Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0037	1.0	178	1.8515	0.7163
0.0017	2.0	356	1.7404	0.7163
0.001	3.0	534	1.2895	0.7921
0.0012	4.0	712	1.3320	0.7669
0.0005	5.0	890	1.3646	0.7949
0.0002	6.0	1068	1.5997	0.7809
0.0001	7.0	1246	1.5772	0.7753
0.0003	8.0	1424	1.7599	0.7556
0.0001	9.0	1602	1.7494	0.7640
0.0001	10.0	1780	1.9942	0.7556
0.0001	11.0	1958	1.9370	0.75
0.0	12.0	2136	1.9671	0.7781
0.0001	13.0	2314	2.1223	0.7640
0.0	14.0	2492	2.1653	0.7472
0.0001	15.0	2670	1.9924	0.75
0.0	16.0	2848	2.1778	0.7528
0.0	17.0	3026	2.3010	0.7612
0.0	18.0	3204	2.2210	0.7669
0.0	19.0	3382	2.3333	0.7556
0.0	20.0	3560	1.8684	0.7697
0.0976	21.0	3738	1.9417	0.7584
0.0	22.0	3916	2.1385	0.7472
0.0	23.0	4094	1.9774	0.7669
0.0	24.0	4272	2.0778	0.75
0.0001	25.0	4450	2.4343	0.7331
0.0	26.0	4628	2.1331	0.7528
0.0	27.0	4806	2.2511	0.7640
0.0	28.0	4984	2.2422	0.7584
0.0	29.0	5162	2.1228	0.7669
0.0006	30.0	5340	2.0973	0.7725
0.0	31.0	5518	1.9392	0.7809
0.0	32.0	5696	2.2996	0.7107
0.4186	33.0	5874	2.2191	0.7584
0.0	34.0	6052	2.2233	0.75
0.0	35.0	6230	2.2263	0.7584
0.0	36.0	6408	2.2205	0.7584
0.0	37.0	6586	2.4488	0.7444
0.0	38.0	6764	2.5616	0.7360
0.0	39.0	6942	2.5941	0.7416
0.0	40.0	7120	2.5129	0.7528
0.0	41.0	7298	2.4978	0.7360
0.0	42.0	7476	2.3089	0.7528
0.0	43.0	7654	2.5056	0.7472
0.0	44.0	7832	2.5786	0.7416
0.0	45.0	8010	2.2956	0.7640
0.0	46.0	8188	2.5265	0.7472
0.0	47.0	8366	2.4396	0.7584
0.0	48.0	8544	2.5547	0.7472
0.0	49.0	8722	2.5556	0.7528
0.0	50.0	8900	2.5732	0.7528
0.0	51.0	9078	2.5062	0.7556
0.0	52.0	9256	2.5504	0.7528
0.0	53.0	9434	2.5602	0.7528
0.0	54.0	9612	2.5627	0.7472
0.0	55.0	9790	2.6575	0.75
0.0	56.0	9968	2.6239	0.7528
0.0	57.0	10146	2.4757	0.7697
0.0	58.0	10324	2.4862	0.7612
0.0	59.0	10502	3.2968	0.6938
0.0	60.0	10680	2.5265	0.7472
0.0	61.0	10858	2.1426	0.7978
0.0	62.0	11036	2.4674	0.7640
0.0	63.0	11214	2.3496	0.7640
0.0	64.0	11392	2.4010	0.7556
0.0	65.0	11570	2.4081	0.7725
0.0	66.0	11748	2.4022	0.7753
0.0	67.0	11926	2.2982	0.7753
0.0	68.0	12104	2.4628	0.7612
0.0	69.0	12282	2.5764	0.7640
0.0	70.0	12460	2.4056	0.7781
0.0	71.0	12638	2.3265	0.7865
0.0	72.0	12816	2.5182	0.7640
0.0	73.0	12994	2.3872	0.7556
0.0	74.0	13172	2.7281	0.7388
0.0	75.0	13350	2.4907	0.7612
0.0	76.0	13528	2.5323	0.7584
0.0	77.0	13706	2.2055	0.7837
0.0	78.0	13884	2.2227	0.7865
0.0	79.0	14062	2.2794	0.7753
0.0	80.0	14240	2.2886	0.7753
0.0	81.0	14418	2.8320	0.7444
0.0	82.0	14596	2.8252	0.7472
0.0	83.0	14774	2.2986	0.7837
0.0	84.0	14952	2.7879	0.7416
0.0	85.0	15130	2.7926	0.7416
0.0	86.0	15308	2.7656	0.7472
0.0	87.0	15486	2.7336	0.7444
0.0	88.0	15664	2.7320	0.7444
0.0	89.0	15842	2.7402	0.7444
0.0	90.0	16020	2.7415	0.7444
0.0	91.0	16198	2.7406	0.7444
0.0	92.0	16376	2.7327	0.7444
0.0	93.0	16554	2.4082	0.7781
0.0	94.0	16732	2.4077	0.7753
0.0	95.0	16910	2.4185	0.7781
0.0	96.0	17088	2.6096	0.7528
0.0	97.0	17266	2.5907	0.7669
0.0	98.0	17444	2.6030	0.7556
0.0	99.0	17622	2.6081	0.7528
0.0	100.0	17800	2.6081	0.7528

Framework versions

Transformers 4.27.3
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

MihaiIonascu/fine_tuned_bert_dreadit

fine_tuned_bert_dreadit

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MihaiIonascu/
fine_tuned_bert_dreadit