bert_baseline_prompt_adherence_task5_fold2

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: 0.4974
• Qwk: 0.6961
• Mse: 0.4980

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	2.2821	0.0	2.2817
No log	0.0588	4	1.9470	-0.0246	1.9459
No log	0.0882	6	1.6364	0.0	1.6351
No log	0.1176	8	1.2066	0.0	1.2052
No log	0.1471	10	0.9869	-0.0028	0.9857
No log	0.1765	12	0.8887	0.2922	0.8874
No log	0.2059	14	0.8591	0.3046	0.8578
No log	0.2353	16	0.8205	0.3085	0.8194
No log	0.2647	18	0.7874	0.3196	0.7865
No log	0.2941	20	0.7405	0.4192	0.7398
No log	0.3235	22	0.7247	0.3757	0.7242
No log	0.3529	24	0.6294	0.4054	0.6292
No log	0.3824	26	0.6148	0.4262	0.6148
No log	0.4118	28	0.6777	0.3954	0.6778
No log	0.4412	30	0.7120	0.4234	0.7120
No log	0.4706	32	0.6023	0.5248	0.6026
No log	0.5	34	0.5625	0.5623	0.5629
No log	0.5294	36	0.5273	0.4965	0.5276
No log	0.5588	38	0.5054	0.5104	0.5058
No log	0.5882	40	0.5157	0.6071	0.5163
No log	0.6176	42	0.6802	0.6978	0.6809
No log	0.6471	44	0.7500	0.6921	0.7507
No log	0.6765	46	0.6160	0.7080	0.6166
No log	0.7059	48	0.4815	0.6385	0.4822
No log	0.7353	50	0.4729	0.5059	0.4735
No log	0.7647	52	0.4612	0.5337	0.4618
No log	0.7941	54	0.4666	0.5762	0.4670
No log	0.8235	56	0.5076	0.6517	0.5081
No log	0.8529	58	0.6052	0.7195	0.6058
No log	0.8824	60	0.6030	0.7166	0.6037
No log	0.9118	62	0.4870	0.6991	0.4877
No log	0.9412	64	0.4240	0.5768	0.4248
No log	0.9706	66	0.4489	0.5218	0.4498
No log	1.0	68	0.4155	0.5740	0.4162
No log	1.0294	70	0.4471	0.6602	0.4478
No log	1.0588	72	0.5177	0.7016	0.5184
No log	1.0882	74	0.5263	0.7127	0.5269
No log	1.1176	76	0.4758	0.7010	0.4764
No log	1.1471	78	0.4357	0.6628	0.4362
No log	1.1765	80	0.4209	0.6616	0.4215
No log	1.2059	82	0.4061	0.6221	0.4068
No log	1.2353	84	0.4186	0.6655	0.4194
No log	1.2647	86	0.4413	0.7015	0.4422
No log	1.2941	88	0.4473	0.7042	0.4482
No log	1.3235	90	0.4758	0.7292	0.4768
No log	1.3529	92	0.4618	0.7148	0.4629
No log	1.3824	94	0.4397	0.6998	0.4407
No log	1.4118	96	0.4345	0.7052	0.4356
No log	1.4412	98	0.4052	0.6829	0.4063
No log	1.4706	100	0.4048	0.5959	0.4060
No log	1.5	102	0.4027	0.6179	0.4039
No log	1.5294	104	0.4026	0.6680	0.4037
No log	1.5588	106	0.4391	0.7087	0.4401
No log	1.5882	108	0.4712	0.7336	0.4721
No log	1.6176	110	0.4276	0.6772	0.4283
No log	1.6471	112	0.4190	0.6668	0.4197
No log	1.6765	114	0.4454	0.7091	0.4461
No log	1.7059	116	0.5060	0.7442	0.5068
No log	1.7353	118	0.4610	0.7042	0.4618
No log	1.7647	120	0.4166	0.6410	0.4173
No log	1.7941	122	0.4153	0.6082	0.4160
No log	1.8235	124	0.4220	0.6257	0.4227
No log	1.8529	126	0.4287	0.6457	0.4294
No log	1.8824	128	0.4788	0.6965	0.4796
No log	1.9118	130	0.5027	0.7107	0.5037
No log	1.9412	132	0.4600	0.6896	0.4610
No log	1.9706	134	0.4402	0.6761	0.4412
No log	2.0	136	0.4284	0.6458	0.4294
No log	2.0294	138	0.4266	0.6560	0.4276
No log	2.0588	140	0.4405	0.6865	0.4414
No log	2.0882	142	0.4423	0.6897	0.4432
No log	2.1176	144	0.4177	0.6767	0.4185
No log	2.1471	146	0.4062	0.6706	0.4070
No log	2.1765	148	0.3957	0.6445	0.3964
No log	2.2059	150	0.4027	0.6540	0.4034
No log	2.2353	152	0.4679	0.7228	0.4687
No log	2.2647	154	0.5938	0.7484	0.5948
No log	2.2941	156	0.6232	0.7519	0.6242
No log	2.3235	158	0.5488	0.7441	0.5497
No log	2.3529	160	0.4352	0.6753	0.4359
No log	2.3824	162	0.4015	0.6315	0.4021
No log	2.4118	164	0.4037	0.6245	0.4044
No log	2.4412	166	0.4137	0.6409	0.4144
No log	2.4706	168	0.4356	0.6641	0.4365
No log	2.5	170	0.4603	0.6627	0.4613
No log	2.5294	172	0.4757	0.6866	0.4768
No log	2.5588	174	0.5170	0.7188	0.5181
No log	2.5882	176	0.5960	0.7437	0.5971
No log	2.6176	178	0.5579	0.7426	0.5589
No log	2.6471	180	0.4732	0.7249	0.4741
No log	2.6765	182	0.4149	0.6476	0.4157
No log	2.7059	184	0.4075	0.6276	0.4083
No log	2.7353	186	0.4103	0.6458	0.4110
No log	2.7647	188	0.4533	0.6988	0.4541
No log	2.7941	190	0.4953	0.7266	0.4961
No log	2.8235	192	0.4722	0.7070	0.4729
No log	2.8529	194	0.4285	0.6622	0.4292
No log	2.8824	196	0.4084	0.6414	0.4090
No log	2.9118	198	0.4201	0.6096	0.4208
No log	2.9412	200	0.4175	0.6451	0.4183
No log	2.9706	202	0.4422	0.6796	0.4430
No log	3.0	204	0.5450	0.7282	0.5459
No log	3.0294	206	0.6121	0.7490	0.6130
No log	3.0588	208	0.5880	0.7396	0.5889
No log	3.0882	210	0.5100	0.7223	0.5108
No log	3.1176	212	0.4333	0.6771	0.4340
No log	3.1471	214	0.4124	0.6371	0.4131
No log	3.1765	216	0.4112	0.6370	0.4119
No log	3.2059	218	0.4155	0.6550	0.4162
No log	3.2353	220	0.4255	0.6713	0.4261
No log	3.2647	222	0.4460	0.6958	0.4466
No log	3.2941	224	0.4749	0.7032	0.4756
No log	3.3235	226	0.5252	0.7171	0.5260
No log	3.3529	228	0.5761	0.7271	0.5770
No log	3.3824	230	0.5424	0.7258	0.5433
No log	3.4118	232	0.5033	0.7179	0.5041
No log	3.4412	234	0.4933	0.7133	0.4940
No log	3.4706	236	0.4964	0.7201	0.4971
No log	3.5	238	0.5313	0.7329	0.5320
No log	3.5294	240	0.5494	0.7341	0.5502
No log	3.5588	242	0.5223	0.7392	0.5230
No log	3.5882	244	0.4708	0.7181	0.4714
No log	3.6176	246	0.4365	0.6782	0.4371
No log	3.6471	248	0.4337	0.6746	0.4344
No log	3.6765	250	0.4371	0.6763	0.4378
No log	3.7059	252	0.4610	0.7008	0.4617
No log	3.7353	254	0.4930	0.7247	0.4938
No log	3.7647	256	0.5341	0.7435	0.5349
No log	3.7941	258	0.5192	0.7332	0.5200
No log	3.8235	260	0.4837	0.7158	0.4845
No log	3.8529	262	0.4452	0.6729	0.4460
No log	3.8824	264	0.4277	0.6541	0.4284
No log	3.9118	266	0.4262	0.6547	0.4268
No log	3.9412	268	0.4212	0.6513	0.4219
No log	3.9706	270	0.4237	0.6547	0.4244
No log	4.0	272	0.4380	0.6695	0.4387
No log	4.0294	274	0.4502	0.6811	0.4508
No log	4.0588	276	0.4730	0.7114	0.4737
No log	4.0882	278	0.4878	0.7223	0.4885
No log	4.1176	280	0.5147	0.7287	0.5155
No log	4.1471	282	0.5187	0.7326	0.5195
No log	4.1765	284	0.5017	0.7180	0.5025
No log	4.2059	286	0.4877	0.7105	0.4884
No log	4.2353	288	0.4733	0.6938	0.4740
No log	4.2647	290	0.4711	0.6904	0.4717
No log	4.2941	292	0.4579	0.6737	0.4585
No log	4.3235	294	0.4489	0.6643	0.4495
No log	4.3529	296	0.4533	0.6660	0.4538
No log	4.3824	298	0.4672	0.6868	0.4677
No log	4.4118	300	0.4840	0.6988	0.4846
No log	4.4412	302	0.4849	0.6971	0.4856
No log	4.4706	304	0.4817	0.6934	0.4823
No log	4.5	306	0.4800	0.6933	0.4807
No log	4.5294	308	0.4724	0.6864	0.4730
No log	4.5588	310	0.4680	0.6745	0.4686
No log	4.5882	312	0.4671	0.6754	0.4677
No log	4.6176	314	0.4664	0.6754	0.4670
No log	4.6471	316	0.4698	0.6823	0.4704
No log	4.6765	318	0.4733	0.6823	0.4739
No log	4.7059	320	0.4816	0.6823	0.4822
No log	4.7353	322	0.4931	0.6945	0.4937
No log	4.7647	324	0.5064	0.7007	0.5071
No log	4.7941	326	0.5134	0.7058	0.5140
No log	4.8235	328	0.5128	0.7058	0.5134
No log	4.8529	330	0.5093	0.7063	0.5099
No log	4.8824	332	0.5035	0.7045	0.5042
No log	4.9118	334	0.5013	0.7038	0.5020
No log	4.9412	336	0.5006	0.7047	0.5012
No log	4.9706	338	0.4986	0.6978	0.4992
No log	5.0	340	0.4974	0.6961	0.4980

Framework versions

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