sentiment-base-1

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown 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	Validation Loss	Accuracy	Precision	Recall	F1
0.3889	1.0	122	0.4200	0.8045	0.8255	0.6867	0.7110
0.2335	2.0	244	0.3136	0.8922	0.8644	0.8863	0.8739
0.1411	3.0	366	0.3569	0.8972	0.8781	0.8723	0.8751
0.1078	4.0	488	0.3537	0.9148	0.8923	0.9072	0.8992
0.0822	5.0	610	0.5069	0.8797	0.8795	0.8224	0.8439
0.0529	6.0	732	0.4262	0.9073	0.8862	0.8919	0.8890
0.0365	7.0	854	0.5586	0.8972	0.8743	0.8798	0.8770
0.033	8.0	976	0.5012	0.8947	0.8870	0.8530	0.8675
0.0248	9.0	1098	0.5833	0.8922	0.8873	0.8462	0.8631
0.0123	10.0	1220	0.6611	0.9023	0.8858	0.8758	0.8806
0.0088	11.0	1342	0.6936	0.8947	0.8847	0.8555	0.8682
0.0074	12.0	1464	0.6790	0.9023	0.8858	0.8758	0.8806
0.0141	13.0	1586	0.6981	0.8972	0.8830	0.8648	0.8731
0.0034	14.0	1708	0.7145	0.8972	0.8781	0.8723	0.8751
0.0059	15.0	1830	0.7304	0.8997	0.8871	0.8666	0.8759
0.0056	16.0	1952	0.7518	0.8997	0.8778	0.8816	0.8797
0.0039	17.0	2074	0.7390	0.9023	0.8893	0.8708	0.8793
0.004	18.0	2196	0.7641	0.9023	0.8875	0.8733	0.8799
0.007	19.0	2318	0.7848	0.9023	0.8875	0.8733	0.8799
0.0042	20.0	2440	0.7908	0.9023	0.8875	0.8733	0.8799

加载中…