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apwic/
nerui-lora-r8-3

Name: nerui-lora-r8-3
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-lora-r8-3

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: 0.0484
Location Precision: 0.9
Location Recall: 0.9419
Location F1: 0.9205
Location Number: 86
Organization Precision: 0.9364
Organization Recall: 0.9101
Organization F1: 0.9231
Organization Number: 178
Person Precision: 0.9843
Person Recall: 0.9766
Person F1: 0.9804
Person Number: 128
Overall Precision: 0.9436
Overall Recall: 0.9388
Overall F1: 0.9412
Overall Accuracy: 0.9846

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
1.1489	1.0	96	0.6808	0.0	0.0	0.0	86	0.0	0.0	0.0	178	0.0	0.0	0.0	128	0.0	0.0	0.0	0.8435
0.6648	2.0	192	0.5508	0.0	0.0	0.0	86	0.5	0.0056	0.0111	178	0.0	0.0	0.0	128	0.3333	0.0026	0.0051	0.8437
0.5545	3.0	288	0.4324	0.0	0.0	0.0	86	0.3793	0.0618	0.1063	178	0.3714	0.1016	0.1595	128	0.3636	0.0612	0.1048	0.8543
0.4347	4.0	384	0.3185	0.3077	0.0465	0.0808	86	0.3876	0.2809	0.3257	178	0.4167	0.5078	0.4577	128	0.3993	0.3036	0.3449	0.8910
0.3178	5.0	480	0.2349	0.5714	0.3721	0.4507	86	0.5476	0.6461	0.5928	178	0.5890	0.75	0.6598	128	0.5664	0.6199	0.5920	0.9320
0.2406	6.0	576	0.1835	0.7407	0.6977	0.7186	86	0.6716	0.7584	0.7124	178	0.7467	0.875	0.8058	128	0.7106	0.7832	0.7451	0.9536
0.1942	7.0	672	0.1519	0.7701	0.7791	0.7746	86	0.7114	0.8034	0.7546	178	0.8786	0.9609	0.9179	128	0.7780	0.8495	0.8122	0.9625
0.1647	8.0	768	0.1279	0.7882	0.7791	0.7836	86	0.7487	0.8034	0.7751	178	0.8986	0.9688	0.9323	128	0.8068	0.8520	0.8288	0.9660
0.1479	9.0	864	0.1130	0.7978	0.8256	0.8114	86	0.7602	0.8371	0.7968	178	0.9118	0.9688	0.9394	128	0.8171	0.8776	0.8462	0.9690
0.135	10.0	960	0.1037	0.7660	0.8372	0.8	86	0.7755	0.8539	0.8128	178	0.9179	0.9609	0.9389	128	0.8184	0.8852	0.8505	0.9682
0.1317	11.0	1056	0.0951	0.7935	0.8488	0.8202	86	0.8182	0.8596	0.8384	178	0.9466	0.9688	0.9575	128	0.8537	0.8929	0.8728	0.9733
0.1196	12.0	1152	0.0904	0.7708	0.8605	0.8132	86	0.8404	0.8876	0.8634	178	0.9328	0.9766	0.9542	128	0.8541	0.9107	0.8815	0.9749
0.1108	13.0	1248	0.0824	0.7979	0.8721	0.8333	86	0.8466	0.8989	0.8719	178	0.9466	0.9688	0.9575	128	0.8671	0.9158	0.8908	0.9768
0.107	14.0	1344	0.0797	0.8	0.8837	0.8398	86	0.8729	0.8876	0.8802	178	0.9394	0.9688	0.9538	128	0.8775	0.9133	0.895	0.9781
0.1063	15.0	1440	0.0760	0.7872	0.8605	0.8222	86	0.8610	0.9045	0.8822	178	0.9394	0.9688	0.9538	128	0.8692	0.9158	0.8919	0.9776
0.1	16.0	1536	0.0724	0.8462	0.8953	0.8701	86	0.8703	0.9045	0.8871	178	0.9538	0.9688	0.9612	128	0.8916	0.9235	0.9073	0.9795
0.095	17.0	1632	0.0705	0.8261	0.8837	0.8539	86	0.8710	0.9101	0.8901	178	0.9466	0.9688	0.9575	128	0.8851	0.9235	0.9039	0.9789
0.0932	18.0	1728	0.0698	0.8370	0.8953	0.8652	86	0.8944	0.9045	0.8994	178	0.9466	0.9688	0.9575	128	0.8983	0.9235	0.9107	0.9803
0.0871	19.0	1824	0.0672	0.8387	0.9070	0.8715	86	0.8944	0.9045	0.8994	178	0.9466	0.9688	0.9575	128	0.8985	0.9260	0.9121	0.9800
0.0883	20.0	1920	0.0650	0.8298	0.9070	0.8667	86	0.8944	0.9045	0.8994	178	0.9612	0.9688	0.9650	128	0.9007	0.9260	0.9132	0.9803
0.0832	21.0	2016	0.0651	0.8298	0.9070	0.8667	86	0.8994	0.9045	0.9020	178	0.9612	0.9688	0.9650	128	0.9030	0.9260	0.9144	0.9811
0.0829	22.0	2112	0.0645	0.8125	0.9070	0.8571	86	0.8663	0.9101	0.8877	178	0.9466	0.9688	0.9575	128	0.8792	0.9286	0.9032	0.9787
0.0789	23.0	2208	0.0601	0.8211	0.9070	0.8619	86	0.8994	0.9045	0.9020	178	0.9766	0.9766	0.9766	128	0.9055	0.9286	0.9169	0.9819
0.078	24.0	2304	0.0612	0.8211	0.9070	0.8619	86	0.8927	0.8876	0.8901	178	0.9766	0.9766	0.9766	128	0.9025	0.9209	0.9116	0.9806
0.0756	25.0	2400	0.0594	0.8298	0.9070	0.8667	86	0.9045	0.9045	0.9045	178	0.9615	0.9766	0.9690	128	0.9055	0.9286	0.9169	0.9806
0.0767	26.0	2496	0.0588	0.7822	0.9186	0.8449	86	0.8960	0.8708	0.8832	178	0.9766	0.9766	0.9766	128	0.8930	0.9158	0.9043	0.9800
0.0721	27.0	2592	0.0561	0.8125	0.9070	0.8571	86	0.8852	0.9101	0.8975	178	0.9766	0.9766	0.9766	128	0.8968	0.9311	0.9136	0.9814
0.0719	28.0	2688	0.0559	0.8404	0.9186	0.8778	86	0.9040	0.8989	0.9014	178	0.9766	0.9766	0.9766	128	0.9123	0.9286	0.9204	0.9819
0.0702	29.0	2784	0.0543	0.8478	0.9070	0.8764	86	0.9016	0.9270	0.9141	178	0.9766	0.9766	0.9766	128	0.9132	0.9388	0.9258	0.9816
0.0711	30.0	2880	0.0539	0.8667	0.9070	0.8864	86	0.9066	0.9270	0.9167	178	0.9690	0.9766	0.9728	128	0.9177	0.9388	0.9281	0.9819
0.067	31.0	2976	0.0576	0.8061	0.9186	0.8587	86	0.9101	0.9101	0.9101	178	0.9766	0.9766	0.9766	128	0.9059	0.9337	0.9196	0.9819
0.0664	32.0	3072	0.0567	0.8211	0.9070	0.8619	86	0.9011	0.9213	0.9111	178	0.9690	0.9766	0.9728	128	0.9039	0.9362	0.9198	0.9814
0.0642	33.0	3168	0.0558	0.8316	0.9186	0.8729	86	0.9096	0.9045	0.9070	178	0.9766	0.9766	0.9766	128	0.9125	0.9311	0.9217	0.9825
0.0642	34.0	3264	0.0545	0.8587	0.9186	0.8876	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9221	0.9362	0.9291	0.9835
0.0624	35.0	3360	0.0542	0.8681	0.9186	0.8927	86	0.9111	0.9213	0.9162	178	0.9766	0.9766	0.9766	128	0.9223	0.9388	0.9305	0.9830
0.0651	36.0	3456	0.0535	0.8778	0.9186	0.8977	86	0.9213	0.9213	0.9213	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9833
0.0635	37.0	3552	0.0523	0.8864	0.9070	0.8966	86	0.9111	0.9213	0.9162	178	0.9766	0.9766	0.9766	128	0.9268	0.9362	0.9315	0.9833
0.0617	38.0	3648	0.0528	0.8587	0.9186	0.8876	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9221	0.9362	0.9291	0.9838
0.0581	39.0	3744	0.0548	0.8061	0.9186	0.8587	86	0.9091	0.8989	0.9040	178	0.9766	0.9766	0.9766	128	0.9055	0.9286	0.9169	0.9827
0.0597	40.0	3840	0.0510	0.8778	0.9186	0.8977	86	0.9270	0.9270	0.9270	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9846
0.0569	41.0	3936	0.0505	0.8778	0.9186	0.8977	86	0.9270	0.9270	0.9270	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9849
0.0579	42.0	4032	0.0504	0.8778	0.9186	0.8977	86	0.9270	0.9270	0.9270	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9843
0.0564	43.0	4128	0.0506	0.8681	0.9186	0.8927	86	0.9106	0.9157	0.9132	178	0.9843	0.9766	0.9804	128	0.9244	0.9362	0.9303	0.9843
0.0572	44.0	4224	0.0499	0.8681	0.9186	0.8927	86	0.9116	0.9270	0.9192	178	0.9843	0.9766	0.9804	128	0.9248	0.9413	0.9330	0.9849
0.0563	45.0	4320	0.0488	0.8681	0.9186	0.8927	86	0.9213	0.9213	0.9213	178	0.9843	0.9766	0.9804	128	0.9293	0.9388	0.9340	0.9843
0.0594	46.0	4416	0.0507	0.8681	0.9186	0.8927	86	0.9167	0.9270	0.9218	178	0.9766	0.9766	0.9766	128	0.9248	0.9413	0.9330	0.9841
0.0545	47.0	4512	0.0497	0.8681	0.9186	0.8927	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9246	0.9388	0.9316	0.9846
0.0536	48.0	4608	0.0487	0.8681	0.9186	0.8927	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9246	0.9388	0.9316	0.9849
0.0556	49.0	4704	0.0501	0.8681	0.9186	0.8927	86	0.9096	0.9045	0.9070	178	0.9766	0.9766	0.9766	128	0.9217	0.9311	0.9264	0.9833
0.0522	50.0	4800	0.0506	0.8791	0.9302	0.9040	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9271	0.9413	0.9342	0.9854
0.0527	51.0	4896	0.0496	0.8791	0.9302	0.9040	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9342	0.9413	0.9377	0.9852
0.0529	52.0	4992	0.0490	0.8791	0.9302	0.9040	86	0.9266	0.9213	0.9239	178	0.9688	0.9688	0.9688	128	0.9293	0.9388	0.9340	0.9852
0.0522	53.0	5088	0.0494	0.8791	0.9302	0.9040	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9270	0.9388	0.9328	0.9846
0.0525	54.0	5184	0.0482	0.8889	0.9302	0.9091	86	0.9270	0.9270	0.9270	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9860
0.0512	55.0	5280	0.0488	0.8696	0.9302	0.8989	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9854
0.053	56.0	5376	0.0487	0.8791	0.9302	0.9040	86	0.9205	0.9101	0.9153	178	0.9766	0.9766	0.9766	128	0.9291	0.9362	0.9327	0.9849
0.0498	57.0	5472	0.0486	0.8791	0.9302	0.9040	86	0.9209	0.9157	0.9183	178	0.9766	0.9766	0.9766	128	0.9293	0.9388	0.9340	0.9846
0.0504	58.0	5568	0.0489	0.8696	0.9302	0.8989	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9854
0.0456	59.0	5664	0.0492	0.8696	0.9302	0.8989	86	0.9148	0.9045	0.9096	178	0.9766	0.9766	0.9766	128	0.9242	0.9337	0.9289	0.9846
0.0504	60.0	5760	0.0475	0.8681	0.9186	0.8927	86	0.9153	0.9101	0.9127	178	0.9766	0.9766	0.9766	128	0.9242	0.9337	0.9289	0.9849
0.0494	61.0	5856	0.0476	0.8681	0.9186	0.8927	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9315	0.9362	0.9338	0.9852
0.046	62.0	5952	0.0478	0.8901	0.9419	0.9153	86	0.9318	0.9213	0.9266	178	0.9843	0.9766	0.9804	128	0.9391	0.9439	0.9415	0.9860
0.0463	63.0	6048	0.0485	0.8696	0.9302	0.8989	86	0.9162	0.9213	0.9188	178	0.9688	0.9688	0.9688	128	0.9223	0.9388	0.9305	0.9849
0.0452	64.0	6144	0.0482	0.8791	0.9302	0.9040	86	0.9213	0.9213	0.9213	178	0.9766	0.9766	0.9766	128	0.9295	0.9413	0.9354	0.9852
0.0446	65.0	6240	0.0492	0.8791	0.9302	0.9040	86	0.9111	0.9213	0.9162	178	0.9843	0.9766	0.9804	128	0.9271	0.9413	0.9342	0.9854
0.0463	66.0	6336	0.0495	0.8587	0.9186	0.8876	86	0.9101	0.9101	0.9101	178	0.9766	0.9766	0.9766	128	0.9196	0.9337	0.9266	0.9843
0.0466	67.0	6432	0.0491	0.8791	0.9302	0.9040	86	0.9101	0.9101	0.9101	178	0.9766	0.9766	0.9766	128	0.9244	0.9362	0.9303	0.9846
0.0451	68.0	6528	0.0499	0.8791	0.9302	0.9040	86	0.9111	0.9213	0.9162	178	0.9766	0.9766	0.9766	128	0.9248	0.9413	0.9330	0.9852
0.047	69.0	6624	0.0493	0.8696	0.9302	0.8989	86	0.9209	0.9157	0.9183	178	0.9766	0.9766	0.9766	128	0.9270	0.9388	0.9328	0.9852
0.0435	70.0	6720	0.0485	0.8791	0.9302	0.9040	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9270	0.9388	0.9328	0.9849
0.045	71.0	6816	0.0490	0.8791	0.9302	0.9040	86	0.9111	0.9213	0.9162	178	0.9766	0.9766	0.9766	128	0.9248	0.9413	0.9330	0.9852
0.0458	72.0	6912	0.0497	0.8901	0.9419	0.9153	86	0.9257	0.9101	0.9178	178	0.9766	0.9766	0.9766	128	0.9340	0.9388	0.9364	0.9849
0.0442	73.0	7008	0.0495	0.8901	0.9419	0.9153	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9295	0.9413	0.9354	0.9854
0.0442	74.0	7104	0.0490	0.8901	0.9419	0.9153	86	0.9153	0.9101	0.9127	178	0.9766	0.9766	0.9766	128	0.9293	0.9388	0.9340	0.9852
0.0437	75.0	7200	0.0487	0.8681	0.9186	0.8927	86	0.9209	0.9157	0.9183	178	0.9766	0.9766	0.9766	128	0.9268	0.9362	0.9315	0.9841
0.0458	76.0	7296	0.0493	0.8791	0.9302	0.9040	86	0.9209	0.9157	0.9183	178	0.9843	0.9766	0.9804	128	0.9316	0.9388	0.9352	0.9843
0.0448	77.0	7392	0.0487	0.8681	0.9186	0.8927	86	0.9153	0.9101	0.9127	178	0.9843	0.9766	0.9804	128	0.9266	0.9337	0.9301	0.9838
0.0451	78.0	7488	0.0495	0.8791	0.9302	0.9040	86	0.9209	0.9157	0.9183	178	0.9766	0.9766	0.9766	128	0.9293	0.9388	0.9340	0.9843
0.0449	79.0	7584	0.0498	0.8791	0.9302	0.9040	86	0.9213	0.9213	0.9213	178	0.9843	0.9766	0.9804	128	0.9318	0.9413	0.9365	0.9846
0.0436	80.0	7680	0.0493	0.8696	0.9302	0.8989	86	0.9205	0.9101	0.9153	178	0.9843	0.9766	0.9804	128	0.9291	0.9362	0.9327	0.9843
0.044	81.0	7776	0.0494	0.8804	0.9419	0.9101	86	0.9209	0.9157	0.9183	178	0.9843	0.9766	0.9804	128	0.9318	0.9413	0.9365	0.9852
0.0438	82.0	7872	0.0485	0.8696	0.9302	0.8989	86	0.9257	0.9101	0.9178	178	0.9766	0.9766	0.9766	128	0.9291	0.9362	0.9327	0.9846
0.0434	83.0	7968	0.0482	0.8901	0.9419	0.9153	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9296	0.9439	0.9367	0.9857
0.0418	84.0	8064	0.0485	0.8696	0.9302	0.8989	86	0.9101	0.9101	0.9101	178	0.9766	0.9766	0.9766	128	0.9221	0.9362	0.9291	0.9846
0.0424	85.0	8160	0.0484	0.8901	0.9419	0.9153	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9364	0.9388	0.9376	0.9849
0.042	86.0	8256	0.0482	0.8901	0.9419	0.9153	86	0.9266	0.9213	0.9239	178	0.9843	0.9766	0.9804	128	0.9367	0.9439	0.9403	0.9857
0.0431	87.0	8352	0.0482	0.8804	0.9419	0.9101	86	0.9257	0.9101	0.9178	178	0.9843	0.9766	0.9804	128	0.9340	0.9388	0.9364	0.9852
0.0417	88.0	8448	0.0482	0.8901	0.9419	0.9153	86	0.9257	0.9101	0.9178	178	0.9843	0.9766	0.9804	128	0.9364	0.9388	0.9376	0.9849
0.0421	89.0	8544	0.0482	0.8901	0.9419	0.9153	86	0.9261	0.9157	0.9209	178	0.9843	0.9766	0.9804	128	0.9365	0.9413	0.9389	0.9854
0.0412	90.0	8640	0.0485	0.8901	0.9419	0.9153	86	0.9257	0.9101	0.9178	178	0.9843	0.9766	0.9804	128	0.9364	0.9388	0.9376	0.9852
0.0407	91.0	8736	0.0484	0.8901	0.9419	0.9153	86	0.9310	0.9101	0.9205	178	0.9843	0.9766	0.9804	128	0.9388	0.9388	0.9388	0.9849
0.0405	92.0	8832	0.0487	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0447	93.0	8928	0.0487	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0402	94.0	9024	0.0487	0.9	0.9419	0.9205	86	0.9310	0.9101	0.9205	178	0.9843	0.9766	0.9804	128	0.9412	0.9388	0.9400	0.9849
0.0406	95.0	9120	0.0485	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0413	96.0	9216	0.0485	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0404	97.0	9312	0.0484	0.9	0.9419	0.9205	86	0.9368	0.9157	0.9261	178	0.9843	0.9766	0.9804	128	0.9437	0.9413	0.9425	0.9852
0.0403	98.0	9408	0.0485	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0403	99.0	9504	0.0484	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846
0.0417	100.0	9600	0.0484	0.9	0.9419	0.9205	86	0.9364	0.9101	0.9231	178	0.9843	0.9766	0.9804	128	0.9436	0.9388	0.9412	0.9846

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-lora-r8-3

nerui-lora-r8-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-lora-r8-3