.0247	86.0	2924	1.7755	{'f1': 0.8317214700193424}	{'accuracy': 0.7497603068072867}
0.0247	87.0	2958	2.8178	{'f1': 0.8299401197604791}	{'accuracy': 0.7277085330776606}
0.0247	88.0	2992	2.7970	{'f1': 0.8307134220072551}	{'accuracy': 0.7315436241610739}
0.0262	89.0	3026	2.2069	{'f1': 0.8210800260247235}	{'accuracy': 0.7363374880153404}
0.0262	90.0	3060	2.4590	{'f1': 0.8289637952559301}	{'accuracy': 0.7372962607861937}
0.0262	91.0	3094	2.6507	{'f1': 0.8239747634069401}	{'accuracy': 0.7325023969319271}
0.0262	92.0	3128	2.3580	{'f1': 0.8371257485029939}	{'accuracy': 0.7392138063279002}
0.0262	93.0	3162	2.0228	{'f1': 0.8173207036535859}	{'accuracy': 0.7411313518696069}
0.0262	94.0	3196	2.9600	{'f1': 0.8229105786090006}	{'accuracy': 0.7094918504314478}
0.0262	95.0	3230	1.5716	{'f1': 0.8368708308065496}	{'accuracy': 0.7420901246404602}
0.0262	96.0	3264	2.2060	{'f1': 0.8202247191011236}	{'accuracy': 0.7238734419942474}
0.0262	97.0	3298	2.1345	{'f1': 0.8324125230202578}	{'accuracy': 0.738255033557047}
0.0262	98.0	3332	2.0905	{'f1': 0.8274111675126904}	{'accuracy': 0.7392138063279002}
0.0262	99.0	3366	2.4812	{'f1': 0.8284466625077979}	{'accuracy': 0.7363374880153404}
0.0262	100.0	3400	2.8454	{'f1': 0.8298001211387038}	{'accuracy': 0.7305848513902206}
0.0262	101.0	3434	2.4681	{'f1': 0.8314199395770392}	{'accuracy': 0.7325023969319271}
0.0262	102.0	3468	2.3057	{'f1': 0.8292094146047073}	{'accuracy': 0.7286673058485139}
0.0243	103.0	3502	2.1339	{'f1': 0.8280254777070063}	{'accuracy': 0.7411313518696069}
0.0243	104.0	3536	2.0451	{'f1': 0.83502538071066}	{'accuracy': 0.75071907957814}
0.0243	105.0	3570	2.6213	{'f1': 0.8346456692913385}	{'accuracy': 0.738255033557047}
0.0243	106.0	3604	2.8700	{'f1': 0.829443447037702}	{'accuracy': 0.7267497603068073}
0.0243	107.0	3638	2.1492	{'f1': 0.8274582560296847}	{'accuracy': 0.7325023969319271}
0.0243	108.0	3672	2.0604	{'f1': 0.8241206030150754}	{'accuracy': 0.7315436241610739}
0.0243	109.0	3706	2.9161	{'f1': 0.822637106184364}	{'accuracy': 0.7085330776605945}
0.0243	110.0	3740	1.2984	{'f1': 0.8116747741487144}	{'accuracy': 0.7401725790987536}
0.0243	111.0	3774	2.4166	{'f1': 0.8304977945809704}	{'accuracy': 0.7420901246404602}
0.0243	112.0	3808	2.4063	{'f1': 0.8286479250334672}	{'accuracy': 0.7545541706615532}
0.0243	113.0	3842	2.5442	{'f1': 0.8377544725478101}	{'accuracy': 0.74784276126558}
0.0243	114.0	3876	2.5746	{'f1': 0.8327228327228328}	{'accuracy': 0.7372962607861937}
0.0243	115.0	3910	2.3220	{'f1': 0.8315457413249213}	{'accuracy': 0.7440076701821668}
0.0243	116.0	3944	1.6114	{'f1': 0.8301647655259823}	{'accuracy': 0.7430488974113135}
0.0243	117.0	3978	2.9069	{'f1': 0.8245717660956882}	{'accuracy': 0.7152444870565676}
0.0222	118.0	4012	2.3299	{'f1': 0.8295102293862369}	{'accuracy': 0.7363374880153404}
0.0222	119.0	4046	1.9029	{'f1': 0.8328115216030058}	{'accuracy': 0.7440076701821668}
0.0222	120.0	4080	1.8684	{'f1': 0.8319123020706456}	{'accuracy': 0.7353787152444871}
0.0222	121.0	4114	2.3230	{'f1': 0.8233830845771144}	{'accuracy': 0.7277085330776606}
0.0222	122.0	4148	1.7117	{'f1': 0.8227927363807138}	{'accuracy': 0.7286673058485139}
0.0222	123.0	4182	2.1507	{'f1': 0.8172715894868587}	{'accuracy': 0.7200383509108341}
0.0222	124.0	4216	1.8650	{'f1': 0.7955801104972376}	{'accuracy': 0.716203259827421}
0.0222	125.0	4250	1.8793	{'f1': 0.8193224592220828}	{'accuracy': 0.7238734419942474}
0.0222	126.0	4284	2.6553	{'f1': 0.8233890214797136}	{'accuracy': 0.716203259827421}
0.0222	127.0	4318	1.2802	{'f1': 0.8148148148148148}	{'accuracy': 0.7267497603068073}
0.0222	128.0	4352	2.5781	{'f1': 0.8163530175210901}	{'accuracy': 0.7286673058485139}
0.0222	129.0	4386	2.7981	{'f1': 0.8256658595641646}	{'accuracy': 0.7238734419942474}
0.0222	130.0	4420	2.8012	{'f1': 0.8204503956177724}	{'accuracy': 0.7171620325982742}
0.0222	131.0	4454	2.5226	{'f1': 0.8157401623985009}	{'accuracy': 0.7171620325982742}
0.0222	132.0	4488	2.4552	{'f1': 0.8199513381995133}	{'accuracy': 0.716203259827421}
0.0372	133.0	4522	1.9021	{'f1': 0.797324414715719}	{'accuracy': 0.7094918504314478}
0.0372	134.0	4556	2.0639	{'f1': 0.8026229508196722}	{'accuracy': 0.7114093959731543}
0.0372	135.0	4590	2.2311	{'f1': 0.8160535117056856}	{'accuracy': 0.7363374880153404}
0.0372	136.0	4624	2.5284	{'f1': 0.8243992606284659}	{'accuracy': 0.7267497603068073}
0.0372	137.0	4658	2.4167	{'f1': 0.8247036805988771}	{'accuracy': 0.7305848513902206}
0.0372	138.0	4692	2.6568	{'f1': 0.8244372990353698}	{'accuracy': 0.738255033557047}
0.0372	139.0	4726	2.2152	{'f1': 0.8304020100502513}	{'accuracy': 0.7411313518696069}
0.0372	140.0	4760	2.2360	{'f1': 0.8236048749198204}	{'accuracy': 0.7363374880153404}
0.0372	141.0	4794	2.1537	{'f1': 0.8233815210559398}	{'accuracy': 0.7305848513902206}
0.0372	142.0	4828	2.7582	{'f1': 0.8266666666666667}	{'accuracy': 0.725790987535954}
0.0372	143.0	4862	2.6203	{'f1': 0.8269831355402872}	{'accuracy': 0.7344199424736337}
0.0372	144.0	4896	2.0762	{'f1': 0.8217054263565892}	{'accuracy': 0.7353787152444871}
0.0372	145.0	4930	1.9867	{'f1': 0.8187211601845747}	{'accuracy': 0.7363374880153404}
0.0372	146.0	4964	2.3924	{'f1': 0.8323133414932682}	{'accuracy': 0.7372962607861937}
0.0372	147.0	4998	2.8917	{'f1': 0.8181268882175228}	{'accuracy': 0.7114093959731543}
0.0237	148.0	5032	1.9651	{'f1': 0.8228155339805826}	{'accuracy': 0.7200383509108341}
0.0237	149.0	5066	2.7154	{'f1': 0.8242498469075321}	{'accuracy': 0.7248322147651006}
0.0237	150.0	5100	2.3627	{'f1': 0.8244274809160305}	{'accuracy': 0.7353787152444871}
0.0237	151.0	5134	2.1442	{'f1': 0.8219013237063778}	{'accuracy': 0.716203259827421}
0.0237	152.0	5168	2.2215	{'f1': 0.8286252354048964}	{'accuracy': 0.738255033557047}
0.0237	153.0	5202	2.5071	{'f1': 0.8232323232323232}	{'accuracy': 0.7315436241610739}
0.0237	154.0	5236	2.7711	{'f1': 0.8219005663939586}	{'accuracy': 0.7286673058485139}
0.0237	155.0	5270	3.0069	{'f1': 0.8185758513931888}	{'accuracy': 0.7190795781399808}
0.0237	156.0	5304	3.0085	{'f1': 0.8216283405842139}	{'accuracy': 0.7248322147651006}
0.0237	157.0	5338	2.9624	{'f1': 0.8208588957055215}	{'accuracy': 0.7200383509108341}
0.0237	158.0	5372	2.1774	{'f1': 0.8129117259552042}	{'accuracy': 0.7277085330776606}
0.0237	159.0	5406	1.6617	{'f1': 0.8022206800832756}	{'accuracy': 0.7267497603068073}
0.0237	160.0	5440	2.1404	{'f1': 0.8059299191374663}	{'accuracy': 0.7238734419942474}
0.0237	161.0	5474	2.1100	{'f1': 0.8241626794258373}	{'accuracy': 0.7181208053691275}
0.0175	162.0	5508	2.4209	{'f1': 0.8274582560296847}	{'accuracy': 0.7325023969319271}
0.0175	163.0	5542	2.3767	{'f1': 0.8124585818422798}	{'accuracy': 0.7286673058485139}
0.0175	164.0	5576	2.4593	{'f1': 0.8209134615384616}	{'accuracy': 0.7142857142857143}
0.0175	165.0	5610	2.1189	{'f1': 0.8123393316195373}	{'accuracy': 0.7200383509108341}
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0.0075	1344.0	45696	3.5305	{'f1': 0.7576142131979695}	{'accuracy': 0.6337488015340365}
0.0075	1345.0	45730	3.5883	{'f1': 0.7594936708860759}	{'accuracy': 0.6356663470757431}
0.0075	1346.0	45764	3.5093	{'f1': 0.7746305418719213}	{'accuracy': 0.6490891658676894}
0.0075	1347.0	45798	3.6125	{'f1': 0.7647058823529411}	{'accuracy': 0.6395014381591563}
0.0075	1348.0	45832	3.6587	{'f1': 0.7620841180163214}	{'accuracy': 0.6366251198465963}
0.0075	1349.0	45866	3.6864	{'f1': 0.7613065326633165}	{'accuracy': 0.6356663470757431}
0.0075	1350.0	45900	3.7250	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0075	1351.0	45934	3.7487	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0075	1352.0	45968	3.7722	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0018	1353.0	46002	3.7933	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0018	1354.0	46036	3.8149	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0018	1355.0	46070	3.8357	{'f1': 0.7607052896725441}	{'accuracy': 0.6356663470757431}
0.0018	1356.0	46104	4.0046	{'f1': 0.7261744966442953}	{'accuracy': 0.6088207094918504}
0.0018	1357.0	46138	3.9125	{'f1': 0.7356624917600526}	{'accuracy': 0.6155321188878236}
0.0018	1358.0	46172	3.9176	{'f1': 0.7376725838264301}	{'accuracy': 0.6174496644295302}
0.0018	1359.0	46206	3.9499	{'f1': 0.7376725838264301}	{'accuracy': 0.6174496644295302}
0.0018	1360.0	46240	3.6228	{'f1': 0.7652933832709113}	{'accuracy': 0.6395014381591563}
0.0018	1361.0	46274	3.4484	{'f1': 0.7745521927115503}	{'accuracy': 0.6500479386385427}
0.0018	1362.0	46308	3.5878	{'f1': 0.7557544757033248}	{'accuracy': 0.6337488015340365}
0.0018	1363.0	46342	3.5620	{'f1': 0.7688442211055276}	{'accuracy': 0.6471716203259827}
0.0018	1364.0	46376	3.5952	{'f1': 0.7689417658108956}	{'accuracy': 0.6462128475551294}
0.0018	1365.0	46410	3.7652	{'f1': 0.7514450867052023}	{'accuracy': 0.6289549376797698}
0.0018	1366.0	46444	3.8198	{'f1': 0.7528809218950064}	{'accuracy': 0.6299137104506232}
0.0018	1367.0	46478	2.8867	{'f1': 0.7611181702668361}	{'accuracy': 0.6395014381591563}
0.0069	1368.0	46512	3.0464	{'f1': 0.7648546144121365}	{'accuracy': 0.6433365292425696}
0.0069	1369.0	46546	3.1224	{'f1': 0.7680703959773727}	{'accuracy': 0.6462128475551294}
0.0069	1370.0	46580	3.2843	{'f1': 0.755612572161642}	{'accuracy': 0.6347075743048898}
0.0069	1371.0	46614	3.2492	{'f1': 0.7681789931634555}	{'accuracy': 0.6423777564717162}
0.0069	1372.0	46648	3.3134	{'f1': 0.7669172932330827}	{'accuracy': 0.6433365292425696}
0.0069	1373.0	46682	3.3714	{'f1': 0.7480620155038761}	{'accuracy': 0.62607861936721}
0.0069	1374.0	46716	3.3207	{'f1': 0.7737948084054388}	{'accuracy': 0.6490891658676894}
0.0069	1375.0	46750	3.3698	{'f1': 0.7711442786069651}	{'accuracy': 0.6471716203259827}
0.0069	1376.0	46784	3.0930	{'f1': 0.7519280205655527}	{'accuracy': 0.6299137104506232}
0.0069	1377.0	46818	3.4375	{'f1': 0.7289719626168224}	{'accuracy': 0.610738255033557}
0.0069	1378.0	46852	3.1735	{'f1': 0.772104607721046}	{'accuracy': 0.6490891658676894}
0.0069	1379.0	46886	3.2524	{'f1': 0.770949720670391}	{'accuracy': 0.6462128475551294}
0.0069	1380.0	46920	3.3112	{'f1': 0.7704714640198512}	{'accuracy': 0.6452540747842761}
0.0069	1381.0	46954	3.3603	{'f1': 0.7704714640198512}	{'accuracy': 0.6452540747842761}
0.0069	1382.0	46988	3.3962	{'f1': 0.7723880597014924}	{'accuracy': 0.6490891658676894}
0.0043	1383.0	47022	3.4355	{'f1': 0.7723880597014924}	{'accuracy': 0.6490891658676894}
0.0043	1384.0	47056	3.6259	{'f1': 0.7389875082182775}	{'accuracy': 0.6193672099712368}
0.0043	1385.0	47090	3.4024	{'f1': 0.7689440993788821}	{'accuracy': 0.6433365292425696}
0.0043	1386.0	47124	3.4427	{'f1': 0.7697082557417754}	{'accuracy': 0.6442953020134228}
0.0043	1387.0	47158	3.4609	{'f1': 0.7634749524413443}	{'accuracy': 0.6423777564717162}
0.0043	1388.0	47192	3.6020	{'f1': 0.7434895833333333}	{'accuracy': 0.6222435282837967}
0.0043	1389.0	47226	3.5073	{'f1': 0.7616020343293072}	{'accuracy': 0.6404602109300096}
0.0043	1390.0	47260	3.4757	{'f1': 0.7724736515809051}	{'accuracy': 0.6481303930968361}
0.0043	1391.0	47294	3.5197	{'f1': 0.7726708074534162}	{'accuracy': 0.6490891658676894}
0.0043	1392.0	47328	3.5570	{'f1': 0.7719080174021131}	{'accuracy': 0.6481303930968361}
0.0043	1393.0	47362	2.9746	{'f1': 0.7693266832917706}	{'accuracy': 0.6452540747842761}
0.0043	1394.0	47396	3.0565	{'f1': 0.7669172932330827}	{'accuracy': 0.6433365292425696}
0.0043	1395.0	47430	3.2641	{'f1': 0.7496774193548387}	{'accuracy': 0.6279961649089166}
0.0043	1396.0	47464	3.2319	{'f1': 0.7654784240150093}	{'accuracy': 0.6404602109300096}
0.0043	1397.0	47498	3.3054	{'f1': 0.7647058823529411}	{'accuracy': 0.6395014381591563}
0.0057	1398.0	47532	3.4740	{'f1': 0.753015873015873}	{'accuracy': 0.6270373921380633}
0.0057	1399.0	47566	3.6016	{'f1': 0.7436399217221136}	{'accuracy': 0.62320230105465}
0.0057	1400.0	47600	3.6997	{'f1': 0.7372549019607842}	{'accuracy': 0.6145733461169702}
0.0057	1401.0	47634	3.5242	{'f1': 0.7533460803059273}	{'accuracy': 0.6289549376797698}
0.0057	1402.0	47668	3.3049	{'f1': 0.7775746496039001}	{'accuracy': 0.6500479386385427}
0.0057	1403.0	47702	3.4798	{'f1': 0.7537878787878789}	{'accuracy': 0.62607861936721}
0.0057	1404.0	47736	3.4908	{'f1': 0.7619047619047619}	{'accuracy': 0.6356663470757431}
0.0057	1405.0	47770	2.9441	{'f1': 0.7722529158993248}	{'accuracy': 0.6442953020134228}
0.0057	1406.0	47804	3.1655	{'f1': 0.7719729563614013}	{'accuracy': 0.6442953020134228}
0.0057	1407.0	47838	3.1089	{'f1': 0.7681789931634555}	{'accuracy': 0.6423777564717162}
0.0057	1408.0	47872	3.2898	{'f1': 0.7684667908131595}	{'accuracy': 0.6423777564717162}
0.0057	1409.0	47906	3.4077	{'f1': 0.7677018633540372}	{'accuracy': 0.6414189837008629}
0.0057	1410.0	47940	3.4799	{'f1': 0.7669359850839029}	{'accuracy': 0.6404602109300096}
0.0057	1411.0	47974	3.4300	{'f1': 0.7815533980582525}	{'accuracy': 0.6548418024928092}
0.0082	1412.0	48008	3.4573	{'f1': 0.7842900302114804}	{'accuracy': 0.6577181208053692}
0.0082	1413.0	48042	2.9054	{'f1': 0.763681592039801}	{'accuracy': 0.6356663470757431}
0.0082	1414.0	48076	3.1856	{'f1': 0.7589679043423537}	{'accuracy': 0.6327900287631831}
0.0082	1415.0	48110	3.2829	{'f1': 0.7589679043423537}	{'accuracy': 0.6327900287631831}
0.0082	1416.0	48144	3.3540	{'f1': 0.7589679043423537}	{'accuracy': 0.6327900287631831}
0.0082	1417.0	48178	3.0411	{'f1': 0.7685643564356436}	{'accuracy': 0.6414189837008629}
0.0082	1418.0	48212	2.9540	{'f1': 0.7661691542288558}	{'accuracy': 0.6395014381591563}
0.0082	1419.0	48246	2.9893	{'f1': 0.7564505978602896}	{'accuracy': 0.6289549376797698}
0.0082	1420.0	48280	3.0807	{'f1': 0.7632729544034977}	{'accuracy': 0.6366251198465963}
0.0082	1421.0	48314	2.8429	{'f1': 0.7851491174680463}	{'accuracy': 0.6615532118887824}
0.0082	1422.0	48348	3.1309	{'f1': 0.7600502512562815}	{'accuracy': 0.6337488015340365}
0.0082	1423.0	48382	3.2483	{'f1': 0.7605279698302955}	{'accuracy': 0.6347075743048898}
0.0082	1424.0	48416	3.3036	{'f1': 0.7605279698302955}	{'accuracy': 0.6347075743048898}
0.0082	1425.0	48450	3.3621	{'f1': 0.7589679043423537}	{'accuracy': 0.6327900287631831}
0.0082	1426.0	48484	3.4070	{'f1': 0.7594458438287154}	{'accuracy': 0.6337488015340365}
0.0073	1427.0	48518	3.4997	{'f1': 0.7427090084251459}	{'accuracy': 0.6193672099712368}
0.0073	1428.0	48552	3.5861	{'f1': 0.7315789473684212}	{'accuracy': 0.6088207094918504}
0.0073	1429.0	48586	3.5632	{'f1': 0.7441860465116279}	{'accuracy': 0.6203259827420902}
0.0073	1430.0	48620	3.5890	{'f1': 0.744516129032258}	{'accuracy': 0.6203259827420902}
0.0073	1431.0	48654	3.6156	{'f1': 0.745325596389426}	{'accuracy': 0.6212847555129435}
0.0073	1432.0	48688	3.6471	{'f1': 0.7466324567030147}	{'accuracy': 0.6212847555129435}
0.0073	1433.0	48722	3.6496	{'f1': 0.7577482605945604}	{'accuracy': 0.6327900287631831}
0.0073	1434.0	48756	3.7989	{'f1': 0.7371836469824788}	{'accuracy': 0.6116970278044104}
0.0073	1435.0	48790	3.7771	{'f1': 0.7531806615776081}	{'accuracy': 0.6279961649089166}
0.0073	1436.0	48824	3.6796	{'f1': 0.7669359850839029}	{'accuracy': 0.6404602109300096}
0.0073	1437.0	48858	3.6959	{'f1': 0.7677018633540372}	{'accuracy': 0.6414189837008629}
0.0073	1438.0	48892	3.7324	{'f1': 0.7661691542288558}	{'accuracy': 0.6395014381591563}
0.0073	1439.0	48926	3.7583	{'f1': 0.7661691542288558}	{'accuracy': 0.6395014381591563}
0.0073	1440.0	48960	3.4722	{'f1': 0.7932692307692308}	{'accuracy': 0.6701821668264621}
0.0073	1441.0	48994	3.0940	{'f1': 0.7796817625458997}	{'accuracy': 0.6548418024928092}
0.0039	1442.0	49028	3.2434	{'f1': 0.7509578544061303}	{'accuracy': 0.62607861936721}
0.0039	1443.0	49062	3.0434	{'f1': 0.7482383087764254}	{'accuracy': 0.62320230105465}
0.0039	1444.0	49096	3.1950	{'f1': 0.746615087040619}	{'accuracy': 0.62320230105465}
0.0039	1445.0	49130	3.0658	{'f1': 0.7385019710906702}	{'accuracy': 0.6184084372003835}
0.0039	1446.0	49164	3.1805	{'f1': 0.7655562539283469}	{'accuracy': 0.6423777564717162}
0.0039	1447.0	49198	3.2558	{'f1': 0.7660377358490567}	{'accuracy': 0.6433365292425696}
0.0039	1448.0	49232	3.3399	{'f1': 0.722036168787676}	{'accuracy': 0.6021093000958773}
0.0039	1449.0	49266	2.8893	{'f1': 0.7283621837549933}	{'accuracy': 0.6088207094918504}
0.0039	1450.0	49300	2.8286	{'f1': 0.7519181585677749}	{'accuracy': 0.6279961649089166}
0.0039	1451.0	49334	2.9886	{'f1': 0.7671232876712328}	{'accuracy': 0.6414189837008629}
0.0039	1452.0	49368	3.0879	{'f1': 0.7743526510480888}	{'accuracy': 0.6490891658676894}
0.0039	1453.0	49402	3.2392	{'f1': 0.742561448900388}	{'accuracy': 0.6184084372003835}
0.0039	1454.0	49436	3.3527	{'f1': 0.7263017356475301}	{'accuracy': 0.6069031639501438}
0.0039	1455.0	49470	3.0399	{'f1': 0.7575757575757576}	{'accuracy': 0.6318312559923298}
0.0136	1456.0	49504	2.9785	{'f1': 0.7689440993788821}	{'accuracy': 0.6433365292425696}
0.0136	1457.0	49538	3.0590	{'f1': 0.7449967721110394}	{'accuracy': 0.6212847555129435}
0.0136	1458.0	49572	3.2933	{'f1': 0.7346405228758169}	{'accuracy': 0.610738255033557}
0.0136	1459.0	49606	3.0753	{'f1': 0.7326732673267328}	{'accuracy': 0.6116970278044104}
0.0136	1460.0	49640	3.1748	{'f1': 0.7338603425559947}	{'accuracy': 0.6126558005752637}
0.0136	1461.0	49674	3.0650	{'f1': 0.7332457293035479}	{'accuracy': 0.610738255033557}
0.0136	1462.0	49708	3.1179	{'f1': 0.7611846250787649}	{'accuracy': 0.6366251198465963}
0.0136	1463.0	49742	3.1942	{'f1': 0.7617850408548082}	{'accuracy': 0.6366251198465963}
0.0136	1464.0	49776	3.2598	{'f1': 0.7611846250787649}	{'accuracy': 0.6366251198465963}
0.0136	1465.0	49810	3.3032	{'f1': 0.7624448645242595}	{'accuracy': 0.638542665388303}
0.0136	1466.0	49844	3.3924	{'f1': 0.7520713830465264}	{'accuracy': 0.6270373921380633}
0.0136	1467.0	49878	3.3364	{'f1': 0.7661442006269592}	{'accuracy': 0.6423777564717162}
0.0136	1468.0	49912	3.2845	{'f1': 0.7725587144622991}	{'accuracy': 0.6471716203259827}
0.0136	1469.0	49946	3.3055	{'f1': 0.7733168622606548}	{'accuracy': 0.6481303930968361}
0.0136	1470.0	49980	3.3756	{'f1': 0.7629255989911727}	{'accuracy': 0.6395014381591563}
0.0053	1471.0	50014	3.4611	{'f1': 0.7520819987187701}	{'accuracy': 0.6289549376797698}
0.0053	1472.0	50048	3.4933	{'f1': 0.7512820512820513}	{'accuracy': 0.6279961649089166}
0.0053	1473.0	50082	3.5197	{'f1': 0.7512820512820513}	{'accuracy': 0.6279961649089166}
0.0053	1474.0	50116	3.5476	{'f1': 0.7569974554707379}	{'accuracy': 0.6337488015340365}
0.0053	1475.0	50150	3.5483	{'f1': 0.7593650793650795}	{'accuracy': 0.6366251198465963}
0.0053	1476.0	50184	3.5712	{'f1': 0.7593650793650795}	{'accuracy': 0.6366251198465963}
0.0053	1477.0	50218	3.5925	{'f1': 0.7588832487309646}	{'accuracy': 0.6356663470757431}
0.0053	1478.0	50252	3.6118	{'f1': 0.7596702599873176}	{'accuracy': 0.6366251198465963}
0.0053	1479.0	50286	3.6913	{'f1': 0.7535121328224776}	{'accuracy': 0.6299137104506232}
0.0053	1480.0	50320	3.6203	{'f1': 0.7699004975124378}	{'accuracy': 0.6452540747842761}
0.0053	1481.0	50354	3.6427	{'f1': 0.7714285714285715}	{'accuracy': 0.6471716203259827}
0.0053	1482.0	50388	3.6615	{'f1': 0.7714285714285715}	{'accuracy': 0.6471716203259827}
0.0053	1483.0	50422	2.9799	{'f1': 0.7433398310591293}	{'accuracy': 0.6212847555129435}
0.0053	1484.0	50456	3.0161	{'f1': 0.738562091503268}	{'accuracy': 0.6164908916586769}
0.0053	1485.0	50490	3.0048	{'f1': 0.7654476670870113}	{'accuracy': 0.6433365292425696}
0.0035	1486.0	50524	3.0608	{'f1': 0.7317397078353254}	{'accuracy': 0.6126558005752637}
0.0035	1487.0	50558	3.1529	{'f1': 0.7557544757033248}	{'accuracy': 0.6337488015340365}
0.0035	1488.0	50592	3.1638	{'f1': 0.7645951035781543}	{'accuracy': 0.6404602109300096}
0.0035	1489.0	50626	3.2747	{'f1': 0.7562062380649268}	{'accuracy': 0.6327900287631831}
0.0035	1490.0	50660	3.3065	{'f1': 0.7555555555555555}	{'accuracy': 0.6308724832214765}
0.0035	1491.0	50694	3.3442	{'f1': 0.7555555555555555}	{'accuracy': 0.6308724832214765}
0.0035	1492.0	50728	3.3815	{'f1': 0.7555555555555555}	{'accuracy': 0.6308724832214765}
0.0035	1493.0	50762	3.4099	{'f1': 0.7566539923954372}	{'accuracy': 0.6318312559923298}
0.0035	1494.0	50796	3.3824	{'f1': 0.7614852108244179}	{'accuracy': 0.6366251198465963}
0.0035	1495.0	50830	3.2660	{'f1': 0.7706650093225607}	{'accuracy': 0.6462128475551294}
0.0035	1496.0	50864	3.3044	{'f1': 0.7706650093225607}	{'accuracy': 0.6462128475551294}
0.0035	1497.0	50898	3.3404	{'f1': 0.7706650093225607}	{'accuracy': 0.6462128475551294}
0.0035	1498.0	50932	3.0127	{'f1': 0.7688473520249222}	{'accuracy': 0.6442953020134228}
0.0035	1499.0	50966	3.2918	{'f1': 0.7380796864794251}	{'accuracy': 0.6155321188878236}
0.0037	1500.0	51000	3.4003	{'f1': 0.7435567010309277}	{'accuracy': 0.6184084372003835}
0.0037	1501.0	51034	3.2729	{'f1': 0.7735965453423813}	{'accuracy': 0.6481303930968361}
0.0037	1502.0	51068	3.3953	{'f1': 0.7576142131979695}	{'accuracy': 0.6337488015340365}
0.0037	1503.0	51102	3.4524	{'f1': 0.7525510204081631}	{'accuracy': 0.6279961649089166}
0.0037	1504.0	51136	3.4516	{'f1': 0.7644836272040302}	{'accuracy': 0.6414189837008629}
0.0037	1505.0	51170	3.4680	{'f1': 0.766624843161857}	{'accuracy': 0.6433365292425696}
0.0037	1506.0	51204	3.4365	{'f1': 0.7408376963350786}	{'accuracy': 0.6203259827420902}
0.0037	1507.0	51238	3.3300	{'f1': 0.732977303070761}	{'accuracy': 0.6164908916586769}
0.0037	1508.0	51272	3.0709	{'f1': 0.7395833333333333}	{'accuracy': 0.6164908916586769}
0.0037	1509.0	51306	2.9596	{'f1': 0.742561448900388}	{'accuracy': 0.6184084372003835}
0.0037	1510.0	51340	3.0029	{'f1': 0.7512755102040816}	{'accuracy': 0.62607861936721}
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0.0037	1513.0	51442	3.1799	{'f1': 0.7683755404570722}	{'accuracy': 0.6404602109300096}
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0.0064	1536.0	52224	3.1982	{'f1': 0.7519181585677749}	{'accuracy': 0.6279961649089166}
0.0064	1537.0	52258	3.1040	{'f1': 0.7690387016229714}	{'accuracy': 0.6452540747842761}
0.0064	1538.0	52292	3.0340	{'f1': 0.7580543272267846}	{'accuracy': 0.6327900287631831}
0.0064	1539.0	52326	3.0873	{'f1': 0.7580543272267846}	{'accuracy': 0.6327900287631831}
0.0064	1540.0	52360	2.9080	{'f1': 0.7527156549520766}	{'accuracy': 0.6289549376797698}
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0.0064	1543.0	52462	2.8773	{'f1': 0.7550761421319797}	{'accuracy': 0.6299137104506232}
0.0064	1544.0	52496	2.9720	{'f1': 0.7550761421319797}	{'accuracy': 0.6299137104506232}
0.0069	1545.0	52530	2.9941	{'f1': 0.7547649301143583}	{'accuracy': 0.6299137104506232}
0.0069	1546.0	52564	3.0352	{'f1': 0.7571337983512999}	{'accuracy': 0.6327900287631831}
0.0069	1547.0	52598	3.1289	{'f1': 0.7483530961791831}	{'accuracy': 0.6337488015340365}
0.0069	1548.0	52632	3.1747	{'f1': 0.747871643745907}	{'accuracy': 0.6308724832214765}
0.0069	1549.0	52666	3.2336	{'f1': 0.7490297542043983}	{'accuracy': 0.6279961649089166}
0.0069	1550.0	52700	3.2556	{'f1': 0.7547892720306513}	{'accuracy': 0.6318312559923298}
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0.0069	1552.0	52768	3.3241	{'f1': 0.7547892720306513}	{'accuracy': 0.6318312559923298}
0.0069	1553.0	52802	3.2627	{'f1': 0.7547892720306513}	{'accuracy': 0.6318312559923298}
0.0069	1554.0	52836	2.9687	{'f1': 0.7473958333333334}	{'accuracy': 0.6279961649089166}
0.0069	1555.0	52870	3.0521	{'f1': 0.7440944881889764}	{'accuracy': 0.62607861936721}
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0.0069	1557.0	52938	3.1595	{'f1': 0.7444589308996088}	{'accuracy': 0.6241610738255033}
0.0069	1558.0	52972	3.1697	{'f1': 0.7302013422818792}	{'accuracy': 0.6145733461169702}
0.007	1559.0	53006	3.0408	{'f1': 0.7398692810457516}	{'accuracy': 0.6184084372003835}
0.007	1560.0	53040	3.0760	{'f1': 0.7794746487477093}	{'accuracy': 0.6538830297219559}
0.007	1561.0	53074	3.1548	{'f1': 0.7587523870146403}	{'accuracy': 0.6366251198465963}
0.007	1562.0	53108	3.2482	{'f1': 0.7459389213775178}	{'accuracy': 0.6251198465963567}
0.007	1563.0	53142	3.2902	{'f1': 0.7464239271781534}	{'accuracy': 0.62607861936721}
0.007	1564.0	53176	3.3844	{'f1': 0.7435728411338167}	{'accuracy': 0.6270373921380633}
0.007	1565.0	53210	3.2504	{'f1': 0.7355096602265156}	{'accuracy': 0.6193672099712368}
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0.007	1568.0	53312	3.2650	{'f1': 0.7657430730478589}	{'accuracy': 0.6433365292425696}
0.007	1569.0	53346	3.0981	{'f1': 0.7724736515809051}	{'accuracy': 0.6481303930968361}
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0.0062	1588.0	53992	3.2376	{'f1': 0.7508048937540245}	{'accuracy': 0.6289549376797698}
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0.0022	1603.0	54502	3.3190	{'f1': 0.7623261694058154}	{'accuracy': 0.6395014381591563}
0.0022	1604.0	54536	3.3478	{'f1': 0.7623261694058154}	{'accuracy': 0.6395014381591563}
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0.0022	1608.0	54672	3.4524	{'f1': 0.7623261694058154}	{'accuracy': 0.6395014381591563}
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0.0038	1628.0	55352	3.5079	{'f1': 0.7530468248877487}	{'accuracy': 0.6308724832214765}
0.0038	1629.0	55386	3.5543	{'f1': 0.7516087516087516}	{'accuracy': 0.6299137104506232}
0.0038	1630.0	55420	3.4818	{'f1': 0.767601246105919}	{'accuracy': 0.6423777564717162}
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0.0038	1632.0	55488	3.5157	{'f1': 0.7677784770295784}	{'accuracy': 0.6462128475551294}
0.0034	1633.0	55522	3.3151	{'f1': 0.755612572161642}	{'accuracy': 0.6347075743048898}
0.0034	1634.0	55556	3.1197	{'f1': 0.7490297542043983}	{'accuracy': 0.6279961649089166}
0.0034	1635.0	55590	3.2555	{'f1': 0.7487046632124352}	{'accuracy': 0.6279961649089166}
0.0034	1636.0	55624	3.3209	{'f1': 0.7506459948320413}	{'accuracy': 0.6299137104506232}
0.0034	1637.0	55658	3.3691	{'f1': 0.7509677419354838}	{'accuracy': 0.6299137104506232}
0.0034	1638.0	55692	3.4196	{'f1': 0.7562380038387715}	{'accuracy': 0.6347075743048898}
0.0034	1639.0	55726	3.3962	{'f1': 0.741851368970013}	{'accuracy': 0.6203259827420902}
0.0034	1640.0	55760	3.5089	{'f1': 0.7297116029510397}	{'accuracy': 0.613614573346117}
0.0034	1641.0	55794	3.3322	{'f1': 0.7656348704990524}	{'accuracy': 0.6442953020134228}
0.0034	1642.0	55828	3.5576	{'f1': 0.7470664928292047}	{'accuracy': 0.6279961649089166}
0.0034	1643.0	55862	3.5899	{'f1': 0.7470664928292047}	{'accuracy': 0.6279961649089166}
0.0034	1644.0	55896	3.4360	{'f1': 0.7673981191222571}	{'accuracy': 0.6442953020134228}
0.0034	1645.0	55930	3.4020	{'f1': 0.7755102040816326}	{'accuracy': 0.6519654841802492}
0.0034	1646.0	55964	3.5473	{'f1': 0.7648546144121365}	{'accuracy': 0.6433365292425696}
0.0034	1647.0	55998	3.6072	{'f1': 0.7523992322456813}	{'accuracy': 0.6289549376797698}
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0.0062	1649.0	56066	3.6669	{'f1': 0.7674858223062383}	{'accuracy': 0.6462128475551294}
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0.0062	1661.0	56474	3.7405	{'f1': 0.7655562539283469}	{'accuracy': 0.6423777564717162}
0.0027	1662.0	56508	3.8038	{'f1': 0.76280834914611}	{'accuracy': 0.6404602109300096}
0.0027	1663.0	56542	3.8314	{'f1': 0.7638888888888887}	{'accuracy': 0.6414189837008629}
0.0027	1664.0	56576	3.8491	{'f1': 0.7638888888888887}	{'accuracy': 0.6414189837008629}
0.0027	1665.0	56610	3.8665	{'f1': 0.7638888888888887}	{'accuracy': 0.6414189837008629}
0.0027	1666.0	56644	3.8832	{'f1': 0.7638888888888887}	{'accuracy': 0.6414189837008629}
0.0027	1667.0	56678	3.9888	{'f1': 0.7546444586803331}	{'accuracy': 0.6327900287631831}
0.0027	1668.0	56712	4.0355	{'f1': 0.7496774193548387}	{'accuracy': 0.6279961649089166}
0.0027	1669.0	56746	4.0444	{'f1': 0.7504835589941972}	{'accuracy': 0.6289549376797698}
0.0027	1670.0	56780	4.0486	{'f1': 0.7535301668806161}	{'accuracy': 0.6318312559923298}
0.0027	1671.0	56814	4.0648	{'f1': 0.7543296985246953}	{'accuracy': 0.6327900287631831}
0.0027	1672.0	56848	4.1683	{'f1': 0.7341269841269841}	{'accuracy': 0.6145733461169702}
0.0027	1673.0	56882	3.9003	{'f1': 0.7210884353741497}	{'accuracy': 0.6069031639501438}
0.0027	1674.0	56916	3.8211	{'f1': 0.7336417713152676}	{'accuracy': 0.613614573346117}
0.0027	1675.0	56950	3.7242	{'f1': 0.7632911392405063}	{'accuracy': 0.6414189837008629}
0.0027	1676.0	56984	3.7519	{'f1': 0.7648546144121365}	{'accuracy': 0.6433365292425696}
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0.0026	1686.0	57324	3.6327	{'f1': 0.7474093264248703}	{'accuracy': 0.62607861936721}
0.0026	1687.0	57358	3.6494	{'f1': 0.7474093264248703}	{'accuracy': 0.62607861936721}
0.0026	1688.0	57392	3.4511	{'f1': 0.7675879396984925}	{'accuracy': 0.6452540747842761}
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0.002	1843.0	62662	4.3351	{'f1': 0.751131221719457}	{'accuracy': 0.6308724832214765}
0.002	1844.0	62696	4.3410	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1845.0	62730	4.3472	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1846.0	62764	4.3534	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1847.0	62798	4.3597	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1848.0	62832	4.3662	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1849.0	62866	4.3726	{'f1': 0.751937984496124}	{'accuracy': 0.6318312559923298}
0.002	1850.0	62900	4.3782	{'f1': 0.7527437056165269}	{'accuracy': 0.6327900287631831}
0.002	1851.0	62934	4.3846	{'f1': 0.7527437056165269}	{'accuracy': 0.6327900287631831}
0.002	1852.0	62968	4.3894	{'f1': 0.7530625402965828}	{'accuracy': 0.6327900287631831}
0.0	1853.0	63002	4.3957	{'f1': 0.7533805537669027}	{'accuracy': 0.6327900287631831}
0.0	1854.0	63036	4.4022	{'f1': 0.7533805537669027}	{'accuracy': 0.6327900287631831}
0.0	1855.0	63070	4.4090	{'f1': 0.7533805537669027}	{'accuracy': 0.6327900287631831}
0.0	1856.0	63104	4.4153	{'f1': 0.7533805537669027}	{'accuracy': 0.6327900287631831}
0.0	1857.0	63138	4.4124	{'f1': 0.755128205128205}	{'accuracy': 0.6337488015340365}
0.0	1858.0	63172	4.4182	{'f1': 0.755128205128205}	{'accuracy': 0.6337488015340365}
0.0	1859.0	63206	4.4047	{'f1': 0.7573435504469986}	{'accuracy': 0.6356663470757431}
0.0	1860.0	63240	4.3785	{'f1': 0.7614213197969542}	{'accuracy': 0.6395014381591563}
0.0	1861.0	63274	4.3812	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.0	1862.0	63308	4.3565	{'f1': 0.770287141073658}	{'accuracy': 0.6471716203259827}
0.0	1863.0	63342	4.4535	{'f1': 0.7597195666029317}	{'accuracy': 0.638542665388303}
0.0	1864.0	63376	4.5322	{'f1': 0.7520927237604637}	{'accuracy': 0.6308724832214765}
0.0	1865.0	63410	4.7348	{'f1': 0.7434554973821991}	{'accuracy': 0.6241610738255033}
0.0	1866.0	63444	4.7447	{'f1': 0.7434554973821991}	{'accuracy': 0.6241610738255033}
0.0	1867.0	63478	4.7290	{'f1': 0.7437908496732025}	{'accuracy': 0.6241610738255033}
0.0003	1868.0	63512	4.7276	{'f1': 0.7437908496732025}	{'accuracy': 0.6241610738255033}
0.0003	1869.0	63546	4.3453	{'f1': 0.748870238863783}	{'accuracy': 0.6270373921380633}
0.0003	1870.0	63580	4.1822	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1871.0	63614	4.1872	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1872.0	63648	4.1971	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1873.0	63682	4.2064	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1874.0	63716	4.2163	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1875.0	63750	4.2263	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1876.0	63784	4.2350	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0003	1877.0	63818	4.2038	{'f1': 0.7579617834394904}	{'accuracy': 0.6356663470757431}
0.0003	1878.0	63852	4.2087	{'f1': 0.7590591226954863}	{'accuracy': 0.6366251198465963}
0.0003	1879.0	63886	4.2164	{'f1': 0.7590591226954863}	{'accuracy': 0.6366251198465963}
0.0003	1880.0	63920	4.2241	{'f1': 0.7590591226954863}	{'accuracy': 0.6366251198465963}
0.0003	1881.0	63954	4.2316	{'f1': 0.7590591226954863}	{'accuracy': 0.6366251198465963}
0.0003	1882.0	63988	4.2386	{'f1': 0.7590591226954863}	{'accuracy': 0.6366251198465963}
0.0014	1883.0	64022	4.3296	{'f1': 0.7459389213775178}	{'accuracy': 0.6251198465963567}
0.0014	1884.0	64056	4.2067	{'f1': 0.7619047619047619}	{'accuracy': 0.6404602109300096}
0.0014	1885.0	64090	4.2108	{'f1': 0.7619047619047619}	{'accuracy': 0.6404602109300096}
0.0014	1886.0	64124	4.2173	{'f1': 0.7619047619047619}	{'accuracy': 0.6404602109300096}
0.0014	1887.0	64158	4.2693	{'f1': 0.754014129736673}	{'accuracy': 0.6327900287631831}
0.0014	1888.0	64192	4.2872	{'f1': 0.7512886597938144}	{'accuracy': 0.6299137104506232}
0.0014	1889.0	64226	4.2938	{'f1': 0.7512886597938144}	{'accuracy': 0.6299137104506232}
0.0014	1890.0	64260	4.2707	{'f1': 0.7609942638623327}	{'accuracy': 0.6404602109300096}
0.0014	1891.0	64294	4.2667	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1892.0	64328	4.2724	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1893.0	64362	4.2782	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1894.0	64396	4.2849	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1895.0	64430	4.2908	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1896.0	64464	4.2966	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0014	1897.0	64498	4.3020	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0011	1898.0	64532	4.3076	{'f1': 0.7603305785123967}	{'accuracy': 0.638542665388303}
0.0011	1899.0	64566	4.2093	{'f1': 0.754014129736673}	{'accuracy': 0.6327900287631831}
0.0011	1900.0	64600	4.1992	{'f1': 0.754014129736673}	{'accuracy': 0.6327900287631831}
0.0011	1901.0	64634	4.2020	{'f1': 0.754014129736673}	{'accuracy': 0.6327900287631831}
0.0011	1902.0	64668	4.2727	{'f1': 0.7434895833333333}	{'accuracy': 0.6222435282837967}
0.0011	1903.0	64702	4.3104	{'f1': 0.7421465968586387}	{'accuracy': 0.6222435282837967}
0.0011	1904.0	64736	4.3148	{'f1': 0.7421465968586387}	{'accuracy': 0.6222435282837967}
0.0011	1905.0	64770	4.3175	{'f1': 0.7416612164813604}	{'accuracy': 0.6212847555129435}
0.0011	1906.0	64804	4.3205	{'f1': 0.7406923579359895}	{'accuracy': 0.6193672099712368}
0.0011	1907.0	64838	4.3244	{'f1': 0.7406923579359895}	{'accuracy': 0.6193672099712368}
0.0011	1908.0	64872	4.3289	{'f1': 0.7406923579359895}	{'accuracy': 0.6193672099712368}
0.0011	1909.0	64906	4.1735	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0011	1910.0	64940	4.1701	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.0011	1911.0	64974	4.1748	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.003	1912.0	65008	4.1801	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.003	1913.0	65042	4.1842	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.003	1914.0	65076	4.1873	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.003	1915.0	65110	4.1919	{'f1': 0.7527296082209377}	{'accuracy': 0.6308724832214765}
0.003	1916.0	65144	4.1965	{'f1': 0.7530468248877487}	{'accuracy': 0.6308724832214765}
0.003	1917.0	65178	4.1538	{'f1': 0.7606349206349207}	{'accuracy': 0.638542665388303}
0.003	1918.0	65212	4.1529	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1919.0	65246	4.1572	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1920.0	65280	4.1613	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1921.0	65314	4.1657	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1922.0	65348	4.1699	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1923.0	65382	4.1741	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1924.0	65416	4.1782	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1925.0	65450	4.1822	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.003	1926.0	65484	4.1864	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.0005	1927.0	65518	4.1903	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.0005	1928.0	65552	4.1941	{'f1': 0.7622067216233355}	{'accuracy': 0.6404602109300096}
0.0005	1929.0	65586	4.2854	{'f1': 0.7428571428571429}	{'accuracy': 0.6203259827420902}
0.0005	1930.0	65620	4.3345	{'f1': 0.7403531720078481}	{'accuracy': 0.6193672099712368}
0.0005	1931.0	65654	4.3389	{'f1': 0.7403531720078481}	{'accuracy': 0.6193672099712368}
0.0005	1932.0	65688	4.3341	{'f1': 0.7415143603133159}	{'accuracy': 0.6203259827420902}
0.0005	1933.0	65722	4.3377	{'f1': 0.7415143603133159}	{'accuracy': 0.6203259827420902}
0.0005	1934.0	65756	4.3686	{'f1': 0.7381578947368422}	{'accuracy': 0.6184084372003835}
0.0005	1935.0	65790	4.3655	{'f1': 0.7401574803149606}	{'accuracy': 0.6203259827420902}
0.0005	1936.0	65824	4.3681	{'f1': 0.739672131147541}	{'accuracy': 0.6193672099712368}
0.0005	1937.0	65858	4.3719	{'f1': 0.739672131147541}	{'accuracy': 0.6193672099712368}
0.0005	1938.0	65892	4.3759	{'f1': 0.739672131147541}	{'accuracy': 0.6193672099712368}
0.0005	1939.0	65926	4.3068	{'f1': 0.7446393762183237}	{'accuracy': 0.62320230105465}
0.0005	1940.0	65960	4.2997	{'f1': 0.7441558441558441}	{'accuracy': 0.6222435282837967}
0.0005	1941.0	65994	4.3030	{'f1': 0.7441558441558441}	{'accuracy': 0.6222435282837967}
0.0022	1942.0	66028	4.3066	{'f1': 0.7441558441558441}	{'accuracy': 0.6222435282837967}
0.0022	1943.0	66062	4.3098	{'f1': 0.743672939649578}	{'accuracy': 0.6212847555129435}
0.0022	1944.0	66096	4.3132	{'f1': 0.743672939649578}	{'accuracy': 0.6212847555129435}
0.0022	1945.0	66130	4.3166	{'f1': 0.743672939649578}	{'accuracy': 0.6212847555129435}
0.0022	1946.0	66164	4.3200	{'f1': 0.743672939649578}	{'accuracy': 0.6212847555129435}
0.0022	1947.0	66198	4.3211	{'f1': 0.743672939649578}	{'accuracy': 0.6212847555129435}
0.0022	1948.0	66232	4.2686	{'f1': 0.7479045776918117}	{'accuracy': 0.6251198465963567}
0.0022	1949.0	66266	4.2483	{'f1': 0.7562380038387715}	{'accuracy': 0.6347075743048898}
0.0022	1950.0	66300	4.2760	{'f1': 0.7475790832795353}	{'accuracy': 0.6251198465963567}
0.0022	1951.0	66334	4.2784	{'f1': 0.7479045776918117}	{'accuracy': 0.6251198465963567}
0.0022	1952.0	66368	4.2700	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0022	1953.0	66402	4.2727	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0022	1954.0	66436	4.2756	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0022	1955.0	66470	4.2785	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1956.0	66504	4.2814	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1957.0	66538	4.2842	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1958.0	66572	4.2872	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1959.0	66606	4.1829	{'f1': 0.758444869343531}	{'accuracy': 0.6366251198465963}
0.0007	1960.0	66640	4.1628	{'f1': 0.7600254614894972}	{'accuracy': 0.638542665388303}
0.0007	1961.0	66674	4.1632	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1962.0	66708	4.1647	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1963.0	66742	4.1664	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1964.0	66776	4.1682	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1965.0	66810	4.1700	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1966.0	66844	4.1718	{'f1': 0.7623888182973315}	{'accuracy': 0.6414189837008629}
0.0007	1967.0	66878	4.1922	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1968.0	66912	4.1965	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1969.0	66946	4.1982	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0007	1970.0	66980	4.1999	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0013	1971.0	67014	4.2015	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0013	1972.0	67048	4.2031	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0013	1973.0	67082	4.2046	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0013	1974.0	67116	4.2051	{'f1': 0.7570332480818414}	{'accuracy': 0.6356663470757431}
0.0013	1975.0	67150	4.2062	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1976.0	67184	4.2076	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1977.0	67218	4.2089	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1978.0	67252	4.2102	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1979.0	67286	4.2115	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1980.0	67320	4.2127	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1981.0	67354	4.2139	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1982.0	67388	4.2151	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1983.0	67422	4.2162	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1984.0	67456	4.2172	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0013	1985.0	67490	4.2181	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1986.0	67524	4.2191	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1987.0	67558	4.2199	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1988.0	67592	4.2208	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1989.0	67626	4.2215	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1990.0	67660	4.2223	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1991.0	67694	4.2230	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1992.0	67728	4.2236	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1993.0	67762	4.2242	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1994.0	67796	4.2246	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1995.0	67830	4.2250	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1996.0	67864	4.2254	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1997.0	67898	4.2256	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1998.0	67932	4.2258	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	1999.0	67966	4.2260	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}
0.0	2000.0	68000	4.2260	{'f1': 0.7565495207667732}	{'accuracy': 0.6347075743048898}

Framework versions

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