finetuned-marktextepoch-n800

This model is a fine-tuned version of leokai/finetuned-marktextepoch-n600 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.8433

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: 200

Training results

Training Loss	Epoch	Step	Validation Loss
0.287	1.0	1606	2.8473
0.2913	2.0	3212	2.8147
0.2865	3.0	4818	2.8809
0.2947	4.0	6424	2.8510
0.2988	5.0	8030	2.8883
0.3109	6.0	9636	2.9016
0.309	7.0	11242	2.8869
0.301	8.0	12848	2.9201
0.303	9.0	14454	2.8902
0.3156	10.0	16060	2.8888
0.3132	11.0	17666	2.8777
0.3089	12.0	19272	2.9429
0.3146	13.0	20878	2.9131
0.3297	14.0	22484	2.8983
0.3214	15.0	24090	2.9321
0.3095	16.0	25696	2.9436
0.3171	17.0	27302	2.9163
0.308	18.0	28908	2.9545
0.3174	19.0	30514	2.9161
0.3163	20.0	32120	2.9081
0.3191	21.0	33726	2.9465
0.3254	22.0	35332	2.9404
0.3168	23.0	36938	2.9054
0.33	24.0	38544	2.9274
0.3115	25.0	40150	2.9277
0.3125	26.0	41756	2.9627
0.3246	27.0	43362	2.9583
0.3133	28.0	44968	2.9433
0.3221	29.0	46574	2.9747
0.3185	30.0	48180	2.9793
0.3123	31.0	49786	2.9170
0.3169	32.0	51392	2.9711
0.3175	33.0	52998	2.9457
0.3253	34.0	54604	2.9518
0.3163	35.0	56210	2.9218
0.3113	36.0	57816	2.9524
0.3208	37.0	59422	2.9570
0.3197	38.0	61028	2.9439
0.3213	39.0	62634	2.9416
0.3259	40.0	64240	2.9884
0.3216	41.0	65846	2.9641
0.3154	42.0	67452	2.9797
0.3258	43.0	69058	2.9813
0.3236	44.0	70664	2.9700
0.3134	45.0	72270	2.9881
0.3219	46.0	73876	2.9982
0.3243	47.0	75482	2.9702
0.3246	48.0	77088	2.9706
0.3245	49.0	78694	2.9965
0.3124	50.0	80300	2.9893
0.3172	51.0	81906	2.9859
0.3118	52.0	83512	2.9707
0.3187	53.0	85118	2.9771
0.3256	54.0	86724	2.9827
0.3222	55.0	88330	2.9776
0.3212	56.0	89936	2.9607
0.3215	57.0	91542	2.9664
0.3266	58.0	93148	2.9638
0.3209	59.0	94754	2.9842
0.333	60.0	96360	3.0053
0.3202	61.0	97966	2.9833
0.3155	62.0	99572	2.9952
0.32	63.0	101178	2.9737
0.3291	64.0	102784	2.9804
0.3259	65.0	104390	2.9767
0.32	66.0	105996	2.9610
0.3208	67.0	107602	3.0111
0.3277	68.0	109208	2.9588
0.337	69.0	110814	2.9920
0.3296	70.0	112420	2.9466
0.3197	71.0	114026	2.9619
0.323	72.0	115632	2.9733
0.3247	73.0	117238	2.9787
0.3246	74.0	118844	2.9383
0.3203	75.0	120450	3.0123
0.3272	76.0	122056	3.0284
0.3407	77.0	123662	3.0047
0.3312	78.0	125268	2.9465
0.3262	79.0	126874	2.9805
0.3221	80.0	128480	2.9713
0.3246	81.0	130086	2.9869
0.3208	82.0	131692	2.9970
0.3196	83.0	133298	2.9864
0.3311	84.0	134904	3.0080
0.3235	85.0	136510	2.9739
0.3251	86.0	138116	2.9749
0.3248	87.0	139722	2.9588
0.3342	88.0	141328	2.9509
0.3456	89.0	142934	2.9713
0.3337	90.0	144540	2.9968
0.323	91.0	146146	2.9790
0.3202	92.0	147752	2.9919
0.3308	93.0	149358	3.0100
0.3232	94.0	150964	2.9873
0.3356	95.0	152570	2.9786
0.3282	96.0	154176	2.9965
0.3404	97.0	155782	3.0198
0.3212	98.0	157388	2.9713
0.3307	99.0	158994	2.9979
0.337	100.0	160600	2.9805
0.3354	101.0	162206	2.9759
0.3252	102.0	163812	2.9810
0.3324	103.0	165418	2.9433
0.3278	104.0	167024	3.0079
0.3419	105.0	168630	2.9576
0.343	106.0	170236	2.9610
0.3294	107.0	171842	2.9147
0.3271	108.0	173448	2.9740
0.3315	109.0	175054	2.9736
0.3413	110.0	176660	2.9819
0.3344	111.0	178266	2.9783
0.3399	112.0	179872	2.9836
0.3314	113.0	181478	2.9605
0.3344	114.0	183084	2.9629
0.3346	115.0	184690	2.9535
0.3324	116.0	186296	2.9139
0.3493	117.0	187902	2.9383
0.341	118.0	189508	2.9547
0.3414	119.0	191114	2.9592
0.335	120.0	192720	2.9822
0.3423	121.0	194326	2.9498
0.3415	122.0	195932	2.9371
0.3557	123.0	197538	2.9625
0.3544	124.0	199144	2.9637
0.3528	125.0	200750	2.9881
0.3567	126.0	202356	2.9576
0.3336	127.0	203962	2.9427
0.3282	128.0	205568	2.9659
0.3605	129.0	207174	2.9555
0.3436	130.0	208780	2.9590
0.3489	131.0	210386	2.9250
0.3604	132.0	211992	2.9411
0.347	133.0	213598	2.9093
0.3623	134.0	215204	2.9324
0.3449	135.0	216810	2.9564
0.3459	136.0	218416	2.9254
0.3519	137.0	220022	2.9512
0.3499	138.0	221628	2.9411
0.3588	139.0	223234	2.8994
0.3657	140.0	224840	2.9372
0.3564	141.0	226446	2.9237
0.3445	142.0	228052	2.9380
0.359	143.0	229658	2.9547
0.3495	144.0	231264	2.9238
0.3545	145.0	232870	2.9436
0.3523	146.0	234476	2.9390
0.3785	147.0	236082	2.8861
0.356	148.0	237688	2.9239
0.3624	149.0	239294	2.8960
0.3619	150.0	240900	2.9224
0.3607	151.0	242506	2.9155
0.3585	152.0	244112	2.9144
0.3735	153.0	245718	2.8805
0.3534	154.0	247324	2.9095
0.3667	155.0	248930	2.8888
0.3705	156.0	250536	2.9049
0.3711	157.0	252142	2.8801
0.3633	158.0	253748	2.8874
0.36	159.0	255354	2.8984
0.3752	160.0	256960	2.9004
0.3717	161.0	258566	2.8577
0.3742	162.0	260172	2.8772
0.3815	163.0	261778	2.9183
0.3695	164.0	263384	2.9144
0.3809	165.0	264990	2.8968
0.3813	166.0	266596	2.8690
0.3803	167.0	268202	2.8748
0.3813	168.0	269808	2.8676
0.3782	169.0	271414	2.8473
0.3848	170.0	273020	2.8816
0.371	171.0	274626	2.8929
0.3843	172.0	276232	2.8858
0.381	173.0	277838	2.8590
0.3889	174.0	279444	2.8484
0.3814	175.0	281050	2.8634
0.3865	176.0	282656	2.8713
0.3968	177.0	284262	2.8490
0.4007	178.0	285868	2.8497
0.3805	179.0	287474	2.8435
0.3903	180.0	289080	2.8582
0.392	181.0	290686	2.8473
0.3926	182.0	292292	2.8584
0.3921	183.0	293898	2.8850
0.3958	184.0	295504	2.8532
0.3858	185.0	297110	2.8568
0.4002	186.0	298716	2.7939
0.3999	187.0	300322	2.8548
0.3932	188.0	301928	2.8598
0.4005	189.0	303534	2.8390
0.4048	190.0	305140	2.8336
0.3983	191.0	306746	2.8286
0.394	192.0	308352	2.8437
0.3989	193.0	309958	2.8594
0.3966	194.0	311564	2.8541
0.397	195.0	313170	2.8697
0.4007	196.0	314776	2.8549
0.3978	197.0	316382	2.8815
0.4005	198.0	317988	2.8565
0.4025	199.0	319594	2.8451
0.4078	200.0	321200	2.8433

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1