bert-base-uncased-test_64_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: 1.2845
• F1: {'f1': 0.8862179487179487}
• Accuracy: {'accuracy': 0.8864}

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	0.6877	{'f1': 0.3681048607318405}	{'accuracy': 0.5372}
No log	2.0	64	0.6758	{'f1': 0.327455919395466}	{'accuracy': 0.5728}
No log	3.0	96	0.5685	{'f1': 0.6833333333333333}	{'accuracy': 0.7112}
No log	4.0	128	0.4751	{'f1': 0.7951363301400148}	{'accuracy': 0.7776}
No log	5.0	160	0.4088	{'f1': 0.8316752884998011}	{'accuracy': 0.8308}
No log	6.0	192	0.3793	{'f1': 0.8511301636788777}	{'accuracy': 0.8472}
No log	7.0	224	0.3794	{'f1': 0.8486312399355878}	{'accuracy': 0.8496}
No log	8.0	256	0.4121	{'f1': 0.8492257538712306}	{'accuracy': 0.852}
No log	9.0	288	0.3895	{'f1': 0.8575875486381322}	{'accuracy': 0.8536}
No log	10.0	320	0.4960	{'f1': 0.8497297297297297}	{'accuracy': 0.8332}
No log	11.0	352	0.4200	{'f1': 0.8564650059311981}	{'accuracy': 0.8548}
No log	12.0	384	0.4513	{'f1': 0.8606985146527499}	{'accuracy': 0.8612}
No log	13.0	416	0.6481	{'f1': 0.8077442593426384}	{'accuracy': 0.8292}
No log	14.0	448	0.4766	{'f1': 0.8713335940555339}	{'accuracy': 0.8684}
No log	15.0	480	0.5145	{'f1': 0.8694986605434367}	{'accuracy': 0.8636}
0.2917	16.0	512	0.5238	{'f1': 0.8653624856156501}	{'accuracy': 0.8596}
0.2917	17.0	544	0.5432	{'f1': 0.8691339183517741}	{'accuracy': 0.8628}
0.2917	18.0	576	0.5481	{'f1': 0.8594905505341003}	{'accuracy': 0.8632}
0.2917	19.0	608	0.5885	{'f1': 0.8667917448405253}	{'accuracy': 0.858}
0.2917	20.0	640	0.6552	{'f1': 0.8473706712270199}	{'accuracy': 0.8572}
0.2917	21.0	672	0.5610	{'f1': 0.8746105919003115}	{'accuracy': 0.8712}
0.2917	22.0	704	0.6055	{'f1': 0.8668532586965214}	{'accuracy': 0.8668}
0.2917	23.0	736	0.7313	{'f1': 0.8440445586975149}	{'accuracy': 0.8544}
0.2917	24.0	768	0.6457	{'f1': 0.8701996927803379}	{'accuracy': 0.8648}
0.2917	25.0	800	0.7746	{'f1': 0.8427835051546393}	{'accuracy': 0.8536}
0.2917	26.0	832	0.6619	{'f1': 0.8726016884113585}	{'accuracy': 0.8672}
0.2917	27.0	864	0.7860	{'f1': 0.8489761092150171}	{'accuracy': 0.8584}
0.2917	28.0	896	0.6837	{'f1': 0.8769968051118211}	{'accuracy': 0.8768}
0.2917	29.0	928	0.7311	{'f1': 0.8708881578947368}	{'accuracy': 0.8744}
0.2917	30.0	960	0.7216	{'f1': 0.8781074578989576}	{'accuracy': 0.8784}
0.2917	31.0	992	0.7178	{'f1': 0.8824463860206514}	{'accuracy': 0.8816}
0.0335	32.0	1024	0.7308	{'f1': 0.8810916179337233}	{'accuracy': 0.878}
0.0335	33.0	1056	0.7302	{'f1': 0.8816521048451151}	{'accuracy': 0.8808}
0.0335	34.0	1088	0.7797	{'f1': 0.8712709440130773}	{'accuracy': 0.874}
0.0335	35.0	1120	0.8001	{'f1': 0.8726530612244897}	{'accuracy': 0.8752}
0.0335	36.0	1152	0.7963	{'f1': 0.8738411930673115}	{'accuracy': 0.8748}
0.0335	37.0	1184	0.8263	{'f1': 0.8765762323270919}	{'accuracy': 0.8708}
0.0335	38.0	1216	0.8035	{'f1': 0.8827258320126782}	{'accuracy': 0.8816}
0.0335	39.0	1248	0.8680	{'f1': 0.8734020618556702}	{'accuracy': 0.8772}
0.0335	40.0	1280	0.8244	{'f1': 0.8771371769383697}	{'accuracy': 0.8764}
0.0335	41.0	1312	0.8517	{'f1': 0.8748977923139819}	{'accuracy': 0.8776}
0.0335	42.0	1344	0.8202	{'f1': 0.8817034700315458}	{'accuracy': 0.88}
0.0335	43.0	1376	0.8370	{'f1': 0.8735909822866345}	{'accuracy': 0.8744}
0.0335	44.0	1408	0.8726	{'f1': 0.8699958728848535}	{'accuracy': 0.874}
0.0335	45.0	1440	0.9942	{'f1': 0.8514090520922288}	{'accuracy': 0.8608}
0.0335	46.0	1472	0.8527	{'f1': 0.8821028218013142}	{'accuracy': 0.878}
0.0067	47.0	1504	0.8594	{'f1': 0.8831168831168831}	{'accuracy': 0.8812}
0.0067	48.0	1536	0.8859	{'f1': 0.8751476959432848}	{'accuracy': 0.8732}
0.0067	49.0	1568	0.9008	{'f1': 0.8705410821643287}	{'accuracy': 0.8708}
0.0067	50.0	1600	1.0122	{'f1': 0.856060606060606}	{'accuracy': 0.8632}
0.0067	51.0	1632	1.0199	{'f1': 0.8560509554140128}	{'accuracy': 0.8644}
0.0067	52.0	1664	0.8915	{'f1': 0.8775592131674026}	{'accuracy': 0.878}
0.0067	53.0	1696	0.8944	{'f1': 0.8781664656212306}	{'accuracy': 0.8788}
0.0067	54.0	1728	0.8983	{'f1': 0.8772635814889336}	{'accuracy': 0.878}
0.0067	55.0	1760	0.8887	{'f1': 0.8765133171912833}	{'accuracy': 0.8776}
0.0067	56.0	1792	0.8827	{'f1': 0.8813151563753007}	{'accuracy': 0.8816}
0.0067	57.0	1824	0.8847	{'f1': 0.8813151563753007}	{'accuracy': 0.8816}
0.0067	58.0	1856	0.9177	{'f1': 0.873469387755102}	{'accuracy': 0.876}
0.0067	59.0	1888	0.9794	{'f1': 0.8687370600414078}	{'accuracy': 0.8732}
0.0067	60.0	1920	0.9186	{'f1': 0.8826550770446463}	{'accuracy': 0.8812}
0.0067	61.0	1952	0.9259	{'f1': 0.8793440062475596}	{'accuracy': 0.8764}
0.0067	62.0	1984	0.9303	{'f1': 0.8809338521400777}	{'accuracy': 0.8776}
0.0019	63.0	2016	0.9508	{'f1': 0.8805799313239222}	{'accuracy': 0.8748}
0.0019	64.0	2048	0.9179	{'f1': 0.8887165568049632}	{'accuracy': 0.8852}
0.0019	65.0	2080	0.9498	{'f1': 0.879219194794632}	{'accuracy': 0.8812}
0.0019	66.0	2112	0.9239	{'f1': 0.8827374086889658}	{'accuracy': 0.878}
0.0019	67.0	2144	0.9988	{'f1': 0.875558867362146}	{'accuracy': 0.8664}
0.0019	68.0	2176	1.0901	{'f1': 0.8585944115156647}	{'accuracy': 0.8664}
0.0019	69.0	2208	1.2280	{'f1': 0.8389349628982977}	{'accuracy': 0.8524}
0.0019	70.0	2240	0.9945	{'f1': 0.8655357881671494}	{'accuracy': 0.87}
0.0019	71.0	2272	1.1583	{'f1': 0.850752688172043}	{'accuracy': 0.8612}
0.0019	72.0	2304	0.9407	{'f1': 0.8785197103781175}	{'accuracy': 0.8792}
0.0019	73.0	2336	0.9479	{'f1': 0.884113584036838}	{'accuracy': 0.8792}
0.0019	74.0	2368	0.9388	{'f1': 0.8819334389857368}	{'accuracy': 0.8808}
0.0019	75.0	2400	0.9555	{'f1': 0.8748500599760095}	{'accuracy': 0.8748}
0.0019	76.0	2432	1.0311	{'f1': 0.865424430641822}	{'accuracy': 0.87}
0.0019	77.0	2464	1.0358	{'f1': 0.8749530604581299}	{'accuracy': 0.8668}
0.0019	78.0	2496	0.9811	{'f1': 0.8757062146892655}	{'accuracy': 0.8768}
0.0038	79.0	2528	0.9731	{'f1': 0.877207062600321}	{'accuracy': 0.8776}
0.0038	80.0	2560	1.1528	{'f1': 0.8681318681318682}	{'accuracy': 0.856}
0.0038	81.0	2592	0.9636	{'f1': 0.8753529649052039}	{'accuracy': 0.8764}
0.0038	82.0	2624	1.0047	{'f1': 0.8793950850661625}	{'accuracy': 0.8724}
0.0038	83.0	2656	1.0107	{'f1': 0.8737704918032787}	{'accuracy': 0.8768}
0.0038	84.0	2688	1.0927	{'f1': 0.87247335538405}	{'accuracy': 0.8612}
0.0038	85.0	2720	0.9591	{'f1': 0.8860759493670884}	{'accuracy': 0.8848}
0.0038	86.0	2752	1.1233	{'f1': 0.864955826672276}	{'accuracy': 0.8716}
0.0038	87.0	2784	0.9808	{'f1': 0.8857938718662952}	{'accuracy': 0.8852}
0.0038	88.0	2816	0.9947	{'f1': 0.88420245398773}	{'accuracy': 0.8792}
0.0038	89.0	2848	0.9814	{'f1': 0.8777327935222672}	{'accuracy': 0.8792}
0.0038	90.0	2880	0.9825	{'f1': 0.8871775125144398}	{'accuracy': 0.8828}
0.0038	91.0	2912	0.9671	{'f1': 0.886976926085256}	{'accuracy': 0.8844}
0.0038	92.0	2944	0.9660	{'f1': 0.8859166011014948}	{'accuracy': 0.884}
0.0038	93.0	2976	0.9681	{'f1': 0.8855564325177585}	{'accuracy': 0.884}
0.0054	94.0	3008	0.9698	{'f1': 0.8866222047861907}	{'accuracy': 0.8844}
0.0054	95.0	3040	0.9718	{'f1': 0.8871473354231975}	{'accuracy': 0.8848}
0.0054	96.0	3072	0.9721	{'f1': 0.8867110936887496}	{'accuracy': 0.8844}
0.0054	97.0	3104	1.0211	{'f1': 0.8853333333333333}	{'accuracy': 0.8796}
0.0054	98.0	3136	0.9764	{'f1': 0.8861756597085467}	{'accuracy': 0.8844}
0.0054	99.0	3168	0.9777	{'f1': 0.8847524752475247}	{'accuracy': 0.8836}
0.0054	100.0	3200	1.1425	{'f1': 0.8621997471554993}	{'accuracy': 0.8692}
0.0054	101.0	3232	1.2408	{'f1': 0.8535745047372953}	{'accuracy': 0.864}
0.0054	102.0	3264	0.9696	{'f1': 0.8812749003984064}	{'accuracy': 0.8808}
0.0054	103.0	3296	0.9785	{'f1': 0.8848106208512301}	{'accuracy': 0.882}
0.0054	104.0	3328	0.9845	{'f1': 0.8816521048451151}	{'accuracy': 0.8808}
0.0054	105.0	3360	0.9848	{'f1': 0.8817460317460318}	{'accuracy': 0.8808}
0.0054	106.0	3392	0.9853	{'f1': 0.8819334389857368}	{'accuracy': 0.8808}
0.0054	107.0	3424	0.9945	{'f1': 0.8828468612554978}	{'accuracy': 0.8828}
0.0054	108.0	3456	0.9833	{'f1': 0.8891500195848022}	{'accuracy': 0.8868}
0.0054	109.0	3488	0.9872	{'f1': 0.8904428904428903}	{'accuracy': 0.8872}
0.004	110.0	3520	1.0961	{'f1': 0.8714168714168714}	{'accuracy': 0.8744}
0.004	111.0	3552	1.0768	{'f1': 0.8778073848496383}	{'accuracy': 0.8716}
0.004	112.0	3584	1.0828	{'f1': 0.8754234098607452}	{'accuracy': 0.8676}
0.004	113.0	3616	0.9793	{'f1': 0.8854368932038834}	{'accuracy': 0.882}
0.004	114.0	3648	1.3126	{'f1': 0.8445798868088812}	{'accuracy': 0.8572}
0.004	115.0	3680	1.0056	{'f1': 0.8866615265998459}	{'accuracy': 0.8824}
0.004	116.0	3712	0.9960	{'f1': 0.887229862475442}	{'accuracy': 0.8852}
0.004	117.0	3744	1.0465	{'f1': 0.885681731864793}	{'accuracy': 0.8796}
0.004	118.0	3776	1.1502	{'f1': 0.8644781144781145}	{'accuracy': 0.8712}
0.004	119.0	3808	1.0003	{'f1': 0.8858846918489065}	{'accuracy': 0.8852}
0.004	120.0	3840	1.0303	{'f1': 0.876979293544458}	{'accuracy': 0.8788}
0.004	121.0	3872	1.0281	{'f1': 0.8781869688385269}	{'accuracy': 0.8796}
0.004	122.0	3904	1.0240	{'f1': 0.88}	{'accuracy': 0.8812}
0.004	123.0	3936	1.0234	{'f1': 0.8804523424878836}	{'accuracy': 0.8816}
0.004	124.0	3968	1.0203	{'f1': 0.8815471394037065}	{'accuracy': 0.8824}
0.007	125.0	4000	1.0133	{'f1': 0.882803060813532}	{'accuracy': 0.8836}
0.007	126.0	4032	1.0361	{'f1': 0.8794788273615635}	{'accuracy': 0.8816}
0.007	127.0	4064	1.0037	{'f1': 0.8832933653077539}	{'accuracy': 0.8832}
0.007	128.0	4096	1.0028	{'f1': 0.8833865814696485}	{'accuracy': 0.8832}
0.007	129.0	4128	1.0397	{'f1': 0.8788617886178862}	{'accuracy': 0.8808}
0.007	130.0	4160	1.0992	{'f1': 0.8769855929072775}	{'accuracy': 0.8668}
0.007	131.0	4192	0.9497	{'f1': 0.8808578236695789}	{'accuracy': 0.88}
0.007	132.0	4224	0.9535	{'f1': 0.8804780876494025}	{'accuracy': 0.88}
0.007	133.0	4256	1.0237	{'f1': 0.8838612368024134}	{'accuracy': 0.8768}
0.007	134.0	4288	1.2089	{'f1': 0.8522188711762171}	{'accuracy': 0.8628}
0.007	135.0	4320	0.9913	{'f1': 0.8774767488879902}	{'accuracy': 0.8788}
0.007	136.0	4352	0.9646	{'f1': 0.8876625936145053}	{'accuracy': 0.886}
0.007	137.0	4384	0.9656	{'f1': 0.8866141732283465}	{'accuracy': 0.8848}
0.007	138.0	4416	0.9670	{'f1': 0.8869633714060654}	{'accuracy': 0.8852}
0.007	139.0	4448	0.9681	{'f1': 0.8869633714060654}	{'accuracy': 0.8852}
0.007	140.0	4480	0.9745	{'f1': 0.8869496231654106}	{'accuracy': 0.886}
0.0028	141.0	4512	0.9768	{'f1': 0.8851807707588399}	{'accuracy': 0.8844}
0.0028	142.0	4544	0.9775	{'f1': 0.8860658991663358}	{'accuracy': 0.8852}
0.0028	143.0	4576	0.9785	{'f1': 0.8869496231654106}	{'accuracy': 0.886}
0.0028	144.0	4608	0.9795	{'f1': 0.8869496231654106}	{'accuracy': 0.886}
0.0028	145.0	4640	0.9802	{'f1': 0.8869496231654106}	{'accuracy': 0.886}
0.0028	146.0	4672	0.9811	{'f1': 0.8865979381443299}	{'accuracy': 0.8856}
0.0028	147.0	4704	0.9823	{'f1': 0.8865979381443299}	{'accuracy': 0.8856}
0.0028	148.0	4736	0.9842	{'f1': 0.8865079365079365}	{'accuracy': 0.8856}
0.0028	149.0	4768	0.9873	{'f1': 0.8851807707588399}	{'accuracy': 0.8844}
0.0028	150.0	4800	0.9911	{'f1': 0.8836653386454183}	{'accuracy': 0.8832}
0.0028	151.0	4832	0.9916	{'f1': 0.8829617834394905}	{'accuracy': 0.8824}
0.0028	152.0	4864	0.9920	{'f1': 0.8829617834394905}	{'accuracy': 0.8824}
0.0028	153.0	4896	0.9926	{'f1': 0.8842942345924454}	{'accuracy': 0.8836}
0.0028	154.0	4928	0.9929	{'f1': 0.8851807707588399}	{'accuracy': 0.8844}
0.0028	155.0	4960	0.9933	{'f1': 0.8851807707588399}	{'accuracy': 0.8844}
0.0028	156.0	4992	1.3663	{'f1': 0.8461873638344227}	{'accuracy': 0.8588}
0.0	157.0	5024	1.0619	{'f1': 0.879219194794632}	{'accuracy': 0.8812}
0.0	158.0	5056	1.0303	{'f1': 0.888803680981595}	{'accuracy': 0.884}
0.0	159.0	5088	1.1667	{'f1': 0.8680994521702485}	{'accuracy': 0.8748}
0.0	160.0	5120	1.0939	{'f1': 0.874074074074074}	{'accuracy': 0.8776}
0.0	161.0	5152	1.0803	{'f1': 0.8746427113107391}	{'accuracy': 0.8772}
0.0	162.0	5184	1.0660	{'f1': 0.876979293544458}	{'accuracy': 0.8788}
0.0	163.0	5216	1.0580	{'f1': 0.8794498381877023}	{'accuracy': 0.8808}
0.0	164.0	5248	1.0544	{'f1': 0.8808578236695789}	{'accuracy': 0.88}
0.0	165.0	5280	1.0617	{'f1': 0.8809523809523809}	{'accuracy': 0.88}
0.0	166.0	5312	1.0701	{'f1': 0.8790419161676648}	{'accuracy': 0.8788}
0.0	167.0	5344	1.0888	{'f1': 0.8773204196933011}	{'accuracy': 0.8784}
0.0	168.0	5376	1.1879	{'f1': 0.8666107382550337}	{'accuracy': 0.8728}
0.0	169.0	5408	1.1651	{'f1': 0.8685524126455907}	{'accuracy': 0.8736}
0.0	170.0	5440	1.1967	{'f1': 0.8771407297096053}	{'accuracy': 0.868}
0.0	171.0	5472	1.1247	{'f1': 0.8737623762376238}	{'accuracy': 0.8776}
0.0018	172.0	5504	1.0690	{'f1': 0.8882875918051798}	{'accuracy': 0.8844}
0.0018	173.0	5536	1.1525	{'f1': 0.8836158192090395}	{'accuracy': 0.8764}
0.0018	174.0	5568	1.1367	{'f1': 0.8857791225416036}	{'accuracy': 0.8792}
0.0018	175.0	5600	1.1446	{'f1': 0.8738105088953249}	{'accuracy': 0.878}
0.0018	176.0	5632	1.0122	{'f1': 0.8882812499999999}	{'accuracy': 0.8856}
0.0018	177.0	5664	1.0805	{'f1': 0.874384236453202}	{'accuracy': 0.8776}
0.0018	178.0	5696	1.0170	{'f1': 0.888631090487239}	{'accuracy': 0.8848}
0.0018	179.0	5728	1.0286	{'f1': 0.8874801901743266}	{'accuracy': 0.8864}
0.0018	180.0	5760	1.0230	{'f1': 0.8895027624309392}	{'accuracy': 0.888}
0.0018	181.0	5792	1.0283	{'f1': 0.8887128712871286}	{'accuracy': 0.8876}
0.0018	182.0	5824	1.0507	{'f1': 0.8824476650563606}	{'accuracy': 0.8832}
0.0018	183.0	5856	1.0512	{'f1': 0.8824476650563606}	{'accuracy': 0.8832}
0.0018	184.0	5888	1.0517	{'f1': 0.8825422365245374}	{'accuracy': 0.8832}
0.0018	185.0	5920	1.0522	{'f1': 0.8825422365245374}	{'accuracy': 0.8832}
0.0018	186.0	5952	1.0522	{'f1': 0.8826366559485529}	{'accuracy': 0.8832}
0.0018	187.0	5984	1.0521	{'f1': 0.8828250401284109}	{'accuracy': 0.8832}
0.0078	188.0	6016	1.0524	{'f1': 0.8821170809943865}	{'accuracy': 0.8824}
0.0078	189.0	6048	1.1665	{'f1': 0.8748430305567183}	{'accuracy': 0.8804}
0.0078	190.0	6080	1.0072	{'f1': 0.8893234258897145}	{'accuracy': 0.8868}
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0.0	385.0	12320	1.2280	{'f1': 0.8845385537355175}	{'accuracy': 0.8844}
0.0	386.0	12352	1.2274	{'f1': 0.8845385537355175}	{'accuracy': 0.8844}
0.0	387.0	12384	1.2268	{'f1': 0.8846307385229542}	{'accuracy': 0.8844}
0.0	388.0	12416	1.2270	{'f1': 0.8850758180367119}	{'accuracy': 0.8848}
0.0	389.0	12448	1.2541	{'f1': 0.8848631239935587}	{'accuracy': 0.8856}
0.0	390.0	12480	1.2600	{'f1': 0.8829701372074253}	{'accuracy': 0.884}
0.0	391.0	12512	1.2595	{'f1': 0.8830645161290323}	{'accuracy': 0.884}
0.0	392.0	12544	1.2595	{'f1': 0.8830645161290323}	{'accuracy': 0.884}
0.0	393.0	12576	1.2595	{'f1': 0.8830645161290323}	{'accuracy': 0.884}
0.0	394.0	12608	1.2595	{'f1': 0.8835147118097542}	{'accuracy': 0.8844}
0.0	395.0	12640	1.2595	{'f1': 0.8839645447219985}	{'accuracy': 0.8848}
0.0	396.0	12672	1.2592	{'f1': 0.8839645447219985}	{'accuracy': 0.8848}
0.0	397.0	12704	1.2579	{'f1': 0.8848631239935587}	{'accuracy': 0.8856}
0.0	398.0	12736	1.2578	{'f1': 0.8845070422535211}	{'accuracy': 0.8852}
0.0	399.0	12768	1.2576	{'f1': 0.8845070422535211}	{'accuracy': 0.8852}
0.0	400.0	12800	1.2458	{'f1': 0.8844301765650081}	{'accuracy': 0.8848}
0.0	401.0	12832	1.2442	{'f1': 0.8838141025641026}	{'accuracy': 0.884}
0.0	402.0	12864	1.2439	{'f1': 0.8847077662129704}	{'accuracy': 0.8848}
0.0	403.0	12896	1.2441	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	404.0	12928	1.2443	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	405.0	12960	1.2468	{'f1': 0.8838141025641026}	{'accuracy': 0.884}
0.0	406.0	12992	1.2472	{'f1': 0.8838141025641026}	{'accuracy': 0.884}
0.0	407.0	13024	1.2473	{'f1': 0.8842611133360032}	{'accuracy': 0.8844}
0.0	408.0	13056	1.2472	{'f1': 0.8842611133360032}	{'accuracy': 0.8844}
0.0	409.0	13088	1.2467	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	410.0	13120	1.2465	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	411.0	13152	1.2467	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	412.0	13184	1.2467	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	413.0	13216	1.2468	{'f1': 0.8851540616246498}	{'accuracy': 0.8852}
0.0	414.0	13248	1.2450	{'f1': 0.8848920863309353}	{'accuracy': 0.8848}
0.0	415.0	13280	1.2449	{'f1': 0.8848920863309353}	{'accuracy': 0.8848}
0.0	416.0	13312	1.2451	{'f1': 0.8848920863309353}	{'accuracy': 0.8848}
0.0	417.0	13344	1.2451	{'f1': 0.8853375948861366}	{'accuracy': 0.8852}
0.0	418.0	13376	1.2456	{'f1': 0.8853375948861366}	{'accuracy': 0.8852}
0.0	419.0	13408	1.2460	{'f1': 0.8853375948861366}	{'accuracy': 0.8852}
0.0	420.0	13440	1.2462	{'f1': 0.8849840255591055}	{'accuracy': 0.8848}
0.0	421.0	13472	1.2464	{'f1': 0.8849840255591055}	{'accuracy': 0.8848}
0.0	422.0	13504	1.2457	{'f1': 0.8859649122807017}	{'accuracy': 0.8856}
0.0	423.0	13536	1.2469	{'f1': 0.8850758180367119}	{'accuracy': 0.8848}
0.0	424.0	13568	1.2473	{'f1': 0.8850758180367119}	{'accuracy': 0.8848}
0.0	425.0	13600	1.2476	{'f1': 0.8850758180367119}	{'accuracy': 0.8848}
0.0	426.0	13632	1.2477	{'f1': 0.8855205424810532}	{'accuracy': 0.8852}
0.0	427.0	13664	1.2479	{'f1': 0.8855205424810532}	{'accuracy': 0.8852}
0.0	428.0	13696	1.2480	{'f1': 0.8859649122807017}	{'accuracy': 0.8856}
0.0	429.0	13728	1.2935	{'f1': 0.8833063209076175}	{'accuracy': 0.8848}
0.0	430.0	13760	1.3489	{'f1': 0.8799672265464974}	{'accuracy': 0.8828}
0.0	431.0	13792	1.3497	{'f1': 0.8799672265464974}	{'accuracy': 0.8828}
0.0	432.0	13824	1.2992	{'f1': 0.8833063209076175}	{'accuracy': 0.8848}
0.0	433.0	13856	1.2547	{'f1': 0.8846459824980112}	{'accuracy': 0.884}
0.0	434.0	13888	1.2543	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	435.0	13920	1.2547	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	436.0	13952	1.2550	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	437.0	13984	1.2554	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	438.0	14016	1.2556	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	439.0	14048	1.2559	{'f1': 0.8859753675009933}	{'accuracy': 0.8852}
0.0	440.0	14080	1.2562	{'f1': 0.8856235107227959}	{'accuracy': 0.8848}
0.0	441.0	14112	1.2564	{'f1': 0.8856235107227959}	{'accuracy': 0.8848}
0.0	442.0	14144	1.2588	{'f1': 0.8849064117881322}	{'accuracy': 0.8844}
0.0	443.0	14176	1.2605	{'f1': 0.8852589641434263}	{'accuracy': 0.8848}
0.0	444.0	14208	1.2608	{'f1': 0.8852589641434263}	{'accuracy': 0.8848}
0.0	445.0	14240	1.2610	{'f1': 0.8852589641434263}	{'accuracy': 0.8848}
0.0	446.0	14272	1.5715	{'f1': 0.8583581454700128}	{'accuracy': 0.8668}
0.0	447.0	14304	1.3320	{'f1': 0.8823768823768823}	{'accuracy': 0.8844}
0.0	448.0	14336	1.3084	{'f1': 0.8824959481361425}	{'accuracy': 0.884}
0.0	449.0	14368	1.3075	{'f1': 0.8834008097165993}	{'accuracy': 0.8848}
0.0	450.0	14400	1.3071	{'f1': 0.8834008097165993}	{'accuracy': 0.8848}
0.0	451.0	14432	1.3054	{'f1': 0.8847553578649412}	{'accuracy': 0.886}
0.0	452.0	14464	1.3053	{'f1': 0.8847553578649412}	{'accuracy': 0.886}
0.0	453.0	14496	1.3053	{'f1': 0.8847553578649412}	{'accuracy': 0.886}
0.0002	454.0	14528	1.3052	{'f1': 0.8847553578649412}	{'accuracy': 0.886}
0.0002	455.0	14560	1.3009	{'f1': 0.8841340331045618}	{'accuracy': 0.8852}
0.0002	456.0	14592	1.3006	{'f1': 0.8841340331045618}	{'accuracy': 0.8852}
0.0002	457.0	14624	1.2918	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	458.0	14656	1.2913	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	459.0	14688	1.2914	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	460.0	14720	1.2914	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	461.0	14752	1.2914	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	462.0	14784	1.2915	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	463.0	14816	1.2915	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	464.0	14848	1.2913	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	465.0	14880	1.2913	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	466.0	14912	1.2913	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	467.0	14944	1.2913	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0002	468.0	14976	1.2912	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0	469.0	15008	1.2912	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0	470.0	15040	1.2911	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0	471.0	15072	1.2911	{'f1': 0.8852194925493353}	{'accuracy': 0.886}
0.0	472.0	15104	1.2911	{'f1': 0.8856682769726248}	{'accuracy': 0.8864}
0.0	473.0	15136	1.2912	{'f1': 0.8856682769726248}	{'accuracy': 0.8864}
0.0	474.0	15168	1.2912	{'f1': 0.8856682769726248}	{'accuracy': 0.8864}
0.0	475.0	15200	1.2912	{'f1': 0.8856682769726248}	{'accuracy': 0.8864}
0.0	476.0	15232	1.2893	{'f1': 0.8853118712273642}	{'accuracy': 0.886}
0.0	477.0	15264	1.2879	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	478.0	15296	1.2879	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	479.0	15328	1.2879	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	480.0	15360	1.2879	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	481.0	15392	1.2880	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	482.0	15424	1.2881	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	483.0	15456	1.2881	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	484.0	15488	1.2882	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	485.0	15520	1.2882	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	486.0	15552	1.2883	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	487.0	15584	1.2883	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	488.0	15616	1.2882	{'f1': 0.8858520900321544}	{'accuracy': 0.8864}
0.0	489.0	15648	1.2846	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	490.0	15680	1.2844	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	491.0	15712	1.2845	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	492.0	15744	1.2845	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	493.0	15776	1.2843	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	494.0	15808	1.2844	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	495.0	15840	1.2844	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	496.0	15872	1.2844	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	497.0	15904	1.2844	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	498.0	15936	1.2845	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	499.0	15968	1.2845	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}
0.0	500.0	16000	1.2845	{'f1': 0.8862179487179487}	{'accuracy': 0.8864}

Framework versions

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