best_model-sst-2-16-42

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4301
• Accuracy: 0.875

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 150

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	0.4423	0.7812
No log	2.0	2	0.4424	0.7812
No log	3.0	3	0.4425	0.7812
No log	4.0	4	0.4427	0.7812
No log	5.0	5	0.4430	0.7812
No log	6.0	6	0.4433	0.7812
No log	7.0	7	0.4437	0.7812
No log	8.0	8	0.4442	0.7812
No log	9.0	9	0.4448	0.7812
0.458	10.0	10	0.4454	0.7812
0.458	11.0	11	0.4461	0.7812
0.458	12.0	12	0.4467	0.7812
0.458	13.0	13	0.4475	0.7812
0.458	14.0	14	0.4482	0.7812
0.458	15.0	15	0.4489	0.7812
0.458	16.0	16	0.4497	0.7812
0.458	17.0	17	0.4506	0.7812
0.458	18.0	18	0.4517	0.7812
0.458	19.0	19	0.4528	0.7812
0.4251	20.0	20	0.4540	0.7812
0.4251	21.0	21	0.4553	0.7812
0.4251	22.0	22	0.4565	0.7812
0.4251	23.0	23	0.4574	0.7812
0.4251	24.0	24	0.4582	0.7812
0.4251	25.0	25	0.4589	0.7812
0.4251	26.0	26	0.4595	0.7812
0.4251	27.0	27	0.4600	0.7812
0.4251	28.0	28	0.4606	0.7812
0.4251	29.0	29	0.4608	0.7812
0.3723	30.0	30	0.4610	0.7812
0.3723	31.0	31	0.4612	0.7812
0.3723	32.0	32	0.4612	0.7812
0.3723	33.0	33	0.4611	0.7812
0.3723	34.0	34	0.4610	0.7812
0.3723	35.0	35	0.4609	0.7812
0.3723	36.0	36	0.4604	0.7812
0.3723	37.0	37	0.4601	0.7812
0.3723	38.0	38	0.4592	0.7812
0.3723	39.0	39	0.4583	0.7812
0.3304	40.0	40	0.4572	0.7812
0.3304	41.0	41	0.4568	0.7812
0.3304	42.0	42	0.4562	0.7812
0.3304	43.0	43	0.4557	0.7812
0.3304	44.0	44	0.4552	0.7812
0.3304	45.0	45	0.4543	0.7812
0.3304	46.0	46	0.4541	0.7812
0.3304	47.0	47	0.4536	0.7812
0.3304	48.0	48	0.4534	0.7812
0.3304	49.0	49	0.4528	0.7812
0.2724	50.0	50	0.4526	0.7812
0.2724	51.0	51	0.4533	0.7812
0.2724	52.0	52	0.4544	0.7812
0.2724	53.0	53	0.4554	0.7812
0.2724	54.0	54	0.4563	0.7812
0.2724	55.0	55	0.4570	0.7812
0.2724	56.0	56	0.4578	0.7812
0.2724	57.0	57	0.4587	0.7812
0.2724	58.0	58	0.4588	0.7812
0.2724	59.0	59	0.4580	0.7812
0.2089	60.0	60	0.4569	0.7812
0.2089	61.0	61	0.4553	0.7812
0.2089	62.0	62	0.4531	0.7812
0.2089	63.0	63	0.4509	0.7812
0.2089	64.0	64	0.4478	0.7812
0.2089	65.0	65	0.4449	0.7812
0.2089	66.0	66	0.4425	0.7812
0.2089	67.0	67	0.4414	0.7812
0.2089	68.0	68	0.4399	0.7812
0.2089	69.0	69	0.4391	0.7812
0.163	70.0	70	0.4382	0.7812
0.163	71.0	71	0.4366	0.7812
0.163	72.0	72	0.4356	0.7812
0.163	73.0	73	0.4344	0.7812
0.163	74.0	74	0.4331	0.7812
0.163	75.0	75	0.4320	0.7812
0.163	76.0	76	0.4310	0.7812
0.163	77.0	77	0.4294	0.7812
0.163	78.0	78	0.4285	0.7812
0.163	79.0	79	0.4273	0.7812
0.1282	80.0	80	0.4267	0.7812
0.1282	81.0	81	0.4262	0.7812
0.1282	82.0	82	0.4271	0.7812
0.1282	83.0	83	0.4275	0.7812
0.1282	84.0	84	0.4289	0.7812
0.1282	85.0	85	0.4295	0.7812
0.1282	86.0	86	0.4293	0.7812
0.1282	87.0	87	0.4284	0.7812
0.1282	88.0	88	0.4275	0.7812
0.1282	89.0	89	0.4263	0.7812
0.1021	90.0	90	0.4249	0.7812
0.1021	91.0	91	0.4233	0.7812
0.1021	92.0	92	0.4210	0.7812
0.1021	93.0	93	0.4188	0.7812
0.1021	94.0	94	0.4166	0.7812
0.1021	95.0	95	0.4162	0.7812
0.1021	96.0	96	0.4154	0.7812
0.1021	97.0	97	0.4139	0.7812
0.1021	98.0	98	0.4126	0.8125
0.1021	99.0	99	0.4117	0.8125
0.0862	100.0	100	0.4115	0.8125
0.0862	101.0	101	0.4119	0.8125
0.0862	102.0	102	0.4116	0.8125
0.0862	103.0	103	0.4119	0.8125
0.0862	104.0	104	0.4141	0.8125
0.0862	105.0	105	0.4156	0.8125
0.0862	106.0	106	0.4165	0.8438
0.0862	107.0	107	0.4170	0.8438
0.0862	108.0	108	0.4183	0.8438
0.0862	109.0	109	0.4200	0.8438
0.0708	110.0	110	0.4212	0.8438
0.0708	111.0	111	0.4216	0.8438
0.0708	112.0	112	0.4213	0.8438
0.0708	113.0	113	0.4205	0.8438
0.0708	114.0	114	0.4191	0.8438
0.0708	115.0	115	0.4180	0.8438
0.0708	116.0	116	0.4167	0.8438
0.0708	117.0	117	0.4154	0.8438
0.0708	118.0	118	0.4143	0.8438
0.0708	119.0	119	0.4125	0.8438
0.056	120.0	120	0.4109	0.8438
0.056	121.0	121	0.4090	0.8438
0.056	122.0	122	0.4092	0.8438
0.056	123.0	123	0.4093	0.8438
0.056	124.0	124	0.4094	0.8438
0.056	125.0	125	0.4095	0.8438
0.056	126.0	126	0.4096	0.8438
0.056	127.0	127	0.4103	0.8438
0.056	128.0	128	0.4109	0.8438
0.056	129.0	129	0.4111	0.8438
0.0436	130.0	130	0.4110	0.8438
0.0436	131.0	131	0.4114	0.8438
0.0436	132.0	132	0.4119	0.8438
0.0436	133.0	133	0.4121	0.8438
0.0436	134.0	134	0.4119	0.875
0.0436	135.0	135	0.4119	0.875
0.0436	136.0	136	0.4121	0.875
0.0436	137.0	137	0.4131	0.875
0.0436	138.0	138	0.4138	0.875
0.0436	139.0	139	0.4150	0.875
0.0326	140.0	140	0.4167	0.875
0.0326	141.0	141	0.4181	0.875
0.0326	142.0	142	0.4194	0.875
0.0326	143.0	143	0.4206	0.875
0.0326	144.0	144	0.4217	0.875
0.0326	145.0	145	0.4228	0.875
0.0326	146.0	146	0.4242	0.875
0.0326	147.0	147	0.4256	0.875
0.0326	148.0	148	0.4268	0.875
0.0326	149.0	149	0.4280	0.875
0.0247	150.0	150	0.4301	0.875

Framework versions

• Transformers 4.32.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.4.0
• Tokenizers 0.13.3