bert_baseline_prompt_adherence_task4_fold1

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

• Loss: 0.3376
• Qwk: 0.7136
• Mse: 0.3347

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: 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse
No log	0.0299	2	1.2352	0.0	1.2327
No log	0.0597	4	0.9657	0.0	0.9636
No log	0.0896	6	0.8614	0.2376	0.8596
No log	0.1194	8	0.8177	0.3472	0.8159
No log	0.1493	10	0.7773	0.3551	0.7756
No log	0.1791	12	0.7212	0.3577	0.7196
No log	0.2090	14	0.7108	0.3493	0.7094
No log	0.2388	16	0.6580	0.3495	0.6568
No log	0.2687	18	0.6153	0.3520	0.6140
No log	0.2985	20	0.5757	0.3622	0.5740
No log	0.3284	22	0.5514	0.3998	0.5492
No log	0.3582	24	0.5541	0.3725	0.5516
No log	0.3881	26	0.5877	0.3478	0.5850
No log	0.4179	28	0.5115	0.3637	0.5088
No log	0.4478	30	0.4795	0.3784	0.4767
No log	0.4776	32	0.4555	0.4686	0.4527
No log	0.5075	34	0.4509	0.6119	0.4480
No log	0.5373	36	0.4494	0.5913	0.4465
No log	0.5672	38	0.4459	0.5273	0.4431
No log	0.5970	40	0.4446	0.4820	0.4421
No log	0.6269	42	0.4337	0.5212	0.4310
No log	0.6567	44	0.4555	0.4669	0.4523
No log	0.6866	46	0.5375	0.3796	0.5338
No log	0.7164	48	0.5162	0.4192	0.5125
No log	0.7463	50	0.4909	0.4316	0.4873
No log	0.7761	52	0.4538	0.4563	0.4505
No log	0.8060	54	0.3986	0.6088	0.3957
No log	0.8358	56	0.3970	0.6362	0.3945
No log	0.8657	58	0.4051	0.6160	0.4029
No log	0.8955	60	0.4402	0.6915	0.4385
No log	0.9254	62	0.4589	0.7131	0.4574
No log	0.9552	64	0.4155	0.6957	0.4136
No log	0.9851	66	0.3925	0.6686	0.3902
No log	1.0149	68	0.3803	0.6230	0.3779
No log	1.0448	70	0.3849	0.5866	0.3823
No log	1.0746	72	0.3933	0.5621	0.3907
No log	1.1045	74	0.3767	0.6134	0.3742
No log	1.1343	76	0.3974	0.6952	0.3954
No log	1.1642	78	0.4197	0.7059	0.4180
No log	1.1940	80	0.4126	0.7019	0.4106
No log	1.2239	82	0.3825	0.6743	0.3801
No log	1.2537	84	0.3870	0.5898	0.3841
No log	1.2836	86	0.4077	0.5308	0.4046
No log	1.3134	88	0.3785	0.6068	0.3754
No log	1.3433	90	0.3659	0.6831	0.3634
No log	1.3731	92	0.4110	0.7143	0.4094
No log	1.4030	94	0.4019	0.7165	0.4003
No log	1.4328	96	0.3639	0.6585	0.3616
No log	1.4627	98	0.3611	0.5696	0.3580
No log	1.4925	100	0.4004	0.4979	0.3969
No log	1.5224	102	0.4052	0.4842	0.4016
No log	1.5522	104	0.3603	0.5786	0.3571
No log	1.5821	106	0.3606	0.7021	0.3581
No log	1.6119	108	0.3934	0.7166	0.3912
No log	1.6418	110	0.3772	0.7016	0.3746
No log	1.6716	112	0.3629	0.6278	0.3595
No log	1.7015	114	0.4135	0.4969	0.4097
No log	1.7313	116	0.4136	0.5040	0.4096
No log	1.7612	118	0.3709	0.6049	0.3670
No log	1.7910	120	0.3694	0.6999	0.3659
No log	1.8209	122	0.3852	0.7076	0.3820
No log	1.8507	124	0.4129	0.7210	0.4100
No log	1.8806	126	0.4104	0.7182	0.4071
No log	1.9104	128	0.3831	0.7150	0.3795
No log	1.9403	130	0.3645	0.7005	0.3605
No log	1.9701	132	0.3586	0.6678	0.3545
No log	2.0	134	0.3508	0.6547	0.3467
No log	2.0299	136	0.3474	0.6867	0.3438
No log	2.0597	138	0.3686	0.7233	0.3658
No log	2.0896	140	0.3793	0.7348	0.3771
No log	2.1194	142	0.3694	0.7303	0.3672
No log	2.1493	144	0.3552	0.7217	0.3529
No log	2.1791	146	0.3436	0.6527	0.3411
No log	2.2090	148	0.3436	0.6231	0.3408
No log	2.2388	150	0.3448	0.6403	0.3422
No log	2.2687	152	0.3592	0.6930	0.3570
No log	2.2985	154	0.3549	0.6682	0.3526
No log	2.3284	156	0.3531	0.6565	0.3509
No log	2.3582	158	0.3577	0.5698	0.3554
No log	2.3881	160	0.3661	0.5155	0.3635
No log	2.4179	162	0.3571	0.5463	0.3543
No log	2.4478	164	0.3388	0.6163	0.3358
No log	2.4776	166	0.3428	0.6843	0.3400
No log	2.5075	168	0.3889	0.7374	0.3864
No log	2.5373	170	0.4104	0.7415	0.4078
No log	2.5672	172	0.3920	0.7343	0.3890
No log	2.5970	174	0.3589	0.7071	0.3553
No log	2.6269	176	0.3418	0.6973	0.3379
No log	2.6567	178	0.3379	0.6729	0.3338
No log	2.6866	180	0.3366	0.6531	0.3325
No log	2.7164	182	0.3335	0.6938	0.3297
No log	2.7463	184	0.3515	0.7081	0.3485
No log	2.7761	186	0.3844	0.7387	0.3820
No log	2.8060	188	0.3874	0.7441	0.3852
No log	2.8358	190	0.3495	0.7224	0.3468
No log	2.8657	192	0.3242	0.6783	0.3208
No log	2.8955	194	0.3322	0.6336	0.3286
No log	2.9254	196	0.3354	0.6577	0.3316
No log	2.9552	198	0.3368	0.6913	0.3332
No log	2.9851	200	0.3676	0.7275	0.3647
No log	3.0149	202	0.4124	0.7294	0.4101
No log	3.0448	204	0.4165	0.7388	0.4142
No log	3.0746	206	0.3928	0.7287	0.3903
No log	3.1045	208	0.3558	0.7149	0.3527
No log	3.1343	210	0.3375	0.6970	0.3341
No log	3.1642	212	0.3306	0.6716	0.3272
No log	3.1940	214	0.3266	0.6814	0.3234
No log	3.2239	216	0.3316	0.7124	0.3287
No log	3.2537	218	0.3329	0.7182	0.3301
No log	3.2836	220	0.3393	0.7243	0.3367
No log	3.3134	222	0.3680	0.7327	0.3658
No log	3.3433	224	0.3882	0.7483	0.3862
No log	3.3731	226	0.3790	0.7355	0.3770
No log	3.4030	228	0.3458	0.7312	0.3432
No log	3.4328	230	0.3360	0.7308	0.3333
No log	3.4627	232	0.3497	0.7328	0.3473
No log	3.4925	234	0.3563	0.7328	0.3539
No log	3.5224	236	0.3456	0.7253	0.3428
No log	3.5522	238	0.3342	0.7016	0.3307
No log	3.5821	240	0.3457	0.6459	0.3418
No log	3.6119	242	0.3569	0.6425	0.3529
No log	3.6418	244	0.3518	0.6463	0.3478
No log	3.6716	246	0.3437	0.6589	0.3400
No log	3.7015	248	0.3393	0.6991	0.3360
No log	3.7313	250	0.3454	0.7089	0.3423
No log	3.7612	252	0.3429	0.7128	0.3398
No log	3.7910	254	0.3331	0.7014	0.3299
No log	3.8209	256	0.3270	0.6810	0.3237
No log	3.8507	258	0.3264	0.6792	0.3231
No log	3.8806	260	0.3264	0.6870	0.3233
No log	3.9104	262	0.3303	0.6999	0.3274
No log	3.9403	264	0.3387	0.7057	0.3359
No log	3.9701	266	0.3403	0.7095	0.3376
No log	4.0	268	0.3329	0.7027	0.3301
No log	4.0299	270	0.3325	0.6986	0.3296
No log	4.0597	272	0.3292	0.6847	0.3262
No log	4.0896	274	0.3278	0.6556	0.3247
No log	4.1194	276	0.3274	0.6594	0.3243
No log	4.1493	278	0.3292	0.7012	0.3262
No log	4.1791	280	0.3352	0.7056	0.3323
No log	4.2090	282	0.3480	0.7221	0.3453
No log	4.2388	284	0.3556	0.7342	0.3530
No log	4.2687	286	0.3560	0.7362	0.3534
No log	4.2985	288	0.3612	0.7380	0.3586
No log	4.3284	290	0.3578	0.7358	0.3552
No log	4.3582	292	0.3490	0.7266	0.3463
No log	4.3881	294	0.3390	0.7071	0.3360
No log	4.4179	296	0.3362	0.6972	0.3331
No log	4.4478	298	0.3333	0.7030	0.3301
No log	4.4776	300	0.3326	0.6942	0.3293
No log	4.5075	302	0.3322	0.6888	0.3288
No log	4.5373	304	0.3335	0.6910	0.3301
No log	4.5672	306	0.3350	0.6892	0.3316
No log	4.5970	308	0.3375	0.7055	0.3343
No log	4.6269	310	0.3425	0.7088	0.3395
No log	4.6567	312	0.3475	0.7169	0.3447
No log	4.6866	314	0.3502	0.7266	0.3474
No log	4.7164	316	0.3509	0.7285	0.3482
No log	4.7463	318	0.3496	0.7266	0.3469
No log	4.7761	320	0.3480	0.7266	0.3452
No log	4.8060	322	0.3461	0.7179	0.3433
No log	4.8358	324	0.3440	0.7159	0.3412
No log	4.8657	326	0.3411	0.7152	0.3383
No log	4.8955	328	0.3397	0.7165	0.3368
No log	4.9254	330	0.3389	0.7177	0.3360
No log	4.9552	332	0.3382	0.7134	0.3353
No log	4.9851	334	0.3376	0.7136	0.3347

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1