bert-base-uncased-test_16_214

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: 4.1976
• F1: {'f1': 0.7852222917971196}
• Accuracy: {'accuracy': 0.6711409395973155}

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	14	0.6256	{'f1': 0.8174603174603174}	{'accuracy': 0.6912751677852349}
No log	2.0	28	0.6609	{'f1': 0.7648612945838837}	{'accuracy': 0.6586768935762224}
No log	3.0	42	0.6389	{'f1': 0.8160000000000001}	{'accuracy': 0.6912751677852349}
No log	4.0	56	0.6437	{'f1': 0.8156424581005587}	{'accuracy': 0.7152444870565676}
No log	5.0	70	0.9414	{'f1': 0.8238012709416522}	{'accuracy': 0.7075743048897412}
No log	6.0	84	0.7093	{'f1': 0.7710144927536231}	{'accuracy': 0.6970278044103547}
No log	7.0	98	0.8908	{'f1': 0.8266666666666668}	{'accuracy': 0.738255033557047}
No log	8.0	112	1.5048	{'f1': 0.8314606741573034}	{'accuracy': 0.7411313518696069}
No log	9.0	126	1.4047	{'f1': 0.8164893617021277}	{'accuracy': 0.7353787152444871}
No log	10.0	140	1.3153	{'f1': 0.8258956631049654}	{'accuracy': 0.7344199424736337}
No log	11.0	154	1.9940	{'f1': 0.8376777251184834}	{'accuracy': 0.7372962607861937}
No log	12.0	168	1.7543	{'f1': 0.8381995133819953}	{'accuracy': 0.7449664429530202}
No log	13.0	182	1.3665	{'f1': 0.8373493975903615}	{'accuracy': 0.7411313518696069}
No log	14.0	196	1.4161	{'f1': 0.8407079646017699}	{'accuracy': 0.7583892617449665}
No log	15.0	210	1.6762	{'f1': 0.8419753086419752}	{'accuracy': 0.7545541706615532}
No log	16.0	224	1.5025	{'f1': 0.8335467349551857}	{'accuracy': 0.75071907957814}
No log	17.0	238	1.8215	{'f1': 0.8324056895485467}	{'accuracy': 0.7401725790987536}
No log	18.0	252	2.1709	{'f1': 0.8317535545023695}	{'accuracy': 0.7277085330776606}
No log	19.0	266	1.6465	{'f1': 0.7889908256880733}	{'accuracy': 0.713326941514861}
No log	20.0	280	2.0726	{'f1': 0.8335329341317366}	{'accuracy': 0.7334611697027804}
No log	21.0	294	1.7360	{'f1': 0.8367593712212817}	{'accuracy': 0.7411313518696069}
No log	22.0	308	1.8625	{'f1': 0.835838845460012}	{'accuracy': 0.738255033557047}
No log	23.0	322	1.9270	{'f1': 0.8316953316953316}	{'accuracy': 0.7372962607861937}
No log	24.0	336	2.0441	{'f1': 0.8258391386953768}	{'accuracy': 0.7363374880153404}
No log	25.0	350	2.5576	{'f1': 0.832349468713105}	{'accuracy': 0.7277085330776606}
No log	26.0	364	1.7861	{'f1': 0.8256765261170548}	{'accuracy': 0.7344199424736337}
No log	27.0	378	1.9904	{'f1': 0.8368967623701894}	{'accuracy': 0.7440076701821668}
No log	28.0	392	2.0872	{'f1': 0.8350578210590383}	{'accuracy': 0.7401725790987536}
No log	29.0	406	2.1188	{'f1': 0.8364972443355787}	{'accuracy': 0.7440076701821668}
No log	30.0	420	2.0073	{'f1': 0.8300694883133292}	{'accuracy': 0.7420901246404602}
No log	31.0	434	2.0161	{'f1': 0.8262253341820497}	{'accuracy': 0.738255033557047}
No log	32.0	448	2.5507	{'f1': 0.8366013071895425}	{'accuracy': 0.7363374880153404}
No log	33.0	462	2.2677	{'f1': 0.8296668761785041}	{'accuracy': 0.7401725790987536}
No log	34.0	476	2.4882	{'f1': 0.8405797101449276}	{'accuracy': 0.7468839884947267}
No log	35.0	490	1.9225	{'f1': 0.8298807281858129}	{'accuracy': 0.7401725790987536}
0.1253	36.0	504	2.4305	{'f1': 0.8370720188902008}	{'accuracy': 0.7353787152444871}
0.1253	37.0	518	1.9073	{'f1': 0.7991886409736308}	{'accuracy': 0.7152444870565676}
0.1253	38.0	532	2.5791	{'f1': 0.8360849056603774}	{'accuracy': 0.7334611697027804}
0.1253	39.0	546	2.4886	{'f1': 0.8359096313912009}	{'accuracy': 0.7353787152444871}
0.1253	40.0	560	2.1634	{'f1': 0.8356923076923077}	{'accuracy': 0.7440076701821668}
0.1253	41.0	574	2.0535	{'f1': 0.8412600370599136}	{'accuracy': 0.7535953978907}
0.1253	42.0	588	2.2921	{'f1': 0.8374325134973004}	{'accuracy': 0.7401725790987536}
0.1253	43.0	602	1.9332	{'f1': 0.7579102281089036}	{'accuracy': 0.6845637583892618}
0.1253	44.0	616	2.2699	{'f1': 0.8439587128111719}	{'accuracy': 0.7535953978907}
0.1253	45.0	630	1.9497	{'f1': 0.8336293664890467}	{'accuracy': 0.7305848513902206}
0.1253	46.0	644	1.5622	{'f1': 0.8301647655259823}	{'accuracy': 0.7430488974113135}
0.1253	47.0	658	1.6814	{'f1': 0.8288973384030417}	{'accuracy': 0.7411313518696069}
0.1253	48.0	672	1.8247	{'f1': 0.8307307932542161}	{'accuracy': 0.7401725790987536}
0.1253	49.0	686	1.9138	{'f1': 0.8300940438871472}	{'accuracy': 0.7401725790987536}
0.1253	50.0	700	1.9741	{'f1': 0.8287153652392948}	{'accuracy': 0.7392138063279002}
0.1253	51.0	714	2.0174	{'f1': 0.8287153652392948}	{'accuracy': 0.7392138063279002}
0.1253	52.0	728	2.0830	{'f1': 0.8297872340425533}	{'accuracy': 0.7392138063279002}
0.1253	53.0	742	2.2226	{'f1': 0.8352725045927741}	{'accuracy': 0.7420901246404602}
0.1253	54.0	756	2.0774	{'f1': 0.8302122347066168}	{'accuracy': 0.7392138063279002}
0.1253	55.0	770	2.2485	{'f1': 0.8374622356495468}	{'accuracy': 0.7420901246404602}
0.1253	56.0	784	1.8539	{'f1': 0.8257817485641353}	{'accuracy': 0.738255033557047}
0.1253	57.0	798	1.9967	{'f1': 0.835458409228901}	{'accuracy': 0.7401725790987536}
0.1253	58.0	812	1.7908	{'f1': 0.7898805340829232}	{'accuracy': 0.713326941514861}
0.1253	59.0	826	1.7549	{'f1': 0.8364972443355787}	{'accuracy': 0.7440076701821668}
0.1253	60.0	840	1.7989	{'f1': 0.8368098159509203}	{'accuracy': 0.7449664429530202}
0.1253	61.0	854	2.0937	{'f1': 0.8372372372372373}	{'accuracy': 0.7401725790987536}
0.1253	62.0	868	1.3363	{'f1': 0.7843416370106763}	{'accuracy': 0.7094918504314478}
0.1253	63.0	882	1.4321	{'f1': 0.8367989918084436}	{'accuracy': 0.7516778523489933}
0.1253	64.0	896	1.9803	{'f1': 0.8371257485029939}	{'accuracy': 0.7392138063279002}
0.1253	65.0	910	1.8372	{'f1': 0.8285714285714286}	{'accuracy': 0.7468839884947267}
0.1253	66.0	924	2.3544	{'f1': 0.8377896613190731}	{'accuracy': 0.738255033557047}
0.1253	67.0	938	1.9866	{'f1': 0.8263707571801565}	{'accuracy': 0.7449664429530202}
0.1253	68.0	952	2.7185	{'f1': 0.830842797369994}	{'accuracy': 0.7286673058485139}
0.1253	69.0	966	2.4310	{'f1': 0.8306148055207027}	{'accuracy': 0.7411313518696069}
0.1253	70.0	980	2.4977	{'f1': 0.8305687203791469}	{'accuracy': 0.725790987535954}
0.1253	71.0	994	1.0387	{'f1': 0.8284789644012944}	{'accuracy': 0.7459252157238735}
0.0198	72.0	1008	1.8469	{'f1': 0.8327402135231317}	{'accuracy': 0.7296260786193672}
0.0198	73.0	1022	1.6845	{'f1': 0.8192612137203166}	{'accuracy': 0.7372962607861937}
0.0198	74.0	1036	1.6471	{'f1': 0.8296943231441049}	{'accuracy': 0.738255033557047}
0.0198	75.0	1050	1.5214	{'f1': 0.8132450331125828}	{'accuracy': 0.7296260786193672}
0.0198	76.0	1064	1.7829	{'f1': 0.8236760124610593}	{'accuracy': 0.7286673058485139}
0.0198	77.0	1078	2.2771	{'f1': 0.8293838862559241}	{'accuracy': 0.7238734419942474}
0.0198	78.0	1092	1.5519	{'f1': 0.8101788170563962}	{'accuracy': 0.7353787152444871}
0.0198	79.0	1106	1.5540	{'f1': 0.8368200836820083}	{'accuracy': 0.738255033557047}
0.0198	80.0	1120	1.5794	{'f1': 0.8259473346178547}	{'accuracy': 0.7401725790987536}
0.0198	81.0	1134	1.8846	{'f1': 0.8293284041897719}	{'accuracy': 0.7344199424736337}
0.0198	82.0	1148	1.7147	{'f1': 0.806947227788911}	{'accuracy': 0.7229146692233941}
0.0198	83.0	1162	1.9138	{'f1': 0.8322309079975947}	{'accuracy': 0.7325023969319271}
0.0198	84.0	1176	1.8756	{'f1': 0.8353510895883779}	{'accuracy': 0.7392138063279002}
0.0198	85.0	1190	1.8631	{'f1': 0.8307692307692307}	{'accuracy': 0.7363374880153404}
0.0198	86.0	1204	1.8303	{'f1': 0.822429906542056}	{'accuracy': 0.7267497603068073}
0.0198	87.0	1218	2.0313	{'f1': 0.8185185185185186}	{'accuracy': 0.7181208053691275}
0.0198	88.0	1232	2.5411	{'f1': 0.8382180539273153}	{'accuracy': 0.7353787152444871}
0.0198	89.0	1246	2.1045	{'f1': 0.7961826857532379}	{'accuracy': 0.713326941514861}
0.0198	90.0	1260	2.5877	{'f1': 0.8313397129186604}	{'accuracy': 0.7296260786193672}
0.0198	91.0	1274	2.6338	{'f1': 0.8334335538184005}	{'accuracy': 0.7344199424736337}
0.0198	92.0	1288	2.3250	{'f1': 0.8208092485549132}	{'accuracy': 0.7325023969319271}
0.0198	93.0	1302	2.1145	{'f1': 0.8264150943396226}	{'accuracy': 0.7353787152444871}
0.0198	94.0	1316	2.4299	{'f1': 0.8325301204819276}	{'accuracy': 0.7334611697027804}
0.0198	95.0	1330	2.4146	{'f1': 0.8258706467661692}	{'accuracy': 0.7315436241610739}
0.0198	96.0	1344	2.9848	{'f1': 0.8305183459522424}	{'accuracy': 0.7209971236816874}
0.0198	97.0	1358	2.4440	{'f1': 0.8053156146179401}	{'accuracy': 0.7190795781399808}
0.0198	98.0	1372	2.8039	{'f1': 0.8364928909952606}	{'accuracy': 0.7353787152444871}
0.0198	99.0	1386	2.5005	{'f1': 0.8316109422492403}	{'accuracy': 0.7344199424736337}
0.0198	100.0	1400	1.9877	{'f1': 0.8150991682661549}	{'accuracy': 0.7229146692233941}
0.0198	101.0	1414	1.8385	{'f1': 0.8207119741100324}	{'accuracy': 0.7344199424736337}
0.0198	102.0	1428	1.9646	{'f1': 0.8270580727504787}	{'accuracy': 0.7401725790987536}
0.0198	103.0	1442	2.0479	{'f1': 0.813847158719791}	{'accuracy': 0.7267497603068073}
0.0198	104.0	1456	2.0466	{'f1': 0.842925659472422}	{'accuracy': 0.7488015340364333}
0.0198	105.0	1470	1.4048	{'f1': 0.825249169435216}	{'accuracy': 0.74784276126558}
0.0198	106.0	1484	1.5167	{'f1': 0.8099861303744799}	{'accuracy': 0.7372962607861937}
0.0198	107.0	1498	2.5238	{'f1': 0.8303886925795053}	{'accuracy': 0.7238734419942474}
0.0184	108.0	1512	2.5067	{'f1': 0.7249802994483845}	{'accuracy': 0.6653883029721956}
0.0184	109.0	1526	2.4375	{'f1': 0.8219346888478127}	{'accuracy': 0.7229146692233941}
0.0184	110.0	1540	2.2507	{'f1': 0.7757660167130919}	{'accuracy': 0.6912751677852349}
0.0184	111.0	1554	2.5541	{'f1': 0.8283808368708309}	{'accuracy': 0.7286673058485139}
0.0184	112.0	1568	2.5094	{'f1': 0.8178343949044586}	{'accuracy': 0.725790987535954}
0.0184	113.0	1582	2.9570	{'f1': 0.832841110454814}	{'accuracy': 0.7286673058485139}
0.0184	114.0	1596	2.6984	{'f1': 0.8015768725361367}	{'accuracy': 0.710450623202301}
0.0184	115.0	1610	3.0147	{'f1': 0.8285547103569338}	{'accuracy': 0.7190795781399808}
0.0184	116.0	1624	1.8179	{'f1': 0.838749233599019}	{'accuracy': 0.74784276126558}
0.0184	117.0	1638	1.7351	{'f1': 0.83270911360799}	{'accuracy': 0.7430488974113135}
0.0184	118.0	1652	2.2947	{'f1': 0.8324324324324324}	{'accuracy': 0.7325023969319271}
0.0184	119.0	1666	2.0843	{'f1': 0.8364972443355787}	{'accuracy': 0.7440076701821668}
0.0184	120.0	1680	2.1599	{'f1': 0.828828828828829}	{'accuracy': 0.7267497603068073}
0.0184	121.0	1694	2.1308	{'f1': 0.8296943231441049}	{'accuracy': 0.738255033557047}
0.0184	122.0	1708	2.3121	{'f1': 0.8340477648499695}	{'accuracy': 0.7401725790987536}
0.0184	123.0	1722	2.0896	{'f1': 0.8280961182994454}	{'accuracy': 0.7325023969319271}
0.0184	124.0	1736	1.8344	{'f1': 0.7572254335260116}	{'accuracy': 0.6778523489932886}
0.0184	125.0	1750	2.2451	{'f1': 0.8271752085816448}	{'accuracy': 0.7219558964525408}
0.0184	126.0	1764	2.0323	{'f1': 0.8204488778054864}	{'accuracy': 0.7238734419942474}
0.0184	127.0	1778	2.0922	{'f1': 0.8189274447949527}	{'accuracy': 0.7248322147651006}
0.0184	128.0	1792	2.1871	{'f1': 0.8184110970996217}	{'accuracy': 0.7238734419942474}
0.0184	129.0	1806	2.2503	{'f1': 0.8190954773869347}	{'accuracy': 0.7238734419942474}
0.0184	130.0	1820	2.3041	{'f1': 0.8197747183979974}	{'accuracy': 0.7238734419942474}
0.0184	131.0	1834	2.3421	{'f1': 0.8205128205128204}	{'accuracy': 0.7248322147651006}
0.0184	132.0	1848	2.3944	{'f1': 0.8219862585883824}	{'accuracy': 0.7267497603068073}
0.0184	133.0	1862	2.7387	{'f1': 0.8284866468842729}	{'accuracy': 0.7229146692233941}
0.0184	134.0	1876	2.1785	{'f1': 0.819814241486068}	{'accuracy': 0.7209971236816874}
0.0184	135.0	1890	2.2825	{'f1': 0.8248175182481752}	{'accuracy': 0.7238734419942474}
0.0184	136.0	1904	2.4291	{'f1': 0.8197923029932803}	{'accuracy': 0.7171620325982742}
0.0184	137.0	1918	2.0556	{'f1': 0.7898351648351649}	{'accuracy': 0.7066155321188878}
0.0184	138.0	1932	2.2096	{'f1': 0.8013333333333333}	{'accuracy': 0.7142857142857143}
0.0184	139.0	1946	2.2809	{'f1': 0.806578947368421}	{'accuracy': 0.7181208053691275}
0.0184	140.0	1960	2.4477	{'f1': 0.8181253879577901}	{'accuracy': 0.7190795781399808}
0.0184	141.0	1974	2.6566	{'f1': 0.8214070956103428}	{'accuracy': 0.7152444870565676}
0.0184	142.0	1988	2.0007	{'f1': 0.7727598566308245}	{'accuracy': 0.6960690316395014}
0.0145	143.0	2002	1.9685	{'f1': 0.8298507462686567}	{'accuracy': 0.7267497603068073}
0.0145	144.0	2016	1.8895	{'f1': 0.8132147395171537}	{'accuracy': 0.7181208053691275}
0.0145	145.0	2030	2.0399	{'f1': 0.8075178224238496}	{'accuracy': 0.7152444870565676}
0.0145	146.0	2044	2.4218	{'f1': 0.8258766626360338}	{'accuracy': 0.7238734419942474}
0.0145	147.0	2058	2.4451	{'f1': 0.8150470219435738}	{'accuracy': 0.7171620325982742}
0.0145	148.0	2072	2.4632	{'f1': 0.8098318240620956}	{'accuracy': 0.7181208053691275}
0.0145	149.0	2086	2.4870	{'f1': 0.8091205211726384}	{'accuracy': 0.7190795781399808}
0.0145	150.0	2100	2.5063	{'f1': 0.8080939947780679}	{'accuracy': 0.7181208053691275}
0.0145	151.0	2114	2.5412	{'f1': 0.8126614987080103}	{'accuracy': 0.7219558964525408}
0.0145	152.0	2128	3.0723	{'f1': 0.8242142025611175}	{'accuracy': 0.710450623202301}
0.0145	153.0	2142	2.2632	{'f1': 0.7977606717984603}	{'accuracy': 0.7229146692233941}
0.0145	154.0	2156	2.8640	{'f1': 0.8235988200589972}	{'accuracy': 0.713326941514861}
0.0145	155.0	2170	2.4601	{'f1': 0.8146214099216709}	{'accuracy': 0.7277085330776606}
0.0145	156.0	2184	3.0811	{'f1': 0.8241500586166471}	{'accuracy': 0.7123681687440077}
0.0145	157.0	2198	2.5084	{'f1': 0.8036410923276983}	{'accuracy': 0.710450623202301}
0.0145	158.0	2212	2.7759	{'f1': 0.8209042865531415}	{'accuracy': 0.7075743048897412}
0.0145	159.0	2226	2.3592	{'f1': 0.81625}	{'accuracy': 0.7181208053691275}
0.0145	160.0	2240	2.5965	{'f1': 0.8194103194103194}	{'accuracy': 0.7181208053691275}
0.0145	161.0	2254	3.0944	{'f1': 0.8215330602691633}	{'accuracy': 0.7075743048897412}
0.0145	162.0	2268	3.0794	{'f1': 0.8204826368452031}	{'accuracy': 0.7075743048897412}
0.0145	163.0	2282	3.0692	{'f1': 0.820816085156712}	{'accuracy': 0.7094918504314478}
0.0145	164.0	2296	3.0611	{'f1': 0.8213649851632047}	{'accuracy': 0.7114093959731543}
0.0145	165.0	2310	3.0655	{'f1': 0.8211527035056446}	{'accuracy': 0.7114093959731543}
0.0145	166.0	2324	3.0710	{'f1': 0.8216409036860881}	{'accuracy': 0.7123681687440077}
0.0145	167.0	2338	3.0783	{'f1': 0.8226190476190477}	{'accuracy': 0.7142857142857143}
0.0145	168.0	2352	3.0869	{'f1': 0.8231089934484812}	{'accuracy': 0.7152444870565676}
0.0145	169.0	2366	3.0948	{'f1': 0.8231089934484812}	{'accuracy': 0.7152444870565676}
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0.0013	1349.0	18886	3.4392	{'f1': 0.7839195979899497}	{'accuracy': 0.6701821668264621}
0.0013	1350.0	18900	3.4522	{'f1': 0.7839195979899497}	{'accuracy': 0.6701821668264621}
0.0013	1351.0	18914	3.4642	{'f1': 0.7846829880728186}	{'accuracy': 0.6711409395973155}
0.0013	1352.0	18928	3.4764	{'f1': 0.7846829880728186}	{'accuracy': 0.6711409395973155}
0.0013	1353.0	18942	3.4877	{'f1': 0.7846829880728186}	{'accuracy': 0.6711409395973155}
0.0013	1354.0	18956	3.4992	{'f1': 0.7846829880728186}	{'accuracy': 0.6711409395973155}
0.0013	1355.0	18970	3.6613	{'f1': 0.7413441955193484}	{'accuracy': 0.6347075743048898}
0.0013	1356.0	18984	3.4304	{'f1': 0.7889447236180904}	{'accuracy': 0.6778523489932886}
0.0013	1357.0	18998	3.7710	{'f1': 0.7192118226600985}	{'accuracy': 0.6174496644295302}
0.0035	1358.0	19012	3.0830	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0035	1359.0	19026	3.2844	{'f1': 0.7794676806083649}	{'accuracy': 0.6663470757430489}
0.0035	1360.0	19040	3.4548	{'f1': 0.7761194029850746}	{'accuracy': 0.6692233940556088}
0.0035	1361.0	19054	3.4851	{'f1': 0.7738562091503267}	{'accuracy': 0.6682646212847555}
0.0035	1362.0	19068	3.5073	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1363.0	19082	3.5212	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1364.0	19096	3.5330	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1365.0	19110	3.5445	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1366.0	19124	3.5552	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1367.0	19138	3.5657	{'f1': 0.7714472822527831}	{'accuracy': 0.6653883029721956}
0.0035	1368.0	19152	3.5996	{'f1': 0.794099569760295}	{'accuracy': 0.6788111217641419}
0.0035	1369.0	19166	3.6130	{'f1': 0.7870081199250469}	{'accuracy': 0.673058485139022}
0.0035	1370.0	19180	3.7669	{'f1': 0.7572944297082228}	{'accuracy': 0.6490891658676894}
0.0035	1371.0	19194	3.7945	{'f1': 0.7469553450608932}	{'accuracy': 0.6414189837008629}
0.0035	1372.0	19208	3.6516	{'f1': 0.7740693196405649}	{'accuracy': 0.6625119846596357}
0.0035	1373.0	19222	3.5258	{'f1': 0.79}	{'accuracy': 0.6778523489932886}
0.0035	1374.0	19236	3.5530	{'f1': 0.7843631778058007}	{'accuracy': 0.6720997123681688}
0.0035	1375.0	19250	3.5624	{'f1': 0.7843631778058007}	{'accuracy': 0.6720997123681688}
0.0035	1376.0	19264	3.5753	{'f1': 0.7828282828282828}	{'accuracy': 0.6701821668264621}
0.0035	1377.0	19278	3.5872	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1378.0	19292	3.5985	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1379.0	19306	3.6096	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1380.0	19320	3.6206	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1381.0	19334	3.6311	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1382.0	19348	3.6415	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1383.0	19362	3.6518	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1384.0	19376	3.6621	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1385.0	19390	3.6719	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1386.0	19404	3.6816	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1387.0	19418	3.6910	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
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0.0035	1389.0	19446	3.7088	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1390.0	19460	3.7176	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1391.0	19474	3.7261	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0035	1392.0	19488	3.7345	{'f1': 0.7817836812144212}	{'accuracy': 0.6692233940556088}
0.0017	1393.0	19502	3.7429	{'f1': 0.7820593809222993}	{'accuracy': 0.6692233940556088}
0.0017	1394.0	19516	3.7508	{'f1': 0.7820593809222993}	{'accuracy': 0.6692233940556088}
0.0017	1395.0	19530	3.7587	{'f1': 0.7828282828282828}	{'accuracy': 0.6701821668264621}
0.0017	1396.0	19544	3.7663	{'f1': 0.7828282828282828}	{'accuracy': 0.6701821668264621}
0.0017	1397.0	19558	3.7736	{'f1': 0.7828282828282828}	{'accuracy': 0.6701821668264621}
0.0017	1398.0	19572	3.7810	{'f1': 0.7828282828282828}	{'accuracy': 0.6701821668264621}
0.0017	1399.0	19586	3.7882	{'f1': 0.7820593809222993}	{'accuracy': 0.6692233940556088}
0.0017	1400.0	19600	3.7954	{'f1': 0.7825537294563844}	{'accuracy': 0.6701821668264621}
0.0017	1401.0	19614	3.8028	{'f1': 0.7825537294563844}	{'accuracy': 0.6701821668264621}
0.0017	1402.0	19628	3.8196	{'f1': 0.7798340778557754}	{'accuracy': 0.6692233940556088}
0.0017	1403.0	19642	3.8332	{'f1': 0.7792706333973128}	{'accuracy': 0.6692233940556088}
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0.0017	1405.0	19670	3.8493	{'f1': 0.7792706333973128}	{'accuracy': 0.6692233940556088}
0.0017	1406.0	19684	3.8562	{'f1': 0.7792706333973128}	{'accuracy': 0.6692233940556088}
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0.0017	1408.0	19712	3.8689	{'f1': 0.7792706333973128}	{'accuracy': 0.6692233940556088}
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0.0017	1413.0	19782	3.8995	{'f1': 0.7792706333973128}	{'accuracy': 0.6692233940556088}
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0.0017	1433.0	20062	3.1778	{'f1': 0.7780612244897959}	{'accuracy': 0.6663470757430489}
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0.0017	1436.0	20104	3.2693	{'f1': 0.7796178343949045}	{'accuracy': 0.6682646212847555}
0.0017	1437.0	20118	3.2941	{'f1': 0.7803946530872056}	{'accuracy': 0.6692233940556088}
0.0017	1438.0	20132	3.3159	{'f1': 0.7811704834605597}	{'accuracy': 0.6701821668264621}
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0.0017	1441.0	20174	3.2934	{'f1': 0.7943349753694582}	{'accuracy': 0.6797698945349953}
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0.0017	1443.0	20202	3.2146	{'f1': 0.8016928657799274}	{'accuracy': 0.6855225311601151}
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0.0028	1465.0	20510	3.5947	{'f1': 0.7705235940530057}	{'accuracy': 0.6596356663470757}
0.0028	1466.0	20524	3.6060	{'f1': 0.7705235940530057}	{'accuracy': 0.6596356663470757}
0.0028	1467.0	20538	3.6184	{'f1': 0.7705235940530057}	{'accuracy': 0.6596356663470757}
0.0028	1468.0	20552	3.6330	{'f1': 0.7713178294573643}	{'accuracy': 0.660594439117929}
0.0028	1469.0	20566	3.6444	{'f1': 0.7713178294573643}	{'accuracy': 0.660594439117929}
0.0028	1470.0	20580	3.6550	{'f1': 0.7713178294573643}	{'accuracy': 0.660594439117929}
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0.0028	1472.0	20608	3.6748	{'f1': 0.7713178294573643}	{'accuracy': 0.660594439117929}
0.0028	1473.0	20622	3.6843	{'f1': 0.7713178294573643}	{'accuracy': 0.660594439117929}
0.0028	1474.0	20636	3.6943	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
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0.0028	1476.0	20664	3.7125	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
0.0028	1477.0	20678	3.7214	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
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0.0028	1480.0	20720	3.7472	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
0.0028	1481.0	20734	3.7558	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
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0.0028	1483.0	20762	3.7720	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
0.0028	1484.0	20776	3.7798	{'f1': 0.7721110393802453}	{'accuracy': 0.6615532118887824}
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0.0028	1490.0	20860	3.8228	{'f1': 0.7729032258064517}	{'accuracy': 0.6625119846596357}
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0.0028	1498.0	20972	3.8755	{'f1': 0.7729032258064517}	{'accuracy': 0.6625119846596357}
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0.0	1500.0	21000	3.8820	{'f1': 0.7729032258064517}	{'accuracy': 0.6625119846596357}
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0.0	1508.0	21112	3.9300	{'f1': 0.7729032258064517}	{'accuracy': 0.6625119846596357}
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0.0	1571.0	21994	3.9713	{'f1': 0.7918468190240889}	{'accuracy': 0.6768935762224353}
0.0035	1572.0	22008	3.9761	{'f1': 0.7918468190240889}	{'accuracy': 0.6768935762224353}
0.0035	1573.0	22022	3.9805	{'f1': 0.7918468190240889}	{'accuracy': 0.6768935762224353}
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0.0014	1653.0	23142	4.1969	{'f1': 0.7787042976266837}	{'accuracy': 0.6692233940556088}
0.0014	1654.0	23156	4.2004	{'f1': 0.7787042976266837}	{'accuracy': 0.6692233940556088}
0.0014	1655.0	23170	4.2040	{'f1': 0.7787042976266837}	{'accuracy': 0.6692233940556088}
0.0014	1656.0	23184	4.2082	{'f1': 0.7794871794871795}	{'accuracy': 0.6701821668264621}
0.0014	1657.0	23198	4.2118	{'f1': 0.7794871794871795}	{'accuracy': 0.6701821668264621}
0.0014	1658.0	23212	4.3573	{'f1': 0.7581179589131875}	{'accuracy': 0.6500479386385427}
0.0014	1659.0	23226	4.3151	{'f1': 0.7787042976266837}	{'accuracy': 0.6692233940556088}
0.0014	1660.0	23240	4.3018	{'f1': 0.7866161616161617}	{'accuracy': 0.675934803451582}
0.0014	1661.0	23254	4.3040	{'f1': 0.7874608150470219}	{'accuracy': 0.6749760306807286}
0.0014	1662.0	23268	4.3155	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1663.0	23282	4.3198	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1664.0	23296	4.3228	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1665.0	23310	4.3256	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1666.0	23324	4.3284	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1667.0	23338	4.3313	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1668.0	23352	4.3340	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1669.0	23366	4.3368	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1670.0	23380	4.3394	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1671.0	23394	4.3420	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1672.0	23408	4.3447	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1673.0	23422	4.3474	{'f1': 0.788220551378446}	{'accuracy': 0.675934803451582}
0.0014	1674.0	23436	4.3504	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0014	1675.0	23450	4.3532	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0014	1676.0	23464	4.3559	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0014	1677.0	23478	4.3585	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0014	1678.0	23492	4.3612	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0005	1679.0	23506	4.3638	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0005	1680.0	23520	4.3665	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
0.0005	1681.0	23534	4.3692	{'f1': 0.788979336255479}	{'accuracy': 0.6768935762224353}
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0.0	1852.0	25928	4.0439	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1853.0	25942	4.0463	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1854.0	25956	4.0486	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1855.0	25970	4.0509	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1856.0	25984	4.0531	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1857.0	25998	4.0554	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1858.0	26012	4.0576	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1859.0	26026	4.0599	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1860.0	26040	4.0622	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1861.0	26054	4.0644	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1862.0	26068	4.0665	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1863.0	26082	4.0684	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1864.0	26096	4.0703	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1865.0	26110	4.0723	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1866.0	26124	4.0743	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1867.0	26138	4.0763	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1868.0	26152	4.0782	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1869.0	26166	4.0800	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1870.0	26180	4.0820	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1871.0	26194	4.0839	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1872.0	26208	4.0859	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1873.0	26222	4.0878	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1874.0	26236	4.0898	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1875.0	26250	4.0917	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1876.0	26264	4.0934	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1877.0	26278	4.0953	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1878.0	26292	4.0971	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1879.0	26306	4.0988	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1880.0	26320	4.1006	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1881.0	26334	4.1024	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1882.0	26348	4.1042	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1883.0	26362	4.1060	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1884.0	26376	4.1077	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1885.0	26390	4.1094	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1886.0	26404	4.1110	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1887.0	26418	4.1127	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1888.0	26432	4.1147	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1889.0	26446	4.1164	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1890.0	26460	4.1180	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1891.0	26474	4.1196	{'f1': 0.7712248865845756}	{'accuracy': 0.6615532118887824}
0.0	1892.0	26488	4.1252	{'f1': 0.78428927680798}	{'accuracy': 0.6682646212847555}
0.0001	1893.0	26502	4.1287	{'f1': 0.7883663366336634}	{'accuracy': 0.6720997123681688}
0.0001	1894.0	26516	4.1285	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1895.0	26530	4.1300	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1896.0	26544	4.1316	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1897.0	26558	4.1330	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1898.0	26572	4.1345	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1899.0	26586	4.1360	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1900.0	26600	4.1378	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1901.0	26614	4.1393	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1902.0	26628	4.1407	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1903.0	26642	4.1422	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1904.0	26656	4.1437	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1905.0	26670	4.1450	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1906.0	26684	4.1463	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1907.0	26698	4.1476	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1908.0	26712	4.1489	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1909.0	26726	4.1502	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1910.0	26740	4.1516	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1911.0	26754	4.1529	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1912.0	26768	4.1542	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1913.0	26782	4.1554	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1914.0	26796	4.1566	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1915.0	26810	4.1578	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1916.0	26824	4.1591	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1917.0	26838	4.1602	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1918.0	26852	4.1613	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1919.0	26866	4.1625	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1920.0	26880	4.1637	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1921.0	26894	4.1648	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1922.0	26908	4.1659	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1923.0	26922	4.1670	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1924.0	26936	4.1682	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1925.0	26950	4.1693	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1926.0	26964	4.1705	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1927.0	26978	4.1716	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0001	1928.0	26992	4.1727	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0	1929.0	27006	4.1738	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0	1930.0	27020	4.1749	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0	1931.0	27034	4.1760	{'f1': 0.7903822441430334}	{'accuracy': 0.6740172579098753}
0.0	1932.0	27048	4.1736	{'f1': 0.7888198757763976}	{'accuracy': 0.6740172579098753}
0.0	1933.0	27062	4.1618	{'f1': 0.7855361596009975}	{'accuracy': 0.6701821668264621}
0.0	1934.0	27076	4.1596	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1935.0	27090	4.1599	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1936.0	27104	4.1607	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1937.0	27118	4.1616	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1938.0	27132	4.1625	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1939.0	27146	4.1634	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1940.0	27160	4.1642	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1941.0	27174	4.1651	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1942.0	27188	4.1659	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1943.0	27202	4.1667	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1944.0	27216	4.1676	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1945.0	27230	4.1686	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1946.0	27244	4.1694	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1947.0	27258	4.1702	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1948.0	27272	4.1709	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1949.0	27286	4.1717	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1950.0	27300	4.1724	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1951.0	27314	4.1731	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1952.0	27328	4.1738	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1953.0	27342	4.1745	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1954.0	27356	4.1752	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1955.0	27370	4.1759	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1956.0	27384	4.1765	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1957.0	27398	4.1772	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1958.0	27412	4.1779	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1959.0	27426	4.1786	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1960.0	27440	4.1792	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1961.0	27454	4.1798	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1962.0	27468	4.1803	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1963.0	27482	4.1809	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1964.0	27496	4.1815	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1965.0	27510	4.1821	{'f1': 0.785}	{'accuracy': 0.6701821668264621}
0.0	1966.0	27524	4.1871	{'f1': 0.785490931832395}	{'accuracy': 0.6711409395973155}
0.0	1967.0	27538	4.1892	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1968.0	27552	4.1897	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1969.0	27566	4.1902	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1970.0	27580	4.1906	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1971.0	27594	4.1911	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1972.0	27608	4.1915	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1973.0	27622	4.1919	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1974.0	27636	4.1923	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1975.0	27650	4.1927	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1976.0	27664	4.1930	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1977.0	27678	4.1934	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1978.0	27692	4.1937	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1979.0	27706	4.1940	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1980.0	27720	4.1944	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1981.0	27734	4.1947	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1982.0	27748	4.1950	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1983.0	27762	4.1952	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1984.0	27776	4.1955	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1985.0	27790	4.1957	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1986.0	27804	4.1960	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1987.0	27818	4.1962	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1988.0	27832	4.1964	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1989.0	27846	4.1966	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1990.0	27860	4.1967	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1991.0	27874	4.1969	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1992.0	27888	4.1970	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1993.0	27902	4.1971	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1994.0	27916	4.1973	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1995.0	27930	4.1973	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1996.0	27944	4.1974	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1997.0	27958	4.1975	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1998.0	27972	4.1975	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	1999.0	27986	4.1975	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}
0.0	2000.0	28000	4.1976	{'f1': 0.7852222917971196}	{'accuracy': 0.6711409395973155}

Framework versions

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