finetuned-marktextepoch-n600

This model is a fine-tuned version of leokai/finetuned-marktextepoch-n500 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.6814

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: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 182

Training results

Training Loss	Epoch	Step	Validation Loss
0.5332	1.0	1606	2.5256
0.5315	2.0	3212	2.4835
0.5181	3.0	4818	2.5471
0.5318	4.0	6424	2.5213
0.5398	5.0	8030	2.5408
0.5474	6.0	9636	2.5557
0.541	7.0	11242	2.5415
0.529	8.0	12848	2.5729
0.5294	9.0	14454	2.5533
0.5404	10.0	16060	2.5414
0.5359	11.0	17666	2.5316
0.5295	12.0	19272	2.5985
0.5319	13.0	20878	2.5644
0.5492	14.0	22484	2.5594
0.5403	15.0	24090	2.5830
0.526	16.0	25696	2.5999
0.5308	17.0	27302	2.5629
0.516	18.0	28908	2.6015
0.525	19.0	30514	2.5726
0.5238	20.0	32120	2.5656
0.5244	21.0	33726	2.5961
0.531	22.0	35332	2.5809
0.517	23.0	36938	2.5688
0.5334	24.0	38544	2.5828
0.505	25.0	40150	2.5861
0.5136	26.0	41756	2.6044
0.5228	27.0	43362	2.6000
0.5066	28.0	44968	2.5971
0.5183	29.0	46574	2.6240
0.5076	30.0	48180	2.6201
0.5059	31.0	49786	2.5746
0.5033	32.0	51392	2.6229
0.5041	33.0	52998	2.6086
0.5132	34.0	54604	2.6115
0.5007	35.0	56210	2.5865
0.4947	36.0	57816	2.6159
0.4997	37.0	59422	2.6072
0.4988	38.0	61028	2.5931
0.5001	39.0	62634	2.5956
0.5027	40.0	64240	2.6444
0.4969	41.0	65846	2.6194
0.4916	42.0	67452	2.6394
0.4986	43.0	69058	2.6401
0.5	44.0	70664	2.6164
0.4848	45.0	72270	2.6479
0.4926	46.0	73876	2.6519
0.492	47.0	75482	2.6359
0.4939	48.0	77088	2.6211
0.4914	49.0	78694	2.6536
0.4743	50.0	80300	2.6434
0.4787	51.0	81906	2.6396
0.4686	52.0	83512	2.6337
0.4775	53.0	85118	2.6352
0.4844	54.0	86724	2.6382
0.4802	55.0	88330	2.6473
0.4799	56.0	89936	2.6284
0.4749	57.0	91542	2.6371
0.4779	58.0	93148	2.6264
0.4727	59.0	94754	2.6506
0.4875	60.0	96360	2.6677
0.4695	61.0	97966	2.6507
0.4612	62.0	99572	2.6600
0.4658	63.0	101178	2.6442
0.4737	64.0	102784	2.6593
0.47	65.0	104390	2.6451
0.4658	66.0	105996	2.6493
0.4634	67.0	107602	2.6795
0.4713	68.0	109208	2.6392
0.4771	69.0	110814	2.6633
0.4704	70.0	112420	2.6273
0.458	71.0	114026	2.6426
0.4577	72.0	115632	2.6652
0.4585	73.0	117238	2.6609
0.4567	74.0	118844	2.6285
0.4524	75.0	120450	2.6860
0.4615	76.0	122056	2.7033
0.4725	77.0	123662	2.6877
0.4621	78.0	125268	2.6343
0.4555	79.0	126874	2.6664
0.4485	80.0	128480	2.6650
0.4508	81.0	130086	2.6777
0.4475	82.0	131692	2.6759
0.4432	83.0	133298	2.6711
0.4541	84.0	134904	2.6905
0.444	85.0	136510	2.6699
0.4428	86.0	138116	2.6737
0.4436	87.0	139722	2.6536
0.4522	88.0	141328	2.6504
0.4632	89.0	142934	2.6697
0.4514	90.0	144540	2.6854
0.4369	91.0	146146	2.6804
0.4324	92.0	147752	2.7011
0.4436	93.0	149358	2.7145
0.4317	94.0	150964	2.6880
0.4468	95.0	152570	2.6784
0.4364	96.0	154176	2.7050
0.4505	97.0	155782	2.7214
0.4273	98.0	157388	2.6843
0.4374	99.0	158994	2.7047
0.4436	100.0	160600	2.6934
0.4399	101.0	162206	2.6913
0.4273	102.0	163812	2.6949
0.4334	103.0	165418	2.6628
0.4277	104.0	167024	2.7170
0.439	105.0	168630	2.6752
0.4418	106.0	170236	2.6832
0.4278	107.0	171842	2.6386
0.4226	108.0	173448	2.6946
0.4255	109.0	175054	2.6911
0.4349	110.0	176660	2.7073
0.4259	111.0	178266	2.7048
0.4328	112.0	179872	2.7105
0.4242	113.0	181478	2.6897
0.4228	114.0	183084	2.6921
0.4227	115.0	184690	2.6833
0.4192	116.0	186296	2.6483
0.4381	117.0	187902	2.6690
0.425	118.0	189508	2.6866
0.4273	119.0	191114	2.6892
0.4201	120.0	192720	2.7128
0.4252	121.0	194326	2.6883
0.423	122.0	195932	2.6766
0.4371	123.0	197538	2.7092
0.4363	124.0	199144	2.7084
0.4315	125.0	200750	2.7321
0.4367	126.0	202356	2.7005
0.4114	127.0	203962	2.6878
0.4025	128.0	205568	2.7100
0.4376	129.0	207174	2.7073
0.4201	130.0	208780	2.7064
0.4248	131.0	210386	2.6755
0.4333	132.0	211992	2.6884
0.4178	133.0	213598	2.6688
0.433	134.0	215204	2.6911
0.4145	135.0	216810	2.7116
0.4163	136.0	218416	2.6867
0.4203	137.0	220022	2.7109
0.4164	138.0	221628	2.7031
0.4252	139.0	223234	2.6656
0.4302	140.0	224840	2.7018
0.4205	141.0	226446	2.6912
0.4055	142.0	228052	2.7107
0.4204	143.0	229658	2.7236
0.4104	144.0	231264	2.6931
0.4146	145.0	232870	2.7160
0.4113	146.0	234476	2.7116
0.4375	147.0	236082	2.6680
0.4135	148.0	237688	2.6984
0.4198	149.0	239294	2.6823
0.4154	150.0	240900	2.7031
0.4159	151.0	242506	2.7000
0.4104	152.0	244112	2.6974
0.4283	153.0	245718	2.6649
0.4046	154.0	247324	2.6989
0.4174	155.0	248930	2.6774
0.4199	156.0	250536	2.6943
0.421	157.0	252142	2.6728
0.4106	158.0	253748	2.6836
0.4081	159.0	255354	2.6946
0.4233	160.0	256960	2.6992
0.4183	161.0	258566	2.6585
0.4213	162.0	260172	2.6761
0.4259	163.0	261778	2.7186
0.4157	164.0	263384	2.7150
0.4257	165.0	264990	2.7004
0.4251	166.0	266596	2.6728
0.4228	167.0	268202	2.6831
0.4233	168.0	269808	2.6781
0.418	169.0	271414	2.6598
0.4263	170.0	273020	2.6930
0.4104	171.0	274626	2.7045
0.4213	172.0	276232	2.6979
0.419	173.0	277838	2.6726
0.4273	174.0	279444	2.6631
0.4189	175.0	281050	2.6802
0.4228	176.0	282656	2.6872
0.431	177.0	284262	2.6677
0.4363	178.0	285868	2.6710
0.4145	179.0	287474	2.6654
0.4256	180.0	289080	2.6802
0.4277	181.0	290686	2.6698
0.4249	182.0	292292	2.6814

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1