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apwic/
nerui-lora-r8-4

Name: nerui-lora-r8-4
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-lora-r8-4

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:

Loss: 0.0437
Location Precision: 0.8739
Location Recall: 0.9417
Location F1: 0.9065
Location Number: 103
Organization Precision: 0.9152
Organization Recall: 0.8830
Organization F1: 0.8988
Organization Number: 171
Person Precision: 0.9695
Person Recall: 0.9695
Person F1: 0.9695
Person Number: 131
Overall Precision: 0.9214
Overall Recall: 0.9259
Overall F1: 0.9236
Overall Accuracy: 0.9870

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: 5e-05
train_batch_size: 16
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100.0

Training results

Training Loss	Epoch	Step	Validation Loss	Location Precision	Location Recall	Location F1	Location Number	Organization Precision	Organization Recall	Organization F1	Organization Number	Person Precision	Person Recall	Person F1	Person Number	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.1566	1.0	96	0.6952	0.0	0.0	0.0	103	0.0	0.0	0.0	171	0.0	0.0	0.0	131	0.0	0.0	0.0	0.8373
0.6676	2.0	192	0.5653	0.0	0.0	0.0	103	0.0	0.0	0.0	171	0.0	0.0	0.0	131	0.0	0.0	0.0	0.8376
0.5559	3.0	288	0.4487	0.0	0.0	0.0	103	0.375	0.0351	0.0642	171	0.2188	0.0534	0.0859	131	0.26	0.0321	0.0571	0.8456
0.4455	4.0	384	0.3389	0.2083	0.0485	0.0787	103	0.3509	0.2339	0.2807	171	0.3816	0.4427	0.4099	131	0.3552	0.2543	0.2964	0.8818
0.3416	5.0	480	0.2583	0.3971	0.2621	0.3158	103	0.4923	0.5614	0.5246	171	0.5176	0.6718	0.5847	131	0.4873	0.5210	0.5036	0.9207
0.2637	6.0	576	0.2006	0.6316	0.5825	0.6061	103	0.6490	0.7895	0.7124	171	0.7124	0.8321	0.7676	131	0.6667	0.7506	0.7062	0.9489
0.2115	7.0	672	0.1649	0.7273	0.6990	0.7129	103	0.6946	0.8246	0.7540	171	0.8542	0.9389	0.8945	131	0.7534	0.8296	0.7897	0.9586
0.1785	8.0	768	0.1343	0.8316	0.7670	0.7980	103	0.7461	0.8421	0.7912	171	0.9	0.9618	0.9299	131	0.8154	0.8617	0.8379	0.9652
0.1541	9.0	864	0.1175	0.8384	0.8058	0.8218	103	0.7737	0.8596	0.8144	171	0.8936	0.9618	0.9265	131	0.8279	0.8790	0.8527	0.9682
0.1387	10.0	960	0.1095	0.8235	0.8155	0.8195	103	0.7853	0.8772	0.8287	171	0.8944	0.9695	0.9304	131	0.8299	0.8914	0.8595	0.9696
0.1275	11.0	1056	0.0995	0.85	0.8252	0.8374	103	0.7937	0.8772	0.8333	171	0.9	0.9618	0.9299	131	0.8415	0.8914	0.8657	0.9710
0.1212	12.0	1152	0.0935	0.8641	0.8641	0.8641	103	0.7917	0.8889	0.8375	171	0.9	0.9618	0.9299	131	0.8437	0.9062	0.8738	0.9724
0.1164	13.0	1248	0.0875	0.8627	0.8544	0.8585	103	0.8010	0.8947	0.8453	171	0.9	0.9618	0.9299	131	0.8476	0.9062	0.8759	0.9724
0.1105	14.0	1344	0.0820	0.8922	0.8835	0.8878	103	0.8466	0.8713	0.8588	171	0.9265	0.9618	0.9438	131	0.8841	0.9037	0.8938	0.9768
0.1063	15.0	1440	0.0793	0.9175	0.8641	0.89	103	0.7908	0.9064	0.8447	171	0.9065	0.9618	0.9333	131	0.8565	0.9136	0.8841	0.9751
0.1018	16.0	1536	0.0783	0.8762	0.8932	0.8846	103	0.8010	0.9181	0.8556	171	0.9065	0.9618	0.9333	131	0.8523	0.9259	0.8876	0.9749
0.0986	17.0	1632	0.0725	0.9109	0.8932	0.9020	103	0.8280	0.9006	0.8627	171	0.9407	0.9695	0.9549	131	0.8839	0.9210	0.9021	0.9779
0.093	18.0	1728	0.0693	0.9010	0.8835	0.8922	103	0.8432	0.9123	0.8764	171	0.9333	0.9618	0.9474	131	0.8860	0.9210	0.9031	0.9779
0.0897	19.0	1824	0.0699	0.8762	0.8932	0.8846	103	0.8470	0.9064	0.8757	171	0.9270	0.9695	0.9478	131	0.88	0.9235	0.9012	0.9782
0.0876	20.0	1920	0.0679	0.8846	0.8932	0.8889	103	0.8201	0.9064	0.8611	171	0.9065	0.9618	0.9333	131	0.8634	0.9210	0.8913	0.9765
0.0846	21.0	2016	0.0654	0.8679	0.8932	0.8804	103	0.8378	0.9064	0.8708	171	0.9197	0.9618	0.9403	131	0.8715	0.9210	0.8956	0.9785
0.0843	22.0	2112	0.0664	0.8932	0.8932	0.8932	103	0.8325	0.9298	0.8785	171	0.9197	0.9618	0.9403	131	0.8747	0.9309	0.9019	0.9787
0.0823	23.0	2208	0.0611	0.8679	0.8932	0.8804	103	0.8492	0.8889	0.8686	171	0.9621	0.9695	0.9658	131	0.8897	0.9160	0.9027	0.9801
0.0808	24.0	2304	0.0627	0.8505	0.8835	0.8667	103	0.8415	0.9006	0.8701	171	0.9549	0.9695	0.9621	131	0.8794	0.9185	0.8986	0.9798
0.0809	25.0	2400	0.0598	0.875	0.8835	0.8792	103	0.8424	0.9064	0.8732	171	0.9474	0.9618	0.9545	131	0.8836	0.9185	0.9007	0.9807
0.078	26.0	2496	0.0581	0.9010	0.8835	0.8922	103	0.8441	0.9181	0.8796	171	0.9403	0.9618	0.9509	131	0.8884	0.9235	0.9056	0.9818
0.0774	27.0	2592	0.0582	0.92	0.8932	0.9064	103	0.8503	0.9298	0.8883	171	0.9403	0.9618	0.9509	131	0.8955	0.9309	0.9128	0.9812
0.0732	28.0	2688	0.0623	0.9020	0.8932	0.8976	103	0.8659	0.9064	0.8857	171	0.9474	0.9618	0.9545	131	0.9010	0.9210	0.9109	0.9815
0.0746	29.0	2784	0.0553	0.9109	0.8932	0.9020	103	0.8827	0.9240	0.9029	171	0.9621	0.9695	0.9658	131	0.9150	0.9309	0.9229	0.9829
0.0695	30.0	2880	0.0536	0.9109	0.8932	0.9020	103	0.8827	0.9240	0.9029	171	0.9474	0.9618	0.9545	131	0.9104	0.9284	0.9193	0.9832
0.0691	31.0	2976	0.0533	0.8762	0.8932	0.8846	103	0.8807	0.9064	0.8934	171	0.9474	0.9618	0.9545	131	0.9010	0.9210	0.9109	0.9826
0.0665	32.0	3072	0.0518	0.8835	0.8835	0.8835	103	0.8652	0.9006	0.8825	171	0.9474	0.9618	0.9545	131	0.8961	0.9160	0.9060	0.9823
0.0649	33.0	3168	0.0527	0.8288	0.8932	0.8598	103	0.9018	0.8596	0.8802	171	0.9474	0.9618	0.9545	131	0.8968	0.9012	0.8990	0.9809
0.0645	34.0	3264	0.0506	0.8835	0.8835	0.8835	103	0.8966	0.9123	0.9043	171	0.9474	0.9618	0.9545	131	0.9098	0.9210	0.9153	0.9843
0.063	35.0	3360	0.0515	0.8505	0.8835	0.8667	103	0.8889	0.8889	0.8889	171	0.9474	0.9618	0.9545	131	0.8978	0.9111	0.9044	0.9826
0.0637	36.0	3456	0.0508	0.8505	0.8835	0.8667	103	0.8830	0.8830	0.8830	171	0.9474	0.9618	0.9545	131	0.8954	0.9086	0.9020	0.9818
0.0614	37.0	3552	0.0495	0.9010	0.8835	0.8922	103	0.8729	0.9240	0.8977	171	0.9474	0.9618	0.9545	131	0.9036	0.9259	0.9146	0.9829
0.0599	38.0	3648	0.0495	0.8585	0.8835	0.8708	103	0.8982	0.8772	0.8876	171	0.9474	0.9618	0.9545	131	0.9039	0.9062	0.9051	0.9820
0.06	39.0	3744	0.0495	0.8519	0.8932	0.8720	103	0.8728	0.8830	0.8779	171	0.9695	0.9695	0.9695	131	0.8981	0.9136	0.9058	0.9820
0.0576	40.0	3840	0.0480	0.8667	0.8835	0.8750	103	0.8994	0.8889	0.8941	171	0.9621	0.9695	0.9658	131	0.9113	0.9136	0.9125	0.9837
0.0597	41.0	3936	0.0485	0.8679	0.8932	0.8804	103	0.875	0.9006	0.8876	171	0.9621	0.9695	0.9658	131	0.9010	0.9210	0.9109	0.9829
0.0581	42.0	4032	0.0473	0.8598	0.8932	0.8762	103	0.8736	0.8889	0.8812	171	0.9621	0.9695	0.9658	131	0.8983	0.9160	0.9071	0.9829
0.0597	43.0	4128	0.0479	0.8679	0.8932	0.8804	103	0.8736	0.8889	0.8812	171	0.9695	0.9695	0.9695	131	0.9027	0.9160	0.9093	0.9826
0.0568	44.0	4224	0.0481	0.8519	0.8932	0.8720	103	0.8982	0.8772	0.8876	171	0.9621	0.9695	0.9658	131	0.9066	0.9111	0.9089	0.9826
0.0561	45.0	4320	0.0470	0.8519	0.8932	0.8720	103	0.8876	0.8772	0.8824	171	0.9695	0.9695	0.9695	131	0.9044	0.9111	0.9077	0.9834
0.0552	46.0	4416	0.0478	0.8519	0.8932	0.8720	103	0.9036	0.8772	0.8902	171	0.9695	0.9695	0.9695	131	0.9111	0.9111	0.9111	0.9837
0.0562	47.0	4512	0.0461	0.8762	0.8932	0.8846	103	0.8644	0.8947	0.8793	171	0.9695	0.9695	0.9695	131	0.9007	0.9185	0.9095	0.9840
0.0533	48.0	4608	0.0474	0.8545	0.9126	0.8826	103	0.9085	0.8713	0.8896	171	0.9695	0.9695	0.9695	131	0.9136	0.9136	0.9136	0.9837
0.0522	49.0	4704	0.0461	0.8468	0.9126	0.8785	103	0.8772	0.8772	0.8772	171	0.9695	0.9695	0.9695	131	0.8983	0.9160	0.9071	0.9843
0.052	50.0	4800	0.0464	0.8559	0.9223	0.8879	103	0.8793	0.8947	0.8870	171	0.9695	0.9695	0.9695	131	0.9014	0.9259	0.9135	0.9840
0.054	51.0	4896	0.0467	0.8571	0.9320	0.8930	103	0.9030	0.8713	0.8869	171	0.9695	0.9695	0.9695	131	0.9118	0.9185	0.9151	0.9854
0.0525	52.0	4992	0.0460	0.8829	0.9515	0.9159	103	0.8671	0.8772	0.8721	171	0.9695	0.9695	0.9695	131	0.9036	0.9259	0.9146	0.9840
0.0501	53.0	5088	0.0466	0.8739	0.9417	0.9065	103	0.8922	0.8713	0.8817	171	0.9695	0.9695	0.9695	131	0.9120	0.9210	0.9165	0.9848
0.0498	54.0	5184	0.0449	0.8716	0.9223	0.8962	103	0.8779	0.8830	0.8805	171	0.9695	0.9695	0.9695	131	0.9053	0.9210	0.9131	0.9840
0.0504	55.0	5280	0.0456	0.8739	0.9417	0.9065	103	0.8982	0.8772	0.8876	171	0.9695	0.9695	0.9695	131	0.9144	0.9235	0.9189	0.9854
0.0486	56.0	5376	0.0453	0.8727	0.9320	0.9014	103	0.8736	0.8889	0.8812	171	0.9695	0.9695	0.9695	131	0.9036	0.9259	0.9146	0.9845
0.0497	57.0	5472	0.0457	0.8509	0.9417	0.8940	103	0.8982	0.8772	0.8876	171	0.9695	0.9695	0.9695	131	0.9078	0.9235	0.9155	0.9845
0.0487	58.0	5568	0.0460	0.8739	0.9417	0.9065	103	0.9036	0.8772	0.8902	171	0.9695	0.9695	0.9695	131	0.9167	0.9235	0.9200	0.9854
0.0473	59.0	5664	0.0456	0.8559	0.9223	0.8879	103	0.8922	0.8713	0.8817	171	0.9695	0.9695	0.9695	131	0.9071	0.9160	0.9115	0.9851
0.0463	60.0	5760	0.0454	0.8559	0.9223	0.8879	103	0.8970	0.8655	0.8810	171	0.9695	0.9695	0.9695	131	0.9091	0.9136	0.9113	0.9854
0.0486	61.0	5856	0.0456	0.8739	0.9417	0.9065	103	0.8970	0.8655	0.8810	171	0.9695	0.9695	0.9695	131	0.9140	0.9185	0.9163	0.9856
0.0484	62.0	5952	0.0465	0.8571	0.9320	0.8930	103	0.8970	0.8655	0.8810	171	0.9695	0.9695	0.9695	131	0.9093	0.9160	0.9127	0.9851
0.0461	63.0	6048	0.0451	0.875	0.9515	0.9116	103	0.8988	0.8830	0.8909	171	0.9695	0.9695	0.9695	131	0.9148	0.9284	0.9216	0.9856
0.0455	64.0	6144	0.0451	0.8727	0.9320	0.9014	103	0.8976	0.8713	0.8843	171	0.9695	0.9695	0.9695	131	0.9140	0.9185	0.9163	0.9854
0.0472	65.0	6240	0.0453	0.8739	0.9417	0.9065	103	0.9030	0.8713	0.8869	171	0.9695	0.9695	0.9695	131	0.9165	0.9210	0.9187	0.9859
0.0453	66.0	6336	0.0451	0.8739	0.9417	0.9065	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9865
0.045	67.0	6432	0.0450	0.8739	0.9417	0.9065	103	0.9085	0.8713	0.8896	171	0.9695	0.9695	0.9695	131	0.9187	0.9210	0.9199	0.9856
0.0466	68.0	6528	0.0440	0.8909	0.9515	0.9202	103	0.9036	0.8772	0.8902	171	0.9695	0.9695	0.9695	131	0.9214	0.9259	0.9236	0.9867
0.046	69.0	6624	0.0446	0.8649	0.9320	0.8972	103	0.9085	0.8713	0.8896	171	0.9695	0.9695	0.9695	131	0.9163	0.9185	0.9174	0.9854
0.0436	70.0	6720	0.0440	0.8649	0.9320	0.8972	103	0.9091	0.8772	0.8929	171	0.9695	0.9695	0.9695	131	0.9165	0.9210	0.9187	0.9862
0.0445	71.0	6816	0.0446	0.8673	0.9515	0.9074	103	0.8988	0.8830	0.8909	171	0.9695	0.9695	0.9695	131	0.9126	0.9284	0.9204	0.9862
0.0437	72.0	6912	0.0459	0.8727	0.9320	0.9014	103	0.9024	0.8655	0.8836	171	0.9695	0.9695	0.9695	131	0.9160	0.9160	0.9160	0.9854
0.0434	73.0	7008	0.0444	0.875	0.9515	0.9116	103	0.9091	0.8772	0.8929	171	0.9695	0.9695	0.9695	131	0.9191	0.9259	0.9225	0.9862
0.0441	74.0	7104	0.0445	0.8739	0.9417	0.9065	103	0.9091	0.8772	0.8929	171	0.9695	0.9695	0.9695	131	0.9189	0.9235	0.9212	0.9862
0.0439	75.0	7200	0.0446	0.8739	0.9417	0.9065	103	0.9091	0.8772	0.8929	171	0.9695	0.9695	0.9695	131	0.9189	0.9235	0.9212	0.9862
0.042	76.0	7296	0.0447	0.8661	0.9417	0.9023	103	0.8982	0.8772	0.8876	171	0.9695	0.9695	0.9695	131	0.9122	0.9235	0.9178	0.9859
0.0428	77.0	7392	0.0449	0.8649	0.9320	0.8972	103	0.9085	0.8713	0.8896	171	0.9695	0.9695	0.9695	131	0.9163	0.9185	0.9174	0.9865
0.0435	78.0	7488	0.0444	0.8739	0.9417	0.9065	103	0.9091	0.8772	0.8929	171	0.9695	0.9695	0.9695	131	0.9189	0.9235	0.9212	0.9867
0.0416	79.0	7584	0.0439	0.8661	0.9417	0.9023	103	0.9102	0.8889	0.8994	171	0.9695	0.9695	0.9695	131	0.9171	0.9284	0.9227	0.9862
0.0414	80.0	7680	0.0436	0.8727	0.9320	0.9014	103	0.9096	0.8830	0.8961	171	0.9695	0.9695	0.9695	131	0.9189	0.9235	0.9212	0.9867
0.043	81.0	7776	0.0437	0.8727	0.9320	0.9014	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9870
0.0433	82.0	7872	0.0434	0.8818	0.9417	0.9108	103	0.9157	0.8889	0.9021	171	0.9695	0.9695	0.9695	131	0.9238	0.9284	0.9261	0.9873
0.0428	83.0	7968	0.0439	0.8661	0.9417	0.9023	103	0.9212	0.8889	0.9048	171	0.9695	0.9695	0.9695	131	0.9216	0.9284	0.9250	0.9867
0.0418	84.0	8064	0.0435	0.8739	0.9417	0.9065	103	0.9157	0.8889	0.9021	171	0.9695	0.9695	0.9695	131	0.9216	0.9284	0.9250	0.9867
0.0416	85.0	8160	0.0435	0.8727	0.9320	0.9014	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9870
0.0413	86.0	8256	0.0439	0.8739	0.9417	0.9065	103	0.9212	0.8889	0.9048	171	0.9695	0.9695	0.9695	131	0.9238	0.9284	0.9261	0.9873
0.0423	87.0	8352	0.0440	0.8727	0.9320	0.9014	103	0.9085	0.8713	0.8896	171	0.9695	0.9695	0.9695	131	0.9185	0.9185	0.9185	0.9865
0.0409	88.0	8448	0.0439	0.8818	0.9417	0.9108	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9235	0.9235	0.9235	0.9870
0.0419	89.0	8544	0.0437	0.8661	0.9417	0.9023	103	0.9212	0.8889	0.9048	171	0.9695	0.9695	0.9695	131	0.9216	0.9284	0.9250	0.9870
0.0424	90.0	8640	0.0438	0.8661	0.9417	0.9023	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9191	0.9259	0.9225	0.9867
0.0419	91.0	8736	0.0439	0.8739	0.9417	0.9065	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9867
0.0427	92.0	8832	0.0443	0.8739	0.9417	0.9065	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9867
0.0397	93.0	8928	0.0438	0.8739	0.9417	0.9065	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9212	0.9235	0.9223	0.9867
0.0414	94.0	9024	0.0437	0.8739	0.9417	0.9065	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9214	0.9259	0.9236	0.9870
0.0401	95.0	9120	0.0438	0.8818	0.9417	0.9108	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9236	0.9259	0.9248	0.9873
0.0415	96.0	9216	0.0439	0.8727	0.9320	0.9014	103	0.9146	0.8772	0.8955	171	0.9695	0.9695	0.9695	131	0.9210	0.9210	0.9210	0.9867
0.0404	97.0	9312	0.0437	0.8818	0.9417	0.9108	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9236	0.9259	0.9248	0.9873
0.0418	98.0	9408	0.0438	0.8739	0.9417	0.9065	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9214	0.9259	0.9236	0.9870
0.0388	99.0	9504	0.0437	0.8739	0.9417	0.9065	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9214	0.9259	0.9236	0.9870
0.0397	100.0	9600	0.0437	0.8739	0.9417	0.9065	103	0.9152	0.8830	0.8988	171	0.9695	0.9695	0.9695	131	0.9214	0.9259	0.9236	0.9870

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-lora-r8-4

nerui-lora-r8-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-lora-r8-4