bert_baseline_prompt_adherence_task5_fold0

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: 0.4663
• Qwk: 0.7013
• Mse: 0.4666

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	0.0294	2	2.9059	0.0	2.9073
No log	0.0588	4	2.2091	0.0034	2.2102
No log	0.0882	6	1.7028	0.0892	1.7039
No log	0.1176	8	1.3629	0.0053	1.3638
No log	0.1471	10	1.0969	0.0053	1.0977
No log	0.1765	12	0.9788	0.0053	0.9796
No log	0.2059	14	0.9151	0.3583	0.9157
No log	0.2353	16	0.9111	0.2899	0.9116
No log	0.2647	18	0.8799	0.2973	0.8804
No log	0.2941	20	0.7980	0.3692	0.7985
No log	0.3235	22	0.7763	0.3377	0.7768
No log	0.3529	24	0.7417	0.3823	0.7420
No log	0.3824	26	0.7347	0.3495	0.7350
No log	0.4118	28	0.7239	0.3732	0.7241
No log	0.4412	30	0.6977	0.3795	0.6977
No log	0.4706	32	0.6337	0.3960	0.6338
No log	0.5	34	0.6358	0.4027	0.6358
No log	0.5294	36	0.6383	0.4043	0.6382
No log	0.5588	38	0.6432	0.4001	0.6432
No log	0.5882	40	0.6993	0.3993	0.6992
No log	0.6176	42	0.8092	0.3736	0.8091
No log	0.6471	44	0.7260	0.3993	0.7259
No log	0.6765	46	0.6148	0.4697	0.6147
No log	0.7059	48	0.5695	0.4985	0.5694
No log	0.7353	50	0.5712	0.4985	0.5710
No log	0.7647	52	0.5515	0.5271	0.5511
No log	0.7941	54	0.6129	0.6509	0.6124
No log	0.8235	56	0.6777	0.6631	0.6772
No log	0.8529	58	0.5835	0.6175	0.5830
No log	0.8824	60	0.5750	0.6613	0.5745
No log	0.9118	62	0.5654	0.6853	0.5649
No log	0.9412	64	0.6357	0.6779	0.6352
No log	0.9706	66	0.6253	0.7047	0.6250
No log	1.0	68	0.5420	0.6737	0.5418
No log	1.0294	70	0.5295	0.6275	0.5294
No log	1.0588	72	0.5528	0.6511	0.5526
No log	1.0882	74	0.5959	0.6831	0.5957
No log	1.1176	76	0.6313	0.7036	0.6310
No log	1.1471	78	0.6964	0.6991	0.6961
No log	1.1765	80	0.6768	0.6991	0.6766
No log	1.2059	82	0.5192	0.6455	0.5189
No log	1.2353	84	0.5170	0.5691	0.5166
No log	1.2647	86	0.5260	0.5899	0.5257
No log	1.2941	88	0.5322	0.6721	0.5317
No log	1.3235	90	0.5795	0.6786	0.5790
No log	1.3529	92	0.5068	0.6386	0.5064
No log	1.3824	94	0.4826	0.6180	0.4825
No log	1.4118	96	0.5348	0.6957	0.5346
No log	1.4412	98	0.6302	0.7008	0.6300
No log	1.4706	100	0.6063	0.7003	0.6061
No log	1.5	102	0.4943	0.6942	0.4941
No log	1.5294	104	0.4529	0.6235	0.4529
No log	1.5588	106	0.4550	0.6311	0.4550
No log	1.5882	108	0.4559	0.6638	0.4559
No log	1.6176	110	0.4935	0.6877	0.4934
No log	1.6471	112	0.4913	0.6908	0.4913
No log	1.6765	114	0.4843	0.6796	0.4843
No log	1.7059	116	0.4715	0.6746	0.4715
No log	1.7353	118	0.4637	0.6543	0.4637
No log	1.7647	120	0.4684	0.6611	0.4685
No log	1.7941	122	0.5161	0.6979	0.5161
No log	1.8235	124	0.5005	0.7001	0.5005
No log	1.8529	126	0.4552	0.6699	0.4553
No log	1.8824	128	0.4527	0.6718	0.4528
No log	1.9118	130	0.4437	0.6668	0.4439
No log	1.9412	132	0.4537	0.6729	0.4538
No log	1.9706	134	0.4450	0.6669	0.4451
No log	2.0	136	0.4353	0.6379	0.4355
No log	2.0294	138	0.4390	0.5972	0.4392
No log	2.0588	140	0.4326	0.6712	0.4327
No log	2.0882	142	0.5061	0.7136	0.5061
No log	2.1176	144	0.5603	0.7234	0.5603
No log	2.1471	146	0.5268	0.7055	0.5269
No log	2.1765	148	0.4447	0.6543	0.4448
No log	2.2059	150	0.4428	0.5967	0.4430
No log	2.2353	152	0.4676	0.5493	0.4679
No log	2.2647	154	0.4542	0.5833	0.4544
No log	2.2941	156	0.4372	0.6312	0.4374
No log	2.3235	158	0.4842	0.6944	0.4844
No log	2.3529	160	0.6415	0.7196	0.6416
No log	2.3824	162	0.7972	0.7001	0.7973
No log	2.4118	164	0.7753	0.6996	0.7754
No log	2.4412	166	0.6333	0.7168	0.6334
No log	2.4706	168	0.4825	0.7103	0.4825
No log	2.5	170	0.4384	0.6651	0.4384
No log	2.5294	172	0.4576	0.6059	0.4576
No log	2.5588	174	0.4453	0.6317	0.4454
No log	2.5882	176	0.4602	0.6838	0.4602
No log	2.6176	178	0.5355	0.7114	0.5355
No log	2.6471	180	0.6103	0.7138	0.6105
No log	2.6765	182	0.6047	0.7209	0.6048
No log	2.7059	184	0.5098	0.6985	0.5100
No log	2.7353	186	0.4528	0.6846	0.4530
No log	2.7647	188	0.4252	0.6570	0.4254
No log	2.7941	190	0.4244	0.6617	0.4247
No log	2.8235	192	0.4399	0.6815	0.4402
No log	2.8529	194	0.4747	0.6992	0.4750
No log	2.8824	196	0.5225	0.7161	0.5227
No log	2.9118	198	0.5233	0.7135	0.5236
No log	2.9412	200	0.4757	0.7010	0.4760
No log	2.9706	202	0.4242	0.6798	0.4244
No log	3.0	204	0.4194	0.6870	0.4196
No log	3.0294	206	0.4232	0.6946	0.4233
No log	3.0588	208	0.4576	0.7193	0.4577
No log	3.0882	210	0.4789	0.7271	0.4790
No log	3.1176	212	0.4761	0.7251	0.4761
No log	3.1471	214	0.4492	0.7110	0.4493
No log	3.1765	216	0.4340	0.6981	0.4341
No log	3.2059	218	0.4228	0.7024	0.4229
No log	3.2353	220	0.4284	0.7029	0.4285
No log	3.2647	222	0.4710	0.7139	0.4712
No log	3.2941	224	0.4969	0.7017	0.4970
No log	3.3235	226	0.4760	0.6958	0.4761
No log	3.3529	228	0.4458	0.6967	0.4459
No log	3.3824	230	0.4482	0.6997	0.4484
No log	3.4118	232	0.4390	0.7062	0.4392
No log	3.4412	234	0.4556	0.7073	0.4558
No log	3.4706	236	0.4625	0.7242	0.4626
No log	3.5	238	0.4673	0.7312	0.4674
No log	3.5294	240	0.5146	0.7311	0.5148
No log	3.5588	242	0.5346	0.7290	0.5348
No log	3.5882	244	0.4986	0.7344	0.4988
No log	3.6176	246	0.4537	0.7202	0.4538
No log	3.6471	248	0.4542	0.7188	0.4544
No log	3.6765	250	0.4541	0.7205	0.4543
No log	3.7059	252	0.4556	0.7175	0.4558
No log	3.7353	254	0.4643	0.7163	0.4645
No log	3.7647	256	0.4924	0.7275	0.4926
No log	3.7941	258	0.5269	0.7295	0.5271
No log	3.8235	260	0.5417	0.7228	0.5419
No log	3.8529	262	0.5307	0.7173	0.5309
No log	3.8824	264	0.5021	0.7082	0.5023
No log	3.9118	266	0.4928	0.7067	0.4930
No log	3.9412	268	0.4711	0.6979	0.4713
No log	3.9706	270	0.4472	0.6943	0.4474
No log	4.0	272	0.4335	0.6822	0.4337
No log	4.0294	274	0.4244	0.6722	0.4246
No log	4.0588	276	0.4246	0.6741	0.4248
No log	4.0882	278	0.4293	0.6810	0.4296
No log	4.1176	280	0.4434	0.7018	0.4436
No log	4.1471	282	0.4503	0.6982	0.4505
No log	4.1765	284	0.4613	0.7045	0.4615
No log	4.2059	286	0.4645	0.7042	0.4647
No log	4.2353	288	0.4655	0.7042	0.4657
No log	4.2647	290	0.4638	0.7105	0.4640
No log	4.2941	292	0.4646	0.7135	0.4649
No log	4.3235	294	0.4559	0.7140	0.4561
No log	4.3529	296	0.4439	0.7070	0.4441
No log	4.3824	298	0.4457	0.7051	0.4459
No log	4.4118	300	0.4524	0.7135	0.4527
No log	4.4412	302	0.4723	0.7151	0.4725
No log	4.4706	304	0.4873	0.7193	0.4876
No log	4.5	306	0.4950	0.7248	0.4953
No log	4.5294	308	0.4952	0.7223	0.4955
No log	4.5588	310	0.4993	0.7205	0.4996
No log	4.5882	312	0.5091	0.7254	0.5094
No log	4.6176	314	0.5039	0.7189	0.5042
No log	4.6471	316	0.4881	0.7099	0.4884
No log	4.6765	318	0.4696	0.7018	0.4700
No log	4.7059	320	0.4550	0.6949	0.4554
No log	4.7353	322	0.4496	0.6947	0.4500
No log	4.7647	324	0.4488	0.6965	0.4492
No log	4.7941	326	0.4495	0.6965	0.4499
No log	4.8235	328	0.4520	0.6965	0.4524
No log	4.8529	330	0.4556	0.7004	0.4559
No log	4.8824	332	0.4584	0.6986	0.4588
No log	4.9118	334	0.4623	0.6986	0.4626
No log	4.9412	336	0.4647	0.7013	0.4650
No log	4.9706	338	0.4656	0.7013	0.4660
No log	5.0	340	0.4663	0.7013	0.4666

Framework versions

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