CodeGenDetect-BERT_Classifier

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Model description

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The following hyperparameters were used during training:

Training Loss	Epoch	Step	Accuracy	F1	Validation Loss	Precision	Recall
0.1206	0.096	3000	0.9503	0.9503	0.1452	0.9515	0.9503
0.1659	0.192	6000	0.9580	0.9581	0.1326	0.9584	0.9580
0.1468	0.288	9000	0.9631	0.9632	0.1131	0.9634	0.9631
0.072	0.384	12000	0.9645	0.9645	0.1199	0.9651	0.9645
0.1184	0.48	15000	0.9656	0.9656	0.1093	0.9661	0.9656
0.2584	0.576	18000	0.9681	0.9681	0.0996	0.9684	0.9681
0.1833	0.672	21000	0.9624	0.9624	0.1154	0.9624	0.9624
0.0551	0.768	24000	0.9694	0.9694	0.1059	0.9700	0.9694
0.1545	0.864	27000	0.9705	0.9705	0.0960	0.9710	0.9705
0.1006	0.96	30000	0.9733	0.9733	0.0884	0.9735	0.9733
0.0941	1.056	33000	0.9696	0.9696	0.1021	0.9704	0.9696
0.1786	1.152	36000	0.9727	0.9727	0.0988	0.9728	0.9727
0.0231	1.248	39000	0.9740	0.9740	0.0923	0.9741	0.9740
0.0131	1.3440	42000	0.9735	0.9735	0.0924	0.9739	0.9735
0.1303	1.44	45000	0.9742	0.9742	0.0959	0.9742	0.9742
0.0637	1.536	48000	0.9753	0.9753	0.0877	0.9754	0.9753
0.1373	1.6320	51000	0.9741	0.9742	0.0977	0.9745	0.9741
0.1152	1.728	54000	0.9755	0.9755	0.1035	0.9756	0.9755
0.0728	1.8240	57000	0.9751	0.9752	0.0922	0.9754	0.9751
0.007	1.92	60000	0.9763	0.9763	0.0814	0.9764	0.9763
0.0043	2.016	63000	0.9768	0.9768	0.0991	0.9769	0.9768
0.0429	2.112	66000	0.9759	0.9759	0.0925	0.9760	0.9759
0.0061	2.208	69000	0.9765	0.9765	0.0930	0.9766	0.9765
0.0774	2.304	72000	0.9761	0.9761	0.0868	0.9763	0.9761
0.0166	2.4	75000	0.0927	0.9775	0.9775	0.9777	0.9775
0.0035	2.496	78000	0.0859	0.9777	0.9777	0.9779	0.9777
0.0891	2.592	81000	0.0898	0.9752	0.9752	0.9752	0.9752
0.093	2.6880	84000	0.0848	0.9777	0.9777	0.9779	0.9777
0.0056	2.784	87000	0.0933	0.9770	0.9770	0.9771	0.9770
0.124	2.88	90000	0.1115	0.9774	0.9774	0.9775	0.9774
0.0861	2.976	93000	0.0975	0.9767	0.9767	0.9768	0.9767