Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.97	14	4.1856	0.4305
No log	2.0	29	4.3178	0.4090
No log	2.97	43	4.0734	0.4342
No log	4.0	58	4.0470	0.4332
No log	4.97	72	4.0668	0.4270
No log	6.0	87	3.9068	0.4390
No log	6.97	101	3.8466	0.4468
No log	8.0	116	3.8330	0.4535
No log	8.97	130	3.7238	0.4516
No log	10.0	145	3.8113	0.4446
No log	10.97	159	3.6681	0.4607
No log	12.0	174	3.5627	0.4679
No log	12.97	188	3.4540	0.4794
No log	14.0	203	3.5997	0.4707
No log	14.97	217	3.4362	0.4860
No log	16.0	232	3.5471	0.4740
No log	16.97	246	3.4968	0.4803
No log	18.0	261	3.2938	0.4985
No log	18.97	275	3.4207	0.4765
No log	20.0	290	3.3869	0.4970
No log	20.97	304	3.3062	0.5012
No log	22.0	319	3.3184	0.4917
No log	22.97	333	3.2132	0.5136
No log	24.0	348	3.2027	0.5074
No log	24.97	362	3.3251	0.4923
No log	26.0	377	3.1569	0.5108
No log	26.97	391	3.0947	0.5194
No log	28.0	406	3.0470	0.5206
No log	28.97	420	3.0662	0.5182
No log	30.0	435	3.0845	0.5191
No log	30.97	449	3.0681	0.5219
No log	32.0	464	2.9902	0.5263
No log	32.97	478	2.8970	0.5448
No log	34.0	493	2.9269	0.5341
3.629	34.97	507	2.8605	0.5519
3.629	36.0	522	2.8657	0.5431
3.629	36.97	536	2.9391	0.5407
3.629	38.0	551	2.8960	0.5437
3.629	38.97	565	2.8819	0.5466
3.629	40.0	580	2.7555	0.5633
3.629	40.97	594	2.7425	0.5555
3.629	42.0	609	2.7960	0.5615
3.629	42.97	623	2.7382	0.5630
3.629	44.0	638	2.7967	0.5580
3.629	44.97	652	2.6611	0.5781
3.629	46.0	667	2.6877	0.5722
3.629	46.97	681	2.7917	0.5609
3.629	48.0	696	2.7029	0.5696
3.629	48.97	710	2.7408	0.5618
3.629	50.0	725	2.6450	0.5772
3.629	50.97	739	2.5569	0.5883
3.629	52.0	754	2.6646	0.5795
3.629	52.97	768	2.6803	0.5729
3.629	54.0	783	2.6233	0.5847
3.629	54.97	797	2.6027	0.5842
3.629	56.0	812	2.4090	0.6034
3.629	56.97	826	2.4978	0.6011
3.629	58.0	841	2.5106	0.5944
3.629	58.97	855	2.5039	0.5912
3.629	60.0	870	2.5792	0.5824
3.629	60.97	884	2.4764	0.6065
3.629	62.0	899	2.5348	0.6036
3.629	62.97	913	2.5338	0.6022
3.629	64.0	928	2.4646	0.6130
3.629	64.97	942	2.4532	0.6066
3.629	66.0	957	2.4526	0.6073
3.629	66.97	971	2.5369	0.5992
3.629	68.0	986	2.4170	0.6181
2.5556	68.97	1000	2.4493	0.6078
2.5556	70.0	1015	2.3939	0.6159
2.5556	70.97	1029	2.4793	0.6049
2.5556	72.0	1044	2.3225	0.6286
2.5556	72.97	1058	2.3551	0.6212
2.5556	74.0	1073	2.4702	0.6075
2.5556	74.97	1087	2.3489	0.6311
2.5556	76.0	1102	2.3455	0.6198
2.5556	76.97	1116	2.4500	0.6145
2.5556	78.0	1131	2.3223	0.6332
2.5556	78.97	1145	2.4375	0.6065
2.5556	80.0	1160	2.2743	0.6291
2.5556	80.97	1174	2.3255	0.6295
2.5556	82.0	1189	2.3785	0.6237
2.5556	82.97	1203	2.2722	0.6344
2.5556	84.0	1218	2.2392	0.6407
2.5556	84.97	1232	2.2322	0.6361
2.5556	86.0	1247	2.2206	0.6496
2.5556	86.97	1261	2.2419	0.6345
2.5556	88.0	1276	2.1919	0.6492
2.5556	88.97	1290	2.2616	0.6433
2.5556	90.0	1305	2.2227	0.6417
2.5556	90.97	1319	2.2847	0.6447
2.5556	92.0	1334	2.2916	0.6339
2.5556	92.97	1348	2.2684	0.6410
2.5556	94.0	1363	2.2432	0.6440
2.5556	94.97	1377	2.2510	0.6462
2.5556	96.0	1392	2.2970	0.6363
2.5556	96.55	1400	2.2197	0.6423

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

sickcell/bert-base-uncased-finetuned-github_cybersecurity_READMEs

bert-base-uncased-finetuned-github_cybersecurity_READMEs

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sickcell/
bert-base-uncased-finetuned-github_cybersecurity_READMEs