best_model-sst-2-16-100

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: 0.4760
• Accuracy: 0.9062

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	0.3957	0.875
No log	2.0	2	0.3958	0.875
No log	3.0	3	0.3961	0.875
No log	4.0	4	0.3964	0.875
No log	5.0	5	0.3968	0.875
No log	6.0	6	0.3971	0.875
No log	7.0	7	0.3974	0.875
No log	8.0	8	0.3976	0.875
No log	9.0	9	0.3978	0.875
0.2951	10.0	10	0.3979	0.875
0.2951	11.0	11	0.3977	0.875
0.2951	12.0	12	0.3971	0.875
0.2951	13.0	13	0.3963	0.875
0.2951	14.0	14	0.3954	0.875
0.2951	15.0	15	0.3943	0.875
0.2951	16.0	16	0.3929	0.875
0.2951	17.0	17	0.3912	0.875
0.2951	18.0	18	0.3895	0.875
0.2951	19.0	19	0.3876	0.875
0.2889	20.0	20	0.3854	0.875
0.2889	21.0	21	0.3830	0.875
0.2889	22.0	22	0.3806	0.875
0.2889	23.0	23	0.3789	0.875
0.2889	24.0	24	0.3770	0.875
0.2889	25.0	25	0.3755	0.9062
0.2889	26.0	26	0.3739	0.9062
0.2889	27.0	27	0.3728	0.9062
0.2889	28.0	28	0.3716	0.9062
0.2889	29.0	29	0.3704	0.9062
0.2147	30.0	30	0.3697	0.9062
0.2147	31.0	31	0.3692	0.9062
0.2147	32.0	32	0.3688	0.9062
0.2147	33.0	33	0.3686	0.9062
0.2147	34.0	34	0.3684	0.9062
0.2147	35.0	35	0.3683	0.9062
0.2147	36.0	36	0.3682	0.9062
0.2147	37.0	37	0.3684	0.9062
0.2147	38.0	38	0.3684	0.9062
0.2147	39.0	39	0.3685	0.9062
0.1272	40.0	40	0.3689	0.9062
0.1272	41.0	41	0.3693	0.9062
0.1272	42.0	42	0.3701	0.9062
0.1272	43.0	43	0.3709	0.875
0.1272	44.0	44	0.3719	0.875
0.1272	45.0	45	0.3728	0.875
0.1272	46.0	46	0.3731	0.875
0.1272	47.0	47	0.3728	0.875
0.1272	48.0	48	0.3729	0.875
0.1272	49.0	49	0.3726	0.875
0.0531	50.0	50	0.3726	0.875
0.0531	51.0	51	0.3721	0.875
0.0531	52.0	52	0.3716	0.875
0.0531	53.0	53	0.3715	0.875
0.0531	54.0	54	0.3707	0.875
0.0531	55.0	55	0.3706	0.875
0.0531	56.0	56	0.3702	0.875
0.0531	57.0	57	0.3707	0.875
0.0531	58.0	58	0.3716	0.875
0.0531	59.0	59	0.3735	0.875
0.0221	60.0	60	0.3754	0.875
0.0221	61.0	61	0.3775	0.875
0.0221	62.0	62	0.3801	0.875
0.0221	63.0	63	0.3824	0.875
0.0221	64.0	64	0.3847	0.875
0.0221	65.0	65	0.3871	0.875
0.0221	66.0	66	0.3883	0.875
0.0221	67.0	67	0.3885	0.875
0.0221	68.0	68	0.3886	0.875
0.0221	69.0	69	0.3876	0.875
0.0151	70.0	70	0.3869	0.875
0.0151	71.0	71	0.3869	0.875
0.0151	72.0	72	0.3871	0.875
0.0151	73.0	73	0.3875	0.875
0.0151	74.0	74	0.3872	0.875
0.0151	75.0	75	0.3873	0.875
0.0151	76.0	76	0.3869	0.875
0.0151	77.0	77	0.3868	0.875
0.0151	78.0	78	0.3876	0.9062
0.0151	79.0	79	0.3885	0.9062
0.0099	80.0	80	0.3896	0.9062
0.0099	81.0	81	0.3908	0.9062
0.0099	82.0	82	0.3921	0.9062
0.0099	83.0	83	0.3935	0.9062
0.0099	84.0	84	0.3952	0.9062
0.0099	85.0	85	0.3972	0.9062
0.0099	86.0	86	0.3992	0.9062
0.0099	87.0	87	0.4017	0.9062
0.0099	88.0	88	0.4042	0.9062
0.0099	89.0	89	0.4062	0.9062
0.0074	90.0	90	0.4082	0.9062
0.0074	91.0	91	0.4100	0.9062
0.0074	92.0	92	0.4118	0.9062
0.0074	93.0	93	0.4135	0.9062
0.0074	94.0	94	0.4152	0.9062
0.0074	95.0	95	0.4169	0.9062
0.0074	96.0	96	0.4185	0.9062
0.0074	97.0	97	0.4198	0.9062
0.0074	98.0	98	0.4211	0.9062
0.0074	99.0	99	0.4224	0.9062
0.006	100.0	100	0.4236	0.9062
0.006	101.0	101	0.4248	0.9062
0.006	102.0	102	0.4259	0.9062
0.006	103.0	103	0.4271	0.9062
0.006	104.0	104	0.4284	0.9062
0.006	105.0	105	0.4296	0.9062
0.006	106.0	106	0.4298	0.9062
0.006	107.0	107	0.4283	0.9062
0.006	108.0	108	0.4276	0.9062
0.006	109.0	109	0.4275	0.9062
0.0065	110.0	110	0.4280	0.9062
0.0065	111.0	111	0.4287	0.9062
0.0065	112.0	112	0.4297	0.9062
0.0065	113.0	113	0.4309	0.9062
0.0065	114.0	114	0.4322	0.9062
0.0065	115.0	115	0.4337	0.9062
0.0065	116.0	116	0.4352	0.9062
0.0065	117.0	117	0.4367	0.9062
0.0065	118.0	118	0.4383	0.9062
0.0065	119.0	119	0.4399	0.9062
0.0046	120.0	120	0.4413	0.9062
0.0046	121.0	121	0.4428	0.9062
0.0046	122.0	122	0.4443	0.9062
0.0046	123.0	123	0.4457	0.9062
0.0046	124.0	124	0.4470	0.9062
0.0046	125.0	125	0.4483	0.9062
0.0046	126.0	126	0.4495	0.9062
0.0046	127.0	127	0.4508	0.9062
0.0046	128.0	128	0.4520	0.9062
0.0046	129.0	129	0.4531	0.9062
0.0037	130.0	130	0.4543	0.9062
0.0037	131.0	131	0.4555	0.9062
0.0037	132.0	132	0.4566	0.9062
0.0037	133.0	133	0.4577	0.9062
0.0037	134.0	134	0.4588	0.9062
0.0037	135.0	135	0.4599	0.9062
0.0037	136.0	136	0.4610	0.9062
0.0037	137.0	137	0.4622	0.9062
0.0037	138.0	138	0.4633	0.9062
0.0037	139.0	139	0.4644	0.9062
0.0033	140.0	140	0.4655	0.9062
0.0033	141.0	141	0.4666	0.9062
0.0033	142.0	142	0.4677	0.9062
0.0033	143.0	143	0.4688	0.9062
0.0033	144.0	144	0.4700	0.9062
0.0033	145.0	145	0.4712	0.9062
0.0033	146.0	146	0.4725	0.9062
0.0033	147.0	147	0.4733	0.9062
0.0033	148.0	148	0.4742	0.9062
0.0033	149.0	149	0.4751	0.9062
0.0029	150.0	150	0.4760	0.9062

Framework versions

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