bert-base-uncased-amazon_polarity

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

• Loss: 0.2945
• Accuracy: 0.9465

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: 5e-05
• train_batch_size: 1
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1782000
• training_steps: 17820000

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7155	0.0	2000	0.7060	0.4622
0.7054	0.0	4000	0.6925	0.5165
0.6842	0.0	6000	0.6653	0.6116
0.6375	0.0	8000	0.5721	0.7909
0.4671	0.0	10000	0.3238	0.8770
0.3403	0.0	12000	0.3692	0.8861
0.4162	0.0	14000	0.4560	0.8908
0.4728	0.0	16000	0.5071	0.8980
0.5111	0.01	18000	0.5204	0.9015
0.4792	0.01	20000	0.5193	0.9076
0.544	0.01	22000	0.4835	0.9133
0.4745	0.01	24000	0.4689	0.9170
0.4403	0.01	26000	0.4778	0.9177
0.4405	0.01	28000	0.4754	0.9163
0.4375	0.01	30000	0.4808	0.9175
0.4628	0.01	32000	0.4340	0.9244
0.4488	0.01	34000	0.4162	0.9265
0.4608	0.01	36000	0.4031	0.9271
0.4478	0.01	38000	0.4502	0.9253
0.4237	0.01	40000	0.4087	0.9279
0.4601	0.01	42000	0.4133	0.9269
0.4153	0.01	44000	0.4230	0.9306
0.4096	0.01	46000	0.4108	0.9301
0.4348	0.01	48000	0.4138	0.9309
0.3787	0.01	50000	0.4066	0.9324
0.4172	0.01	52000	0.4812	0.9206
0.3897	0.02	54000	0.4013	0.9325
0.3787	0.02	56000	0.3837	0.9344
0.4253	0.02	58000	0.3925	0.9347
0.3959	0.02	60000	0.3907	0.9353
0.4402	0.02	62000	0.3708	0.9341
0.4115	0.02	64000	0.3477	0.9361
0.3876	0.02	66000	0.3634	0.9373
0.4286	0.02	68000	0.3778	0.9378
0.422	0.02	70000	0.3540	0.9361
0.3732	0.02	72000	0.3853	0.9378
0.3641	0.02	74000	0.3951	0.9386
0.3701	0.02	76000	0.3582	0.9388
0.4498	0.02	78000	0.3268	0.9375
0.3587	0.02	80000	0.3825	0.9401
0.4474	0.02	82000	0.3155	0.9391
0.3598	0.02	84000	0.3666	0.9388
0.389	0.02	86000	0.3745	0.9377
0.3625	0.02	88000	0.3776	0.9387
0.3511	0.03	90000	0.4275	0.9336
0.3428	0.03	92000	0.4301	0.9336
0.4042	0.03	94000	0.3547	0.9359
0.3583	0.03	96000	0.3763	0.9396
0.3887	0.03	98000	0.3213	0.9412
0.3915	0.03	100000	0.3557	0.9409
0.3378	0.03	102000	0.3627	0.9418
0.349	0.03	104000	0.3614	0.9402
0.3596	0.03	106000	0.3834	0.9381
0.3519	0.03	108000	0.3560	0.9421
0.3598	0.03	110000	0.3485	0.9419
0.3642	0.03	112000	0.3754	0.9395
0.3477	0.03	114000	0.3634	0.9426
0.4202	0.03	116000	0.3071	0.9427
0.3656	0.03	118000	0.3155	0.9441
0.3709	0.03	120000	0.2923	0.9433
0.374	0.03	122000	0.3272	0.9441
0.3142	0.03	124000	0.3348	0.9444
0.3452	0.04	126000	0.3603	0.9436
0.3365	0.04	128000	0.3339	0.9434
0.3353	0.04	130000	0.3471	0.9450
0.343	0.04	132000	0.3508	0.9418
0.3174	0.04	134000	0.3753	0.9436
0.3009	0.04	136000	0.3687	0.9422
0.3785	0.04	138000	0.3818	0.9396
0.3199	0.04	140000	0.3291	0.9438
0.4049	0.04	142000	0.3372	0.9454
0.3435	0.04	144000	0.3315	0.9459
0.3814	0.04	146000	0.3462	0.9401
0.359	0.04	148000	0.3981	0.9361
0.3552	0.04	150000	0.3226	0.9469
0.345	0.04	152000	0.3731	0.9384
0.3228	0.04	154000	0.2956	0.9471
0.3637	0.04	156000	0.2869	0.9477
0.349	0.04	158000	0.3331	0.9430
0.3374	0.04	160000	0.4159	0.9340
0.3718	0.05	162000	0.3241	0.9459
0.315	0.05	164000	0.3544	0.9391
0.3215	0.05	166000	0.3311	0.9451
0.3464	0.05	168000	0.3682	0.9453
0.3495	0.05	170000	0.3193	0.9469
0.305	0.05	172000	0.4132	0.9389
0.3479	0.05	174000	0.3465	0.947
0.3537	0.05	176000	0.3277	0.9449

Framework versions

• Transformers 4.10.2
• Pytorch 1.7.1
• Datasets 1.12.1
• Tokenizers 0.10.3