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kwwww/
bert-base-uncased-test_2_1000

Name: bert-base-uncased-test_2_1000
Availability: InStock

pytorch

generated_from_trainer

apache-2.0

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bert-base-uncased-test_2_1000

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

Loss: 1.4610
F1: {'f1': 0.8997668997668997}
Accuracy: {'accuracy': 0.8968}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 500

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
No log	1.0	63	0.6391	{'f1': 0.6503930492345883}	{'accuracy': 0.662}
No log	2.0	126	0.4130	{'f1': 0.8169452001554606}	{'accuracy': 0.8116}
No log	3.0	189	0.3583	{'f1': 0.8627889634601043}	{'accuracy': 0.8528}
No log	4.0	252	0.3703	{'f1': 0.8339869281045752}	{'accuracy': 0.8476}
No log	5.0	315	0.3199	{'f1': 0.8757812500000001}	{'accuracy': 0.8728}
No log	6.0	378	0.3347	{'f1': 0.8809710258418166}	{'accuracy': 0.8784}
No log	7.0	441	0.3409	{'f1': 0.8823993685872139}	{'accuracy': 0.8808}
0.3461	8.0	504	0.3439	{'f1': 0.8810572687224669}	{'accuracy': 0.8812}
0.3461	9.0	567	0.3696	{'f1': 0.8848106208512301}	{'accuracy': 0.882}
0.3461	10.0	630	0.3782	{'f1': 0.8891537544696066}	{'accuracy': 0.8884}
0.3461	11.0	693	0.4324	{'f1': 0.8825448613376835}	{'accuracy': 0.8848}
0.3461	12.0	756	0.4460	{'f1': 0.8929276965626235}	{'accuracy': 0.8916}
0.3461	13.0	819	0.4796	{'f1': 0.8898678414096918}	{'accuracy': 0.89}
0.3461	14.0	882	0.4878	{'f1': 0.8905630452022205}	{'accuracy': 0.8896}
0.3461	15.0	945	0.5118	{'f1': 0.8919888401753687}	{'accuracy': 0.8916}
0.1074	16.0	1008	0.5677	{'f1': 0.8877975066112579}	{'accuracy': 0.8812}
0.1074	17.0	1071	0.5654	{'f1': 0.8932270916334661}	{'accuracy': 0.8928}
0.1074	18.0	1134	0.6581	{'f1': 0.8865671641791045}	{'accuracy': 0.8784}
0.1074	19.0	1197	0.6107	{'f1': 0.8948194662480377}	{'accuracy': 0.8928}
0.1074	20.0	1260	0.7440	{'f1': 0.8747913188647746}	{'accuracy': 0.88}
0.1074	21.0	1323	0.6414	{'f1': 0.8981662114709325}	{'accuracy': 0.8956}
0.1074	22.0	1386	0.7005	{'f1': 0.8905168905168905}	{'accuracy': 0.8924}
0.1074	23.0	1449	0.6756	{'f1': 0.8960490985807441}	{'accuracy': 0.8916}
0.0366	24.0	1512	0.6751	{'f1': 0.8974854932301741}	{'accuracy': 0.894}
0.0366	25.0	1575	0.6686	{'f1': 0.9018691588785047}	{'accuracy': 0.8992}
0.0366	26.0	1638	0.7207	{'f1': 0.8952234206471495}	{'accuracy': 0.8912}
0.0366	27.0	1701	0.7898	{'f1': 0.8830313014827018}	{'accuracy': 0.8864}
0.0366	28.0	1764	0.7543	{'f1': 0.8958083832335328}	{'accuracy': 0.8956}
0.0366	29.0	1827	0.8158	{'f1': 0.8938759984785091}	{'accuracy': 0.8884}
0.0366	30.0	1890	0.7586	{'f1': 0.8958818958818958}	{'accuracy': 0.8928}
0.0366	31.0	1953	0.7801	{'f1': 0.8900398406374502}	{'accuracy': 0.8896}
0.0163	32.0	2016	0.8027	{'f1': 0.8953674121405752}	{'accuracy': 0.8952}
0.0163	33.0	2079	0.8411	{'f1': 0.8894817073170732}	{'accuracy': 0.884}
0.0163	34.0	2142	0.8420	{'f1': 0.8962300816167897}	{'accuracy': 0.8932}
0.0163	35.0	2205	0.8386	{'f1': 0.899607843137255}	{'accuracy': 0.8976}
0.0163	36.0	2268	0.8562	{'f1': 0.8937373737373737}	{'accuracy': 0.8948}
0.0163	37.0	2331	0.8530	{'f1': 0.8959491660047657}	{'accuracy': 0.8952}
0.0163	38.0	2394	0.9108	{'f1': 0.8944381384790012}	{'accuracy': 0.8884}
0.0163	39.0	2457	0.8615	{'f1': 0.8949663099484741}	{'accuracy': 0.894}
0.0049	40.0	2520	0.9041	{'f1': 0.8956022944550669}	{'accuracy': 0.8908}
0.0049	41.0	2583	0.8908	{'f1': 0.895615056266977}	{'accuracy': 0.8924}
0.0049	42.0	2646	0.9507	{'f1': 0.8858895705521472}	{'accuracy': 0.8884}
0.0049	43.0	2709	0.9089	{'f1': 0.89921875}	{'accuracy': 0.8968}
0.0049	44.0	2772	0.9126	{'f1': 0.8977361436377831}	{'accuracy': 0.8952}
0.0049	45.0	2835	0.9296	{'f1': 0.8942695722356738}	{'accuracy': 0.8952}
0.0049	46.0	2898	0.9613	{'f1': 0.894736842105263}	{'accuracy': 0.8896}
0.0049	47.0	2961	0.9581	{'f1': 0.8968713789107763}	{'accuracy': 0.8932}
0.0028	48.0	3024	0.9723	{'f1': 0.8982318271119842}	{'accuracy': 0.8964}
0.0028	49.0	3087	0.9448	{'f1': 0.8972254787026182}	{'accuracy': 0.8948}
0.0028	50.0	3150	0.9594	{'f1': 0.8954758190327613}	{'accuracy': 0.8928}
0.0028	51.0	3213	0.9644	{'f1': 0.8925956061838893}	{'accuracy': 0.8944}
0.0028	52.0	3276	1.0120	{'f1': 0.8933893771494076}	{'accuracy': 0.8884}
0.0028	53.0	3339	1.0214	{'f1': 0.8908117016893284}	{'accuracy': 0.894}
0.0028	54.0	3402	0.9771	{'f1': 0.8966588966588968}	{'accuracy': 0.8936}
0.0028	55.0	3465	0.9867	{'f1': 0.8975180144115293}	{'accuracy': 0.8976}
0.0106	56.0	3528	1.0040	{'f1': 0.8982371794871795}	{'accuracy': 0.8984}
0.0106	57.0	3591	0.9824	{'f1': 0.8942006269592476}	{'accuracy': 0.892}
0.0106	58.0	3654	1.0175	{'f1': 0.8932190179267343}	{'accuracy': 0.8904}
0.0106	59.0	3717	0.9887	{'f1': 0.8977361436377831}	{'accuracy': 0.8952}
0.0106	60.0	3780	1.2662	{'f1': 0.8786885245901639}	{'accuracy': 0.8668}
0.0106	61.0	3843	1.0422	{'f1': 0.8906882591093117}	{'accuracy': 0.892}
0.0106	62.0	3906	1.0669	{'f1': 0.8898208158597025}	{'accuracy': 0.8844}
0.0106	63.0	3969	1.0097	{'f1': 0.8998482549317147}	{'accuracy': 0.8944}
0.0135	64.0	4032	0.9358	{'f1': 0.8980224893369523}	{'accuracy': 0.8948}
0.0135	65.0	4095	0.9722	{'f1': 0.9009287925696595}	{'accuracy': 0.8976}
0.0135	66.0	4158	1.0900	{'f1': 0.8904672897196262}	{'accuracy': 0.8828}
0.0135	67.0	4221	1.1846	{'f1': 0.8727735368956743}	{'accuracy': 0.88}
0.0135	68.0	4284	1.0357	{'f1': 0.8887077048512027}	{'accuracy': 0.8908}
0.0135	69.0	4347	1.1368	{'f1': 0.8869113830181683}	{'accuracy': 0.878}
0.0135	70.0	4410	0.9984	{'f1': 0.8957836117740653}	{'accuracy': 0.8952}
0.0135	71.0	4473	1.0332	{'f1': 0.8924302788844621}	{'accuracy': 0.892}
0.0049	72.0	4536	1.0703	{'f1': 0.8883809523809523}	{'accuracy': 0.8828}
0.0049	73.0	4599	1.1153	{'f1': 0.8913125235050771}	{'accuracy': 0.8844}
0.0049	74.0	4662	1.0695	{'f1': 0.8971820258948972}	{'accuracy': 0.892}
0.0049	75.0	4725	1.0327	{'f1': 0.8959875340864822}	{'accuracy': 0.8932}
0.0049	76.0	4788	1.0359	{'f1': 0.8995327102803738}	{'accuracy': 0.8968}
0.0049	77.0	4851	1.1397	{'f1': 0.8919330289193302}	{'accuracy': 0.8864}
0.0049	78.0	4914	1.0799	{'f1': 0.8905453118870146}	{'accuracy': 0.8884}
0.0049	79.0	4977	1.1109	{'f1': 0.8918918918918918}	{'accuracy': 0.888}
0.0045	80.0	5040	1.1067	{'f1': 0.8906851424172441}	{'accuracy': 0.8864}
0.0045	81.0	5103	1.0877	{'f1': 0.8940055577610163}	{'accuracy': 0.8932}
0.0045	82.0	5166	1.1170	{'f1': 0.8876221498371335}	{'accuracy': 0.8896}
0.0045	83.0	5229	1.1236	{'f1': 0.8926940639269406}	{'accuracy': 0.8872}
0.0045	84.0	5292	1.0894	{'f1': 0.892651019622932}	{'accuracy': 0.8884}
0.0045	85.0	5355	1.0890	{'f1': 0.8936825885978429}	{'accuracy': 0.8896}
0.0045	86.0	5418	1.0614	{'f1': 0.8964974419519874}	{'accuracy': 0.8948}
0.0045	87.0	5481	1.1658	{'f1': 0.8847583643122676}	{'accuracy': 0.8884}
0.0025	88.0	5544	1.1107	{'f1': 0.8904333605887164}	{'accuracy': 0.8928}
0.0025	89.0	5607	1.1438	{'f1': 0.8873771730914589}	{'accuracy': 0.8808}
0.0025	90.0	5670	0.9925	{'f1': 0.8984251968503937}	{'accuracy': 0.8968}
0.0025	91.0	5733	0.9887	{'f1': 0.8971098265895954}	{'accuracy': 0.8932}
0.0025	92.0	5796	1.0241	{'f1': 0.8888888888888887}	{'accuracy': 0.888}
0.0025	93.0	5859	1.2746	{'f1': 0.8829160530191459}	{'accuracy': 0.8728}
0.0025	94.0	5922	1.0502	{'f1': 0.8964686998394863}	{'accuracy': 0.8968}
0.0025	95.0	5985	1.1087	{'f1': 0.8886148787505137}	{'accuracy': 0.8916}
0.0084	96.0	6048	1.2064	{'f1': 0.885273258239466}	{'accuracy': 0.89}
0.0084	97.0	6111	1.0969	{'f1': 0.8986852281515855}	{'accuracy': 0.8952}
0.0084	98.0	6174	1.1267	{'f1': 0.8960739030023094}	{'accuracy': 0.892}
0.0084	99.0	6237	1.1098	{'f1': 0.897625535227715}	{'accuracy': 0.8948}
0.0084	100.0	6300	1.0892	{'f1': 0.8999199359487591}	{'accuracy': 0.9}
0.0084	101.0	6363	1.2852	{'f1': 0.8869951834012597}	{'accuracy': 0.878}
0.0084	102.0	6426	1.1348	{'f1': 0.8925869894099848}	{'accuracy': 0.8864}
0.0084	103.0	6489	1.1357	{'f1': 0.8972602739726027}	{'accuracy': 0.892}
0.0038	104.0	6552	1.0627	{'f1': 0.899266692396758}	{'accuracy': 0.8956}
0.0038	105.0	6615	1.1244	{'f1': 0.8947566955865711}	{'accuracy': 0.8884}
0.0038	106.0	6678	1.0451	{'f1': 0.9015181004281821}	{'accuracy': 0.8988}
0.0038	107.0	6741	1.0424	{'f1': 0.9019914096056229}	{'accuracy': 0.8996}
0.0038	108.0	6804	1.3424	{'f1': 0.8836869056327726}	{'accuracy': 0.8728}
0.0038	109.0	6867	0.9600	{'f1': 0.8899876390605687}	{'accuracy': 0.8932}
0.0038	110.0	6930	0.9558	{'f1': 0.8953263808420239}	{'accuracy': 0.8916}
0.0038	111.0	6993	0.9831	{'f1': 0.9020230067433558}	{'accuracy': 0.9012}
0.0131	112.0	7056	1.0165	{'f1': 0.8948995363214837}	{'accuracy': 0.8912}
0.0131	113.0	7119	1.0627	{'f1': 0.8917925683952633}	{'accuracy': 0.894}
0.0131	114.0	7182	1.0546	{'f1': 0.8974854932301741}	{'accuracy': 0.894}
0.0131	115.0	7245	1.0036	{'f1': 0.9014195583596215}	{'accuracy': 0.9}
0.0131	116.0	7308	1.0108	{'f1': 0.8992609879424348}	{'accuracy': 0.8964}
0.0131	117.0	7371	1.0130	{'f1': 0.8993392926544889}	{'accuracy': 0.8964}
0.0131	118.0	7434	1.0153	{'f1': 0.9045383411580593}	{'accuracy': 0.9024}
0.0131	119.0	7497	0.9937	{'f1': 0.9042386185243328}	{'accuracy': 0.9024}
0.0019	120.0	7560	1.0257	{'f1': 0.8997310795236265}	{'accuracy': 0.8956}
0.0019	121.0	7623	1.0452	{'f1': 0.9006163328197226}	{'accuracy': 0.8968}
0.0019	122.0	7686	1.0803	{'f1': 0.8987390141383262}	{'accuracy': 0.894}
0.0019	123.0	7749	1.0830	{'f1': 0.8982402448355011}	{'accuracy': 0.8936}
0.0019	124.0	7812	1.0769	{'f1': 0.8994627782041443}	{'accuracy': 0.8952}
0.0019	125.0	7875	1.0732	{'f1': 0.9004994237418363}	{'accuracy': 0.8964}
0.0019	126.0	7938	1.0907	{'f1': 0.897709923664122}	{'accuracy': 0.8928}
0.0015	127.0	8001	1.0586	{'f1': 0.9000385951370128}	{'accuracy': 0.8964}
0.0015	128.0	8064	1.1171	{'f1': 0.8921689216892169}	{'accuracy': 0.8948}
0.0015	129.0	8127	1.0571	{'f1': 0.8964127367996775}	{'accuracy': 0.8972}
0.0015	130.0	8190	1.1986	{'f1': 0.8960546282245827}	{'accuracy': 0.8904}
0.0015	131.0	8253	1.7163	{'f1': 0.8677361853832442}	{'accuracy': 0.8516}
0.0015	132.0	8316	1.1918	{'f1': 0.8963929393706831}	{'accuracy': 0.892}
0.0015	133.0	8379	1.1048	{'f1': 0.8983050847457626}	{'accuracy': 0.8992}
0.0015	134.0	8442	1.2585	{'f1': 0.8794926004228331}	{'accuracy': 0.886}
0.0054	135.0	8505	1.1483	{'f1': 0.8942229454841335}	{'accuracy': 0.896}
0.0054	136.0	8568	1.1223	{'f1': 0.8962558502340094}	{'accuracy': 0.8936}
0.0054	137.0	8631	1.1952	{'f1': 0.899236641221374}	{'accuracy': 0.8944}
0.0054	138.0	8694	1.1595	{'f1': 0.9013107170393215}	{'accuracy': 0.8976}
0.0054	139.0	8757	1.1580	{'f1': 0.900611620795107}	{'accuracy': 0.896}
0.0054	140.0	8820	1.1509	{'f1': 0.9002302379125094}	{'accuracy': 0.896}
0.0054	141.0	8883	1.1632	{'f1': 0.8997686969930608}	{'accuracy': 0.896}
0.0054	142.0	8946	1.3198	{'f1': 0.8982035928143713}	{'accuracy': 0.8912}
0.0068	143.0	9009	1.1653	{'f1': 0.89860535243121}	{'accuracy': 0.8924}
0.0068	144.0	9072	1.1224	{'f1': 0.8996566196108357}	{'accuracy': 0.8948}
0.0068	145.0	9135	1.1151	{'f1': 0.8990053557765876}	{'accuracy': 0.8944}
0.0068	146.0	9198	1.1623	{'f1': 0.8887036265110463}	{'accuracy': 0.8932}
0.0068	147.0	9261	1.0910	{'f1': 0.8955582232893158}	{'accuracy': 0.8956}
0.0068	148.0	9324	1.0941	{'f1': 0.8952837729816147}	{'accuracy': 0.8952}
0.0068	149.0	9387	1.1096	{'f1': 0.8960063266113089}	{'accuracy': 0.8948}
0.0068	150.0	9450	1.1112	{'f1': 0.8960063266113089}	{'accuracy': 0.8948}
0.0009	151.0	9513	1.1133	{'f1': 0.8963607594936709}	{'accuracy': 0.8952}
0.0009	152.0	9576	1.1185	{'f1': 0.8973954222573007}	{'accuracy': 0.896}
0.0009	153.0	9639	1.1276	{'f1': 0.8983915260886622}	{'accuracy': 0.8964}
0.0009	154.0	9702	1.0736	{'f1': 0.8979753870583566}	{'accuracy': 0.8972}
0.0009	155.0	9765	1.1090	{'f1': 0.8929011079195732}	{'accuracy': 0.8956}
0.0009	156.0	9828	1.1977	{'f1': 0.8971036585365852}	{'accuracy': 0.892}
0.0009	157.0	9891	1.1388	{'f1': 0.8963317384370016}	{'accuracy': 0.896}
0.0009	158.0	9954	1.1264	{'f1': 0.8970528865563181}	{'accuracy': 0.898}
0.0045	159.0	10017	1.1199	{'f1': 0.8945483485873458}	{'accuracy': 0.894}
0.0045	160.0	10080	1.1508	{'f1': 0.9002759164367363}	{'accuracy': 0.8988}
0.0045	161.0	10143	1.1578	{'f1': 0.9007572738142686}	{'accuracy': 0.9004}
0.0045	162.0	10206	1.1629	{'f1': 0.9019762845849801}	{'accuracy': 0.9008}
0.0045	163.0	10269	1.1723	{'f1': 0.901821060965954}	{'accuracy': 0.9008}
0.0045	164.0	10332	1.0742	{'f1': 0.8987885892926926}	{'accuracy': 0.8964}
0.0045	165.0	10395	1.0640	{'f1': 0.8964159117762899}	{'accuracy': 0.8948}
0.0045	166.0	10458	1.0580	{'f1': 0.8969601263324122}	{'accuracy': 0.8956}
0.0039	167.0	10521	1.0604	{'f1': 0.8972332015810278}	{'accuracy': 0.896}
0.0039	168.0	10584	1.0631	{'f1': 0.8973954222573007}	{'accuracy': 0.896}
0.0039	169.0	10647	1.1243	{'f1': 0.8875205254515598}	{'accuracy': 0.8904}
0.0039	170.0	10710	1.1319	{'f1': 0.8882521489971348}	{'accuracy': 0.8908}
0.0039	171.0	10773	1.0792	{'f1': 0.8981191222570533}	{'accuracy': 0.896}
0.0039	172.0	10836	1.0852	{'f1': 0.9002338269680438}	{'accuracy': 0.8976}
0.0039	173.0	10899	1.1632	{'f1': 0.9000757002271007}	{'accuracy': 0.8944}
0.0039	174.0	10962	1.1744	{'f1': 0.8983505945531262}	{'accuracy': 0.894}
0.0002	175.0	11025	1.3889	{'f1': 0.8917716827279467}	{'accuracy': 0.8832}
0.0002	176.0	11088	1.3635	{'f1': 0.8935532233883059}	{'accuracy': 0.8864}
0.0002	177.0	11151	1.3682	{'f1': 0.8956228956228955}	{'accuracy': 0.8884}
0.0002	178.0	11214	1.2081	{'f1': 0.9003544702638834}	{'accuracy': 0.8988}
0.0002	179.0	11277	1.2294	{'f1': 0.8994875837603469}	{'accuracy': 0.898}
0.0002	180.0	11340	1.2316	{'f1': 0.8994875837603469}	{'accuracy': 0.898}
0.0002	181.0	11403	1.2324	{'f1': 0.8994875837603469}	{'accuracy': 0.898}
0.0002	182.0	11466	1.2332	{'f1': 0.8994875837603469}	{'accuracy': 0.898}
0.0015	183.0	11529	1.2340	{'f1': 0.8994875837603469}	{'accuracy': 0.898}
0.0015	184.0	11592	1.2357	{'f1': 0.8999211977935383}	{'accuracy': 0.8984}
0.0015	185.0	11655	1.2370	{'f1': 0.9003544702638834}	{'accuracy': 0.8988}
0.0015	186.0	11718	1.2666	{'f1': 0.8931726907630522}	{'accuracy': 0.8936}
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0.0011	379.0	23877	1.3151	{'f1': 0.9049128367670365}	{'accuracy': 0.904}
0.0011	380.0	23940	1.3154	{'f1': 0.9053465346534653}	{'accuracy': 0.9044}
0.0	381.0	24003	1.3160	{'f1': 0.9058544303797469}	{'accuracy': 0.9048}
0.0	382.0	24066	1.3162	{'f1': 0.9058544303797469}	{'accuracy': 0.9048}
0.0	383.0	24129	1.3170	{'f1': 0.9052132701421801}	{'accuracy': 0.904}
0.0	384.0	24192	1.3179	{'f1': 0.9054962435745354}	{'accuracy': 0.9044}
0.0	385.0	24255	1.3185	{'f1': 0.9054962435745354}	{'accuracy': 0.9044}
0.0	386.0	24318	1.3192	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	387.0	24381	1.3193	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	388.0	24444	1.3202	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	389.0	24507	1.3209	{'f1': 0.9052132701421801}	{'accuracy': 0.904}
0.0	390.0	24570	1.3214	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	391.0	24633	1.3219	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	392.0	24696	1.3221	{'f1': 0.9055709205847491}	{'accuracy': 0.9044}
0.0	393.0	24759	1.3225	{'f1': 0.9052132701421801}	{'accuracy': 0.904}
0.0	394.0	24822	1.3229	{'f1': 0.904498816101026}	{'accuracy': 0.9032}
0.0	395.0	24885	1.3231	{'f1': 0.904498816101026}	{'accuracy': 0.9032}
0.0	396.0	24948	1.3235	{'f1': 0.9049309664694279}	{'accuracy': 0.9036}
0.0	397.0	25011	1.3659	{'f1': 0.9022498060512025}	{'accuracy': 0.8992}
0.0	398.0	25074	1.3666	{'f1': 0.9022498060512025}	{'accuracy': 0.8992}
0.0	399.0	25137	1.4143	{'f1': 0.9029940119760479}	{'accuracy': 0.9028}
0.0	400.0	25200	1.4131	{'f1': 0.9034317637669593}	{'accuracy': 0.9032}
0.0	401.0	25263	1.4131	{'f1': 0.9030714000797766}	{'accuracy': 0.9028}
0.0	402.0	25326	1.4129	{'f1': 0.9031486648066959}	{'accuracy': 0.9028}
0.0	403.0	25389	1.4098	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	404.0	25452	1.4097	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	405.0	25515	1.4095	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	406.0	25578	1.4094	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	407.0	25641	1.4089	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	408.0	25704	1.4086	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	409.0	25767	1.4076	{'f1': 0.9036624203821655}	{'accuracy': 0.9032}
0.0	410.0	25830	1.4376	{'f1': 0.8995983935742972}	{'accuracy': 0.9}
0.0	411.0	25893	1.4380	{'f1': 0.8995983935742972}	{'accuracy': 0.9}
0.0	412.0	25956	1.4953	{'f1': 0.899735149451381}	{'accuracy': 0.894}
0.0013	413.0	26019	1.4050	{'f1': 0.9016459253311923}	{'accuracy': 0.902}
0.0013	414.0	26082	1.4062	{'f1': 0.9023704298915227}	{'accuracy': 0.9028}
0.0013	415.0	26145	1.4062	{'f1': 0.9020080321285141}	{'accuracy': 0.9024}
0.0013	416.0	26208	1.4030	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0013	417.0	26271	1.4031	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0013	418.0	26334	1.4032	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0013	419.0	26397	1.4030	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0013	420.0	26460	1.4031	{'f1': 0.9028213166144201}	{'accuracy': 0.9008}
0.0	421.0	26523	1.4031	{'f1': 0.9028213166144201}	{'accuracy': 0.9008}
0.0	422.0	26586	1.4032	{'f1': 0.9028213166144201}	{'accuracy': 0.9008}
0.0	423.0	26649	1.4032	{'f1': 0.9028213166144201}	{'accuracy': 0.9008}
0.0	424.0	26712	1.4037	{'f1': 0.9024676850763808}	{'accuracy': 0.9004}
0.0	425.0	26775	1.4039	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0	426.0	26838	1.4040	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0	427.0	26901	1.4041	{'f1': 0.9021143304620203}	{'accuracy': 0.9}
0.0	428.0	26964	1.4042	{'f1': 0.9027450980392158}	{'accuracy': 0.9008}
0.0	429.0	27027	1.4042	{'f1': 0.902391219129753}	{'accuracy': 0.9004}
0.0	430.0	27090	1.4044	{'f1': 0.902391219129753}	{'accuracy': 0.9004}
0.0	431.0	27153	1.4046	{'f1': 0.9023146331894862}	{'accuracy': 0.9004}
0.0	432.0	27216	1.4048	{'f1': 0.9023146331894862}	{'accuracy': 0.9004}
0.0	433.0	27279	1.4287	{'f1': 0.9011254019292605}	{'accuracy': 0.9016}
0.0	434.0	27342	1.4161	{'f1': 0.902633679169992}	{'accuracy': 0.9024}
0.0	435.0	27405	1.4081	{'f1': 0.9037787300350604}	{'accuracy': 0.9012}
0.0	436.0	27468	1.4085	{'f1': 0.9037787300350604}	{'accuracy': 0.9012}
0.0005	437.0	27531	1.4085	{'f1': 0.9037787300350604}	{'accuracy': 0.9012}
0.0005	438.0	27594	1.4068	{'f1': 0.9029239766081871}	{'accuracy': 0.9004}
0.0005	439.0	27657	1.4066	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0005	440.0	27720	1.4066	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0005	441.0	27783	1.4067	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0005	442.0	27846	1.4068	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0005	443.0	27909	1.4069	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0005	444.0	27972	1.4070	{'f1': 0.9032761310452418}	{'accuracy': 0.9008}
0.0	445.0	28035	1.4073	{'f1': 0.9029239766081871}	{'accuracy': 0.9004}
0.0	446.0	28098	1.5189	{'f1': 0.8943488943488943}	{'accuracy': 0.8968}
0.0	447.0	28161	1.5200	{'f1': 0.8947152806226956}	{'accuracy': 0.8972}
0.0	448.0	28224	1.5197	{'f1': 0.8947152806226956}	{'accuracy': 0.8972}
0.0	449.0	28287	1.5195	{'f1': 0.8943488943488943}	{'accuracy': 0.8968}
0.0	450.0	28350	1.4609	{'f1': 0.9018974566007266}	{'accuracy': 0.9028}
0.0	451.0	28413	1.4473	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	452.0	28476	1.4397	{'f1': 0.9014423076923077}	{'accuracy': 0.9016}
0.0	453.0	28539	1.4506	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	454.0	28602	1.4508	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	455.0	28665	1.4507	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	456.0	28728	1.4507	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	457.0	28791	1.4507	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	458.0	28854	1.4504	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	459.0	28917	1.4496	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	460.0	28980	1.4493	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	461.0	29043	1.4493	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	462.0	29106	1.4493	{'f1': 0.9019292604501606}	{'accuracy': 0.9024}
0.0	463.0	29169	1.4808	{'f1': 0.9001156961048979}	{'accuracy': 0.8964}
0.0	464.0	29232	1.4812	{'f1': 0.9001156961048979}	{'accuracy': 0.8964}
0.0	465.0	29295	1.4813	{'f1': 0.9001156961048979}	{'accuracy': 0.8964}
0.0	466.0	29358	1.4789	{'f1': 0.9000385951370128}	{'accuracy': 0.8964}
0.0	467.0	29421	1.4788	{'f1': 0.9000385951370128}	{'accuracy': 0.8964}
0.0	468.0	29484	1.4788	{'f1': 0.9000385951370128}	{'accuracy': 0.8964}
0.0	469.0	29547	1.4788	{'f1': 0.9000385951370128}	{'accuracy': 0.8964}
0.0	470.0	29610	1.4783	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	471.0	29673	1.4783	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	472.0	29736	1.4781	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	473.0	29799	1.4782	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	474.0	29862	1.4781	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	475.0	29925	1.4781	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	476.0	29988	1.4780	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	477.0	30051	1.4780	{'f1': 0.9003861003861003}	{'accuracy': 0.8968}
0.0	478.0	30114	1.4755	{'f1': 0.8999613750482811}	{'accuracy': 0.8964}
0.0	479.0	30177	1.4747	{'f1': 0.9006571318129107}	{'accuracy': 0.8972}
0.0	480.0	30240	1.4746	{'f1': 0.9006571318129107}	{'accuracy': 0.8972}
0.0	481.0	30303	1.4747	{'f1': 0.9006571318129107}	{'accuracy': 0.8972}
0.0	482.0	30366	1.4748	{'f1': 0.9006571318129107}	{'accuracy': 0.8972}
0.0	483.0	30429	1.4750	{'f1': 0.9006571318129107}	{'accuracy': 0.8972}
0.0	484.0	30492	1.4714	{'f1': 0.9005032907471932}	{'accuracy': 0.8972}
0.0	485.0	30555	1.4707	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	486.0	30618	1.4709	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	487.0	30681	1.4709	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	488.0	30744	1.4709	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	489.0	30807	1.4709	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	490.0	30870	1.4709	{'f1': 0.900852052672347}	{'accuracy': 0.8976}
0.0	491.0	30933	1.4618	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	492.0	30996	1.4614	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	493.0	31059	1.4614	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	494.0	31122	1.4614	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	495.0	31185	1.4614	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	496.0	31248	1.4613	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	497.0	31311	1.4613	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	498.0	31374	1.4610	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	499.0	31437	1.4610	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}
0.0	500.0	31500	1.4610	{'f1': 0.8997668997668997}	{'accuracy': 0.8968}

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

kwwww/bert-base-uncased-test_2_1000

bert-base-uncased-test_2_1000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kwwww/
bert-base-uncased-test_2_1000