metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - crows_pairs
metrics:
  - accuracy
model-index:
  - name: bert-base-uncased_crows_pairs_finetuned
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: crows_pairs
          type: crows_pairs
          config: crows_pairs
          split: test
          args: crows_pairs
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7649006622516556

bert-base-uncased_crows_pairs_finetuned

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: 2.1731
Accuracy: 0.7649
Tp: 0.3344
Tn: 0.4305
Fp: 0.1126
Fn: 0.1225

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
0.703	1.05	20	0.6889	0.5430	0.0	0.5430	0.0	0.4570
0.6884	2.11	40	0.6886	0.5	0.3013	0.1987	0.3444	0.1556
0.5388	3.16	60	0.6347	0.7053	0.1821	0.5232	0.0199	0.2748
0.2228	4.21	80	0.9802	0.6987	0.1887	0.5099	0.0331	0.2682
0.1644	5.26	100	0.7523	0.7583	0.3675	0.3907	0.1523	0.0894
0.0478	6.32	120	1.5712	0.6954	0.2053	0.4901	0.0530	0.2517
0.0465	7.37	140	1.2587	0.7351	0.2781	0.4570	0.0861	0.1788
0.0313	8.42	160	1.5825	0.7450	0.3775	0.3675	0.1755	0.0795
0.0137	9.47	180	1.3570	0.7318	0.2815	0.4503	0.0927	0.1755
0.04	10.53	200	2.1377	0.6921	0.1887	0.5033	0.0397	0.2682
0.0041	11.58	220	1.6776	0.7351	0.3278	0.4073	0.1358	0.1291
0.0042	12.63	240	1.8873	0.7086	0.2980	0.4106	0.1325	0.1589
0.0009	13.68	260	2.2464	0.6987	0.3543	0.3444	0.1987	0.1026
0.014	14.74	280	1.9753	0.7252	0.3245	0.4007	0.1424	0.1325
0.0026	15.79	300	1.8852	0.7417	0.2914	0.4503	0.0927	0.1656
0.0147	16.84	320	2.0273	0.7351	0.3113	0.4238	0.1192	0.1457
0.0009	17.89	340	1.7328	0.7483	0.3278	0.4205	0.1225	0.1291
0.0085	18.95	360	2.0146	0.7450	0.2815	0.4636	0.0795	0.1755
0.0001	20.0	380	2.0808	0.7450	0.3113	0.4338	0.1093	0.1457
0.0001	21.05	400	2.2655	0.7417	0.3609	0.3808	0.1623	0.0960
0.0034	22.11	420	2.0298	0.7583	0.3079	0.4503	0.0927	0.1490
0.0082	23.16	440	2.0650	0.7550	0.3344	0.4205	0.1225	0.1225
0.0001	24.21	460	2.2472	0.7450	0.2748	0.4702	0.0728	0.1821
0.0001	25.26	480	2.3655	0.7351	0.3709	0.3642	0.1788	0.0861
0.0004	26.32	500	2.1407	0.7550	0.3510	0.4040	0.1391	0.1060
0.0001	27.37	520	2.1168	0.7450	0.3642	0.3808	0.1623	0.0927
0.0002	28.42	540	2.2050	0.7517	0.3775	0.3742	0.1689	0.0795
0.0	29.47	560	2.0560	0.7682	0.3212	0.4470	0.0960	0.1358
0.0	30.53	580	2.0859	0.7715	0.3179	0.4536	0.0894	0.1391
0.0	31.58	600	2.0958	0.7715	0.3179	0.4536	0.0894	0.1391
0.0	32.63	620	2.1039	0.7715	0.3179	0.4536	0.0894	0.1391
0.0	33.68	640	2.1113	0.7715	0.3179	0.4536	0.0894	0.1391
0.0	34.74	660	2.1180	0.7715	0.3179	0.4536	0.0894	0.1391
0.0	35.79	680	2.1127	0.7715	0.3278	0.4437	0.0993	0.1291
0.0	36.84	700	2.1376	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	37.89	720	2.1460	0.7616	0.3377	0.4238	0.1192	0.1192
0.0	38.95	740	2.1507	0.7649	0.3377	0.4272	0.1159	0.1192
0.0	40.0	760	2.1548	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	41.05	780	2.1586	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	42.11	800	2.1620	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	43.16	820	2.1649	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	44.21	840	2.1674	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	45.26	860	2.1690	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	46.32	880	2.1705	0.7682	0.3377	0.4305	0.1126	0.1192
0.0	47.37	900	2.1717	0.7649	0.3344	0.4305	0.1126	0.1225
0.0	48.42	920	2.1726	0.7649	0.3344	0.4305	0.1126	0.1225
0.0	49.47	940	2.1731	0.7649	0.3344	0.4305	0.1126	0.1225

Framework versions

Transformers 4.26.1
Pytorch 1.13.1
Datasets 2.10.1
Tokenizers 0.13.2