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kwwww/
bert-base-uncased-test_2_500

Name: bert-base-uncased-test_2_500
Availability: InStock

pytorch

generated_from_trainer

apache-2.0

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bert-base-uncased-test_2_500

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: 1.3242
F1: {'f1': 0.8878243512974051}
Accuracy: {'accuracy': 0.8876}

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	32	0.6801	{'f1': 0.253315649867374}	{'accuracy': 0.5496}
No log	2.0	64	0.6567	{'f1': 0.395820528580209}	{'accuracy': 0.6068}
No log	3.0	96	0.5299	{'f1': 0.7167362076958739}	{'accuracy': 0.7556}
No log	4.0	128	0.3719	{'f1': 0.8450704225352113}	{'accuracy': 0.8416}
No log	5.0	160	0.3542	{'f1': 0.8584795321637427}	{'accuracy': 0.8548}
No log	6.0	192	0.3509	{'f1': 0.8587708587708587}	{'accuracy': 0.8612}
No log	7.0	224	0.3426	{'f1': 0.8642370845014016}	{'accuracy': 0.8644}
No log	8.0	256	0.3844	{'f1': 0.8569065343258891}	{'accuracy': 0.8616}
No log	9.0	288	0.3601	{'f1': 0.8700696055684455}	{'accuracy': 0.8656}
No log	10.0	320	0.4025	{'f1': 0.8663141993957705}	{'accuracy': 0.8584}
No log	11.0	352	0.4094	{'f1': 0.8730600875447672}	{'accuracy': 0.8724}
No log	12.0	384	0.4316	{'f1': 0.8725529364762286}	{'accuracy': 0.8724}
No log	13.0	416	0.4566	{'f1': 0.8772763262074426}	{'accuracy': 0.876}
No log	14.0	448	0.5450	{'f1': 0.8553406223717409}	{'accuracy': 0.8624}
No log	15.0	480	0.4764	{'f1': 0.8766692851531814}	{'accuracy': 0.8744}
0.2904	16.0	512	0.5232	{'f1': 0.8812600969305331}	{'accuracy': 0.8824}
0.2904	17.0	544	0.5538	{'f1': 0.8764302059496568}	{'accuracy': 0.8704}
0.2904	18.0	576	0.5426	{'f1': 0.8780487804878049}	{'accuracy': 0.874}
0.2904	19.0	608	0.5473	{'f1': 0.876509544215037}	{'accuracy': 0.8732}
0.2904	20.0	640	0.5786	{'f1': 0.8822605965463108}	{'accuracy': 0.88}
0.2904	21.0	672	0.6041	{'f1': 0.883197516492045}	{'accuracy': 0.8796}
0.2904	22.0	704	0.6239	{'f1': 0.8787061994609163}	{'accuracy': 0.874}
0.2904	23.0	736	0.6593	{'f1': 0.881688018085908}	{'accuracy': 0.8744}
0.2904	24.0	768	0.6500	{'f1': 0.882051282051282}	{'accuracy': 0.8804}
0.2904	25.0	800	0.7181	{'f1': 0.8694581280788177}	{'accuracy': 0.8728}
0.2904	26.0	832	0.6766	{'f1': 0.8800940438871473}	{'accuracy': 0.8776}
0.2904	27.0	864	0.7055	{'f1': 0.8803789972364785}	{'accuracy': 0.8788}
0.2904	28.0	896	0.7455	{'f1': 0.876979293544458}	{'accuracy': 0.8788}
0.2904	29.0	928	0.7227	{'f1': 0.8797443068318018}	{'accuracy': 0.8796}
0.2904	30.0	960	0.7445	{'f1': 0.8800635424940428}	{'accuracy': 0.8792}
0.2904	31.0	992	0.7485	{'f1': 0.884942084942085}	{'accuracy': 0.8808}
0.0294	32.0	1024	0.7603	{'f1': 0.8833333333333332}	{'accuracy': 0.8824}
0.0294	33.0	1056	0.7681	{'f1': 0.8843700159489634}	{'accuracy': 0.884}
0.0294	34.0	1088	0.8303	{'f1': 0.8727871552079044}	{'accuracy': 0.8764}
0.0294	35.0	1120	0.8575	{'f1': 0.8669991687448046}	{'accuracy': 0.872}
0.0294	36.0	1152	0.8227	{'f1': 0.8755573571139035}	{'accuracy': 0.8772}
0.0294	37.0	1184	0.8699	{'f1': 0.8705302096177558}	{'accuracy': 0.874}
0.0294	38.0	1216	0.8056	{'f1': 0.8847205939820243}	{'accuracy': 0.882}
0.0294	39.0	1248	0.8177	{'f1': 0.8847058823529411}	{'accuracy': 0.8824}
0.0294	40.0	1280	0.8988	{'f1': 0.874031797798614}	{'accuracy': 0.8764}
0.0294	41.0	1312	0.9963	{'f1': 0.8571428571428571}	{'accuracy': 0.8648}
0.0294	42.0	1344	0.9049	{'f1': 0.8857356235997013}	{'accuracy': 0.8776}
0.0294	43.0	1376	0.8490	{'f1': 0.8865820838139177}	{'accuracy': 0.882}
0.0294	44.0	1408	0.8760	{'f1': 0.8744484556758926}	{'accuracy': 0.8748}
0.0294	45.0	1440	1.0835	{'f1': 0.8533561351004704}	{'accuracy': 0.8628}
0.0294	46.0	1472	0.8930	{'f1': 0.8805132317562148}	{'accuracy': 0.8808}
0.0059	47.0	1504	0.9490	{'f1': 0.8773507767784138}	{'accuracy': 0.88}
0.0059	48.0	1536	0.9000	{'f1': 0.8845541401273885}	{'accuracy': 0.884}
0.0059	49.0	1568	0.9667	{'f1': 0.8729055986922762}	{'accuracy': 0.8756}
0.0059	50.0	1600	0.9061	{'f1': 0.8838482596793117}	{'accuracy': 0.8812}
0.0059	51.0	1632	0.9607	{'f1': 0.8744412840308817}	{'accuracy': 0.8764}
0.0059	52.0	1664	0.9181	{'f1': 0.8868288914638857}	{'accuracy': 0.8828}
0.0059	53.0	1696	0.9219	{'f1': 0.88800949742778}	{'accuracy': 0.8868}
0.0059	54.0	1728	0.9399	{'f1': 0.8876881512929371}	{'accuracy': 0.8836}
0.0059	55.0	1760	0.9157	{'f1': 0.888201160541586}	{'accuracy': 0.8844}
0.0059	56.0	1792	0.9330	{'f1': 0.8840927258193446}	{'accuracy': 0.884}
0.0059	57.0	1824	0.9414	{'f1': 0.8902392707937713}	{'accuracy': 0.8844}
0.0059	58.0	1856	0.9542	{'f1': 0.8764135702746365}	{'accuracy': 0.8776}
0.0059	59.0	1888	0.9881	{'f1': 0.8753056234718826}	{'accuracy': 0.8776}
0.0059	60.0	1920	0.9599	{'f1': 0.8808373590982287}	{'accuracy': 0.8816}
0.0059	61.0	1952	0.9520	{'f1': 0.8825651302605211}	{'accuracy': 0.8828}
0.0059	62.0	1984	0.9344	{'f1': 0.8832088084939048}	{'accuracy': 0.8812}
0.0043	63.0	2016	0.9675	{'f1': 0.8797748291113793}	{'accuracy': 0.8804}
0.0043	64.0	2048	0.9444	{'f1': 0.8871834228702994}	{'accuracy': 0.8824}
0.0043	65.0	2080	0.9464	{'f1': 0.882775119617225}	{'accuracy': 0.8824}
0.0043	66.0	2112	0.9734	{'f1': 0.8813559322033898}	{'accuracy': 0.8824}
0.0043	67.0	2144	0.9715	{'f1': 0.8827309236947791}	{'accuracy': 0.8832}
0.0043	68.0	2176	0.9423	{'f1': 0.8898539281484406}	{'accuracy': 0.8884}
0.0043	69.0	2208	0.9447	{'f1': 0.886983632112237}	{'accuracy': 0.884}
0.0043	70.0	2240	0.9595	{'f1': 0.8885448916408668}	{'accuracy': 0.8848}
0.0043	71.0	2272	0.9721	{'f1': 0.8872180451127819}	{'accuracy': 0.886}
0.0043	72.0	2304	0.9750	{'f1': 0.8873072360616844}	{'accuracy': 0.886}
0.0043	73.0	2336	1.0369	{'f1': 0.8771358828315704}	{'accuracy': 0.8792}
0.0043	74.0	2368	1.0656	{'f1': 0.8744339234252778}	{'accuracy': 0.878}
0.0043	75.0	2400	1.0304	{'f1': 0.88}	{'accuracy': 0.8812}
0.0043	76.0	2432	1.0651	{'f1': 0.8744872846595569}	{'accuracy': 0.8776}
0.0043	77.0	2464	1.0023	{'f1': 0.8921374950612406}	{'accuracy': 0.8908}
0.0043	78.0	2496	0.9992	{'f1': 0.8890643505724438}	{'accuracy': 0.8876}
0.0031	79.0	2528	0.9912	{'f1': 0.8864696734059099}	{'accuracy': 0.8832}
0.0031	80.0	2560	1.0876	{'f1': 0.8821999256781866}	{'accuracy': 0.8732}
0.0031	81.0	2592	1.0018	{'f1': 0.887009992313605}	{'accuracy': 0.8824}
0.0031	82.0	2624	1.0475	{'f1': 0.8773055332798717}	{'accuracy': 0.8776}
0.0031	83.0	2656	1.0269	{'f1': 0.8855445544554456}	{'accuracy': 0.8844}
0.0031	84.0	2688	1.0180	{'f1': 0.8863456985003947}	{'accuracy': 0.8848}
0.0031	85.0	2720	1.2762	{'f1': 0.8522483940042828}	{'accuracy': 0.862}
0.0031	86.0	2752	1.0328	{'f1': 0.8823064770932069}	{'accuracy': 0.8808}
0.0031	87.0	2784	1.0338	{'f1': 0.8841201716738198}	{'accuracy': 0.8812}
0.0031	88.0	2816	1.0630	{'f1': 0.8797876374668184}	{'accuracy': 0.8732}
0.0031	89.0	2848	1.0467	{'f1': 0.8803180914512924}	{'accuracy': 0.8796}
0.0031	90.0	2880	1.0381	{'f1': 0.8824921135646688}	{'accuracy': 0.8808}
0.0031	91.0	2912	1.1350	{'f1': 0.8735632183908045}	{'accuracy': 0.8768}
0.0031	92.0	2944	1.2254	{'f1': 0.8589527027027027}	{'accuracy': 0.8664}
0.0031	93.0	2976	1.0737	{'f1': 0.879415347137637}	{'accuracy': 0.8812}
0.0079	94.0	3008	1.0254	{'f1': 0.8832204065364686}	{'accuracy': 0.8828}
0.0079	95.0	3040	1.0451	{'f1': 0.8829190056134724}	{'accuracy': 0.8832}
0.0079	96.0	3072	1.0376	{'f1': 0.8876058506543494}	{'accuracy': 0.8832}
0.0079	97.0	3104	1.0467	{'f1': 0.8867562380038387}	{'accuracy': 0.882}
0.0079	98.0	3136	1.0908	{'f1': 0.8808080808080808}	{'accuracy': 0.882}
0.0079	99.0	3168	1.1546	{'f1': 0.8797047970479704}	{'accuracy': 0.8696}
0.0079	100.0	3200	1.0611	{'f1': 0.8873239436619718}	{'accuracy': 0.8848}
0.0079	101.0	3232	1.1234	{'f1': 0.872815928484356}	{'accuracy': 0.8748}
0.0079	102.0	3264	1.0708	{'f1': 0.8848146671980869}	{'accuracy': 0.8844}
0.0079	103.0	3296	1.4289	{'f1': 0.8423344947735192}	{'accuracy': 0.8552}
0.0079	104.0	3328	1.0982	{'f1': 0.877079107505071}	{'accuracy': 0.8788}
0.0079	105.0	3360	1.2706	{'f1': 0.8601784955376116}	{'accuracy': 0.8684}
0.0079	106.0	3392	1.0504	{'f1': 0.8805433479824213}	{'accuracy': 0.8804}
0.0079	107.0	3424	1.0664	{'f1': 0.8819725141471302}	{'accuracy': 0.8832}
0.0079	108.0	3456	1.0435	{'f1': 0.8888888888888888}	{'accuracy': 0.8836}
0.0079	109.0	3488	1.0634	{'f1': 0.878775674587193}	{'accuracy': 0.8796}
0.0038	110.0	3520	1.0531	{'f1': 0.8879605013292824}	{'accuracy': 0.882}
0.0038	111.0	3552	1.1024	{'f1': 0.8788732394366197}	{'accuracy': 0.8796}
0.0038	112.0	3584	1.1009	{'f1': 0.8837209302325582}	{'accuracy': 0.88}
0.0038	113.0	3616	1.1039	{'f1': 0.885195206803247}	{'accuracy': 0.8812}
0.0038	114.0	3648	1.1037	{'f1': 0.8848390849166343}	{'accuracy': 0.8812}
0.0038	115.0	3680	1.3680	{'f1': 0.846483704974271}	{'accuracy': 0.8568}
0.0038	116.0	3712	1.1014	{'f1': 0.8790259230164964}	{'accuracy': 0.8768}
0.0038	117.0	3744	1.1115	{'f1': 0.8753507014028056}	{'accuracy': 0.8756}
0.0038	118.0	3776	1.1046	{'f1': 0.8777152051488336}	{'accuracy': 0.8784}
0.0038	119.0	3808	1.3210	{'f1': 0.8581109699279966}	{'accuracy': 0.866}
0.0038	120.0	3840	1.1407	{'f1': 0.877001171417415}	{'accuracy': 0.874}
0.0038	121.0	3872	1.2861	{'f1': 0.873992673992674}	{'accuracy': 0.8624}
0.0038	122.0	3904	1.4529	{'f1': 0.8476764199655766}	{'accuracy': 0.8584}
0.0038	123.0	3936	1.1866	{'f1': 0.8746968472109943}	{'accuracy': 0.876}
0.0038	124.0	3968	1.1406	{'f1': 0.8884585592563905}	{'accuracy': 0.8848}
0.0043	125.0	4000	1.2049	{'f1': 0.8772357723577234}	{'accuracy': 0.8792}
0.0043	126.0	4032	1.1289	{'f1': 0.8919129082426128}	{'accuracy': 0.8888}
0.0043	127.0	4064	1.1299	{'f1': 0.8917445482866044}	{'accuracy': 0.8888}
0.0043	128.0	4096	1.1307	{'f1': 0.8919235271166601}	{'accuracy': 0.8892}
0.0043	129.0	4128	1.1318	{'f1': 0.8897576231430805}	{'accuracy': 0.8872}
0.0043	130.0	4160	1.1481	{'f1': 0.8857142857142859}	{'accuracy': 0.8848}
0.0043	131.0	4192	1.1503	{'f1': 0.8857142857142859}	{'accuracy': 0.8848}
0.0043	132.0	4224	1.1934	{'f1': 0.8803556992724333}	{'accuracy': 0.8816}
0.0043	133.0	4256	1.1931	{'f1': 0.88}	{'accuracy': 0.8812}
0.0043	134.0	4288	1.1904	{'f1': 0.8804523424878836}	{'accuracy': 0.8816}
0.0043	135.0	4320	1.1773	{'f1': 0.8826366559485529}	{'accuracy': 0.8832}
0.0043	136.0	4352	1.1680	{'f1': 0.8852459016393444}	{'accuracy': 0.8852}
0.0043	137.0	4384	1.1401	{'f1': 0.8869701726844584}	{'accuracy': 0.8848}
0.0043	138.0	4416	1.1414	{'f1': 0.8846761453396524}	{'accuracy': 0.8832}
0.0043	139.0	4448	1.4003	{'f1': 0.8530038346825735}	{'accuracy': 0.862}
0.0043	140.0	4480	1.1447	{'f1': 0.8835182250396196}	{'accuracy': 0.8824}
0.0007	141.0	4512	1.2298	{'f1': 0.8708094848732624}	{'accuracy': 0.8736}
0.0007	142.0	4544	1.1491	{'f1': 0.8894026974951831}	{'accuracy': 0.8852}
0.0007	143.0	4576	1.1352	{'f1': 0.88984375}	{'accuracy': 0.8872}
0.0007	144.0	4608	1.2173	{'f1': 0.876523151909017}	{'accuracy': 0.8784}
0.0007	145.0	4640	1.1757	{'f1': 0.8833667334669338}	{'accuracy': 0.8836}
0.0007	146.0	4672	1.4335	{'f1': 0.84796573875803}	{'accuracy': 0.858}
0.0007	147.0	4704	1.5681	{'f1': 0.8419689119170984}	{'accuracy': 0.8536}
0.0007	148.0	4736	1.1316	{'f1': 0.8892367906066537}	{'accuracy': 0.8868}
0.0007	149.0	4768	1.1266	{'f1': 0.8906189178668742}	{'accuracy': 0.8876}
0.0007	150.0	4800	1.1379	{'f1': 0.889063729346971}	{'accuracy': 0.8872}
0.0007	151.0	4832	1.1267	{'f1': 0.8905336969224776}	{'accuracy': 0.8876}
0.0007	152.0	4864	1.1140	{'f1': 0.8882791062328498}	{'accuracy': 0.886}
0.0007	153.0	4896	1.1807	{'f1': 0.8810679611650486}	{'accuracy': 0.8824}
0.0007	154.0	4928	1.1753	{'f1': 0.8841512469831053}	{'accuracy': 0.8848}
0.0007	155.0	4960	1.3370	{'f1': 0.8694929343308396}	{'accuracy': 0.8744}
0.0007	156.0	4992	1.2215	{'f1': 0.8846153846153846}	{'accuracy': 0.8848}
0.0042	157.0	5024	1.3197	{'f1': 0.8734593262119967}	{'accuracy': 0.8768}
0.0042	158.0	5056	1.1845	{'f1': 0.8859375}	{'accuracy': 0.8832}
0.0042	159.0	5088	1.1096	{'f1': 0.8882812499999999}	{'accuracy': 0.8856}
0.0042	160.0	5120	1.2496	{'f1': 0.8753599341834636}	{'accuracy': 0.8788}
0.0042	161.0	5152	1.1503	{'f1': 0.8868742609381159}	{'accuracy': 0.8852}
0.0042	162.0	5184	1.2286	{'f1': 0.8748461222814937}	{'accuracy': 0.878}
0.0042	163.0	5216	1.1418	{'f1': 0.888015717092338}	{'accuracy': 0.886}
0.0042	164.0	5248	1.1096	{'f1': 0.8815344114328695}	{'accuracy': 0.874}
0.0042	165.0	5280	1.2758	{'f1': 0.8567828643427131}	{'accuracy': 0.8636}
0.0042	166.0	5312	1.1868	{'f1': 0.8718991459943066}	{'accuracy': 0.874}
0.0042	167.0	5344	1.1437	{'f1': 0.8828685258964143}	{'accuracy': 0.8824}
0.0042	168.0	5376	1.1945	{'f1': 0.8734793187347932}	{'accuracy': 0.8752}
0.0042	169.0	5408	1.2188	{'f1': 0.8728606356968216}	{'accuracy': 0.8752}
0.0042	170.0	5440	1.1677	{'f1': 0.8786173633440514}	{'accuracy': 0.8792}
0.0042	171.0	5472	1.1273	{'f1': 0.8863366336633663}	{'accuracy': 0.8852}
0.0056	172.0	5504	1.6021	{'f1': 0.8369042413642326}	{'accuracy': 0.8508}
0.0056	173.0	5536	1.1175	{'f1': 0.8892355694227769}	{'accuracy': 0.8864}
0.0056	174.0	5568	1.1510	{'f1': 0.8846307385229542}	{'accuracy': 0.8844}
0.0056	175.0	5600	1.1517	{'f1': 0.8850758180367119}	{'accuracy': 0.8848}
0.0056	176.0	5632	1.1507	{'f1': 0.8853503184713376}	{'accuracy': 0.8848}
0.0056	177.0	5664	1.1483	{'f1': 0.886327503974563}	{'accuracy': 0.8856}
0.0056	178.0	5696	1.1462	{'f1': 0.8873015873015873}	{'accuracy': 0.8864}
0.0056	179.0	5728	1.1448	{'f1': 0.88800949742778}	{'accuracy': 0.8868}
0.0056	180.0	5760	1.1447	{'f1': 0.8877470355731224}	{'accuracy': 0.8864}
0.0056	181.0	5792	1.1450	{'f1': 0.8890643505724438}	{'accuracy': 0.8876}
0.0056	182.0	5824	1.1452	{'f1': 0.8888012618296529}	{'accuracy': 0.8872}
0.0056	183.0	5856	1.1457	{'f1': 0.8877510831035841}	{'accuracy': 0.886}
0.0056	184.0	5888	1.1459	{'f1': 0.8867033831628639}	{'accuracy': 0.8848}
0.0056	185.0	5920	1.1458	{'f1': 0.8886283704572098}	{'accuracy': 0.886}
0.0056	186.0	5952	1.1269	{'f1': 0.8888024883359252}	{'accuracy': 0.8856}
0.0056	187.0	5984	1.3035	{'f1': 0.8672199170124482}	{'accuracy': 0.872}
0.002	188.0	6016	1.2137	{'f1': 0.877079107505071}	{'accuracy': 0.8788}
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0.0	383.0	12256	1.2260	{'f1': 0.8906311250490004}	{'accuracy': 0.8884}
0.0	384.0	12288	1.3156	{'f1': 0.8826634185952092}	{'accuracy': 0.8844}
0.0	385.0	12320	1.3269	{'f1': 0.8797390949857318}	{'accuracy': 0.882}
0.0	386.0	12352	1.3158	{'f1': 0.8826634185952092}	{'accuracy': 0.8844}
0.0	387.0	12384	1.3105	{'f1': 0.8845686512758202}	{'accuracy': 0.886}
0.0	388.0	12416	1.2943	{'f1': 0.8855761482675262}	{'accuracy': 0.8864}
0.0	389.0	12448	1.2896	{'f1': 0.8845070422535211}	{'accuracy': 0.8852}
0.0	390.0	12480	1.2891	{'f1': 0.885048231511254}	{'accuracy': 0.8856}
0.0013	391.0	12512	1.2894	{'f1': 0.885048231511254}	{'accuracy': 0.8856}
0.0013	392.0	12544	1.2898	{'f1': 0.8846926476496585}	{'accuracy': 0.8852}
0.0013	393.0	12576	1.2905	{'f1': 0.8846926476496585}	{'accuracy': 0.8852}
0.0013	394.0	12608	1.2910	{'f1': 0.8846926476496585}	{'accuracy': 0.8852}
0.0013	395.0	12640	1.2916	{'f1': 0.8846926476496585}	{'accuracy': 0.8852}
0.0013	396.0	12672	1.2921	{'f1': 0.8846926476496585}	{'accuracy': 0.8852}
0.0013	397.0	12704	1.2923	{'f1': 0.8851405622489958}	{'accuracy': 0.8856}
0.0013	398.0	12736	1.2925	{'f1': 0.8855881172219991}	{'accuracy': 0.886}
0.0013	399.0	12768	1.2929	{'f1': 0.8856799037304453}	{'accuracy': 0.886}
0.0013	400.0	12800	1.2927	{'f1': 0.8866639967961554}	{'accuracy': 0.8868}
0.0013	401.0	12832	1.2922	{'f1': 0.8859543817527011}	{'accuracy': 0.886}
0.0013	402.0	12864	1.2922	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0013	403.0	12896	1.2921	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0013	404.0	12928	1.2923	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0013	405.0	12960	1.2929	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0013	406.0	12992	1.2933	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0	407.0	13024	1.2937	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0	408.0	13056	1.2942	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0	409.0	13088	1.2946	{'f1': 0.8864908073541168}	{'accuracy': 0.8864}
0.0	410.0	13120	1.2953	{'f1': 0.8864908073541168}	{'accuracy': 0.8864}
0.0	411.0	13152	1.3014	{'f1': 0.8863090472377902}	{'accuracy': 0.8864}
0.0	412.0	13184	1.3020	{'f1': 0.8863090472377902}	{'accuracy': 0.8864}
0.0	413.0	13216	1.3022	{'f1': 0.8859543817527011}	{'accuracy': 0.886}
0.0	414.0	13248	1.3024	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	415.0	13280	1.3028	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	416.0	13312	1.3030	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	417.0	13344	1.3035	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	418.0	13376	1.3057	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	419.0	13408	1.3062	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	420.0	13440	1.3062	{'f1': 0.8864}	{'accuracy': 0.8864}
0.0	421.0	13472	1.3064	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0	422.0	13504	1.3068	{'f1': 0.8860455817672931}	{'accuracy': 0.886}
0.0	423.0	13536	1.3068	{'f1': 0.8864908073541168}	{'accuracy': 0.8864}
0.0	424.0	13568	1.3069	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	425.0	13600	1.3074	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	426.0	13632	1.3078	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	427.0	13664	1.3081	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	428.0	13696	1.3085	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	429.0	13728	1.3089	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	430.0	13760	1.3093	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	431.0	13792	1.3098	{'f1': 0.8861366360367559}	{'accuracy': 0.886}
0.0	432.0	13824	1.3103	{'f1': 0.8865814696485623}	{'accuracy': 0.8864}
0.0	433.0	13856	1.3108	{'f1': 0.8865814696485623}	{'accuracy': 0.8864}
0.0	434.0	13888	1.3111	{'f1': 0.8865814696485623}	{'accuracy': 0.8864}
0.0	435.0	13920	1.3325	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	436.0	13952	1.3348	{'f1': 0.8859330914953648}	{'accuracy': 0.8868}
0.0	437.0	13984	1.3347	{'f1': 0.8859330914953648}	{'accuracy': 0.8868}
0.0	438.0	14016	1.3350	{'f1': 0.8863819500402902}	{'accuracy': 0.8872}
0.0	439.0	14048	1.3356	{'f1': 0.8863819500402902}	{'accuracy': 0.8872}
0.0	440.0	14080	1.3356	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	441.0	14112	1.3358	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	442.0	14144	1.3361	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	443.0	14176	1.3365	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	444.0	14208	1.3371	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	445.0	14240	1.3372	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	446.0	14272	1.3370	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	447.0	14304	1.3374	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	448.0	14336	1.3377	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	449.0	14368	1.3378	{'f1': 0.8856682769726248}	{'accuracy': 0.8864}
0.0	450.0	14400	1.3389	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	451.0	14432	1.3398	{'f1': 0.8860249697946033}	{'accuracy': 0.8868}
0.0	452.0	14464	1.3398	{'f1': 0.8853118712273642}	{'accuracy': 0.886}
0.0	453.0	14496	1.4733	{'f1': 0.8717948717948717}	{'accuracy': 0.876}
0.0	454.0	14528	1.2690	{'f1': 0.8940809968847352}	{'accuracy': 0.8912}
0.0	455.0	14560	1.2784	{'f1': 0.8932562620423893}	{'accuracy': 0.8892}
0.0	456.0	14592	1.2703	{'f1': 0.8930669800235017}	{'accuracy': 0.8908}
0.0	457.0	14624	1.2847	{'f1': 0.8900316455696203}	{'accuracy': 0.8888}
0.0	458.0	14656	1.2967	{'f1': 0.8888888888888887}	{'accuracy': 0.888}
0.0	459.0	14688	1.3012	{'f1': 0.8883591577274532}	{'accuracy': 0.8876}
0.0	460.0	14720	1.3181	{'f1': 0.8892443022790882}	{'accuracy': 0.8892}
0.0	461.0	14752	1.3198	{'f1': 0.8883553421368547}	{'accuracy': 0.8884}
0.0	462.0	14784	1.3201	{'f1': 0.8883553421368547}	{'accuracy': 0.8884}
0.0	463.0	14816	1.3203	{'f1': 0.8883553421368547}	{'accuracy': 0.8884}
0.0	464.0	14848	1.3205	{'f1': 0.8883553421368547}	{'accuracy': 0.8884}
0.0	465.0	14880	1.3196	{'f1': 0.8888888888888888}	{'accuracy': 0.8888}
0.0	466.0	14912	1.3196	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0	467.0	14944	1.3198	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0	468.0	14976	1.3201	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	469.0	15008	1.3204	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	470.0	15040	1.3205	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	471.0	15072	1.3207	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	472.0	15104	1.3209	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	473.0	15136	1.3210	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	474.0	15168	1.3212	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	475.0	15200	1.3214	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	476.0	15232	1.3215	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	477.0	15264	1.3217	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	478.0	15296	1.3219	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	479.0	15328	1.3220	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	480.0	15360	1.3222	{'f1': 0.8885337594886136}	{'accuracy': 0.8884}
0.0007	481.0	15392	1.3221	{'f1': 0.8878243512974051}	{'accuracy': 0.8876}
0.0007	482.0	15424	1.3221	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0007	483.0	15456	1.3223	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0007	484.0	15488	1.3224	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	485.0	15520	1.3226	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	486.0	15552	1.3227	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	487.0	15584	1.3228	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	488.0	15616	1.3230	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	489.0	15648	1.3231	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	490.0	15680	1.3232	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	491.0	15712	1.3233	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	492.0	15744	1.3233	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	493.0	15776	1.3234	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	494.0	15808	1.3235	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	495.0	15840	1.3235	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	496.0	15872	1.3236	{'f1': 0.888268156424581}	{'accuracy': 0.888}
0.0	497.0	15904	1.3240	{'f1': 0.8878243512974051}	{'accuracy': 0.8876}
0.0	498.0	15936	1.3242	{'f1': 0.8878243512974051}	{'accuracy': 0.8876}
0.0	499.0	15968	1.3242	{'f1': 0.8878243512974051}	{'accuracy': 0.8876}
0.0	500.0	16000	1.3242	{'f1': 0.8878243512974051}	{'accuracy': 0.8876}

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_500

bert-base-uncased-test_2_500

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kwwww/
bert-base-uncased-test_2_500