bert-base-uncased_crows_pairs_classifieronly

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

• Loss: 0.6924
• Accuracy: 0.5364
• Tp: 0.0066
• Tn: 0.5298
• Fp: 0.0033
• Fn: 0.4603

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Tp	Tn	Fp	Fn
0.7219	1.05	20	0.7045	0.5331	0.0	0.5331	0.0	0.4669
0.6962	2.11	40	0.6962	0.4669	0.4669	0.0	0.5331	0.0
0.6983	3.16	60	0.6925	0.5331	0.0	0.5331	0.0	0.4669
0.7018	4.21	80	0.6962	0.4669	0.4669	0.0	0.5331	0.0
0.6972	5.26	100	0.6915	0.5331	0.0	0.5331	0.0	0.4669
0.6964	6.32	120	0.6934	0.4801	0.1391	0.3411	0.1921	0.3278
0.6983	7.37	140	0.6940	0.4636	0.3709	0.0927	0.4404	0.0960
0.7025	8.42	160	0.6964	0.4669	0.4669	0.0	0.5331	0.0
0.6958	9.47	180	0.6919	0.5331	0.0	0.5331	0.0	0.4669
0.7079	10.53	200	0.7002	0.4669	0.4669	0.0	0.5331	0.0
0.7033	11.58	220	0.6915	0.5331	0.0	0.5331	0.0	0.4669
0.6932	12.63	240	0.6933	0.5	0.1060	0.3940	0.1391	0.3609
0.7075	13.68	260	0.6919	0.5331	0.0	0.5331	0.0	0.4669
0.695	14.74	280	0.6936	0.4371	0.1523	0.2848	0.2483	0.3146
0.7068	15.79	300	0.6916	0.5331	0.0	0.5331	0.0	0.4669
0.7007	16.84	320	0.6916	0.5331	0.0	0.5331	0.0	0.4669
0.7035	17.89	340	0.6961	0.4669	0.4669	0.0	0.5331	0.0
0.7002	18.95	360	0.6919	0.5331	0.0	0.5331	0.0	0.4669
0.6992	20.0	380	0.6930	0.5166	0.0331	0.4834	0.0497	0.4338
0.7024	21.05	400	0.6924	0.5364	0.0066	0.5298	0.0033	0.4603
0.694	22.11	420	0.6949	0.4669	0.4603	0.0066	0.5265	0.0066
0.7085	23.16	440	0.6928	0.5265	0.0199	0.5066	0.0265	0.4470
0.6999	24.21	460	0.6936	0.4338	0.1457	0.2881	0.2450	0.3212
0.6926	25.26	480	0.6921	0.5331	0.0033	0.5298	0.0033	0.4636
0.7088	26.32	500	0.6923	0.5331	0.0033	0.5298	0.0033	0.4636
0.6932	27.37	520	0.6922	0.5331	0.0033	0.5298	0.0033	0.4636
0.7011	28.42	540	0.6925	0.5364	0.0066	0.5298	0.0033	0.4603
0.7016	29.47	560	0.6923	0.5331	0.0033	0.5298	0.0033	0.4636
0.7015	30.53	580	0.6925	0.5364	0.0099	0.5265	0.0066	0.4570
0.7002	31.58	600	0.6929	0.5232	0.0331	0.4901	0.0430	0.4338
0.701	32.63	620	0.6932	0.5099	0.0563	0.4536	0.0795	0.4106
0.693	33.68	640	0.6921	0.5331	0.0	0.5331	0.0	0.4669
0.711	34.74	660	0.6925	0.5364	0.0099	0.5265	0.0066	0.4570
0.7013	35.79	680	0.6924	0.5331	0.0033	0.5298	0.0033	0.4636
0.6975	36.84	700	0.6916	0.5331	0.0	0.5331	0.0	0.4669
0.7035	37.89	720	0.6918	0.5331	0.0	0.5331	0.0	0.4669
0.6991	38.95	740	0.6929	0.5232	0.0298	0.4934	0.0397	0.4371
0.7165	40.0	760	0.6923	0.5331	0.0033	0.5298	0.0033	0.4636
0.7029	41.05	780	0.6931	0.5066	0.0464	0.4603	0.0728	0.4205
0.7021	42.11	800	0.6923	0.5331	0.0033	0.5298	0.0033	0.4636
0.6993	43.16	820	0.6935	0.4934	0.1291	0.3642	0.1689	0.3377
0.7	44.21	840	0.6926	0.5331	0.0132	0.5199	0.0132	0.4536
0.7023	45.26	860	0.6926	0.5331	0.0099	0.5232	0.0099	0.4570
0.6961	46.32	880	0.6927	0.5232	0.0132	0.5099	0.0232	0.4536
0.7014	47.37	900	0.6923	0.5331	0.0033	0.5298	0.0033	0.4636
0.7025	48.42	920	0.6924	0.5331	0.0033	0.5298	0.0033	0.4636
0.702	49.47	940	0.6924	0.5364	0.0066	0.5298	0.0033	0.4603

Framework versions

• Transformers 4.26.1
• Pytorch 1.13.1
• Datasets 2.10.1
• Tokenizers 0.13.2