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apwic/
nerui-seq_bn-rf64-3

Name: nerui-seq_bn-rf64-3
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-seq_bn-rf64-3

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: 0.0452
Location Precision: 0.8632
Location Recall: 0.9535
Location F1: 0.9061
Location Number: 86
Organization Precision: 0.9111
Organization Recall: 0.9213
Organization F1: 0.9162
Organization Number: 178
Person Precision: 0.9688
Person Recall: 0.9688
Person F1: 0.9688
Person Number: 128
Overall Precision: 0.9181
Overall Recall: 0.9439
Overall F1: 0.9308
Overall Accuracy: 0.9852

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
1.062	1.0	96	0.6283	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
0.5934	2.0	192	0.4368	0.0	0.0	0.0	86	0.3462	0.0506	0.0882	178	0.2647	0.0703	0.1111	128	0.2951	0.0459	0.0795	0.8507
0.4331	3.0	288	0.3204	0.5667	0.1977	0.2931	86	0.3977	0.3933	0.3955	178	0.3441	0.5	0.4076	128	0.3852	0.3852	0.3852	0.8955
0.3496	4.0	384	0.2769	0.5323	0.3837	0.4459	86	0.4512	0.5449	0.4936	178	0.4080	0.6406	0.4985	128	0.4435	0.5408	0.4874	0.9193
0.303	5.0	480	0.2427	0.5806	0.4186	0.4865	86	0.4937	0.6629	0.5659	178	0.4946	0.7109	0.5833	128	0.5052	0.625	0.5587	0.9314
0.2698	6.0	576	0.2096	0.6533	0.5698	0.6087	86	0.5701	0.7079	0.6316	178	0.6131	0.8047	0.6959	128	0.5991	0.7092	0.6495	0.9439
0.2305	7.0	672	0.1785	0.6585	0.6279	0.6429	86	0.6301	0.7753	0.6952	178	0.7434	0.8828	0.8071	128	0.6733	0.7781	0.7219	0.9557
0.1941	8.0	768	0.1476	0.7711	0.7442	0.7574	86	0.7340	0.7753	0.7541	178	0.8156	0.8984	0.8550	128	0.7694	0.8087	0.7886	0.9641
0.1651	9.0	864	0.1254	0.7634	0.8256	0.7933	86	0.7475	0.8315	0.7872	178	0.8369	0.9219	0.8773	128	0.7801	0.8597	0.8180	0.9665
0.1432	10.0	960	0.1102	0.7474	0.8256	0.7845	86	0.7708	0.8315	0.8000	178	0.8897	0.9453	0.9167	128	0.8038	0.8673	0.8344	0.9690
0.1296	11.0	1056	0.1010	0.7273	0.8372	0.7784	86	0.7551	0.8315	0.7914	178	0.9118	0.9688	0.9394	128	0.7981	0.8776	0.8360	0.9703
0.1207	12.0	1152	0.0945	0.7526	0.8488	0.7978	86	0.7857	0.8652	0.8235	178	0.8921	0.9688	0.9288	128	0.8125	0.8954	0.8519	0.9714
0.11	13.0	1248	0.0859	0.7579	0.8372	0.7956	86	0.8095	0.8596	0.8338	178	0.9612	0.9688	0.9650	128	0.8450	0.8903	0.8671	0.9752
0.1041	14.0	1344	0.0841	0.7474	0.8256	0.7845	86	0.7978	0.8202	0.8089	178	0.9323	0.9688	0.9502	128	0.8297	0.8699	0.8493	0.9730
0.1023	15.0	1440	0.0778	0.7826	0.8372	0.8090	86	0.8281	0.8933	0.8595	178	0.9538	0.9688	0.9612	128	0.8575	0.9056	0.8809	0.9765
0.0924	16.0	1536	0.0765	0.7660	0.8372	0.8	86	0.8247	0.8989	0.8602	178	0.9323	0.9688	0.9502	128	0.8456	0.9082	0.8758	0.9754
0.0914	17.0	1632	0.0723	0.7935	0.8488	0.8202	86	0.8519	0.9045	0.8774	178	0.9394	0.9688	0.9538	128	0.8668	0.9133	0.8894	0.9773
0.0878	18.0	1728	0.0706	0.7849	0.8488	0.8156	86	0.8182	0.8596	0.8384	178	0.9538	0.9688	0.9612	128	0.8537	0.8929	0.8728	0.9760
0.0838	19.0	1824	0.0692	0.7789	0.8605	0.8177	86	0.8396	0.8820	0.8603	178	0.9538	0.9688	0.9612	128	0.8617	0.9056	0.8831	0.9765
0.0826	20.0	1920	0.0662	0.7979	0.8721	0.8333	86	0.8360	0.8876	0.8610	178	0.9612	0.9688	0.9650	128	0.8665	0.9107	0.8881	0.9779
0.0783	21.0	2016	0.0659	0.8085	0.8837	0.8444	86	0.8579	0.8820	0.8698	178	0.9612	0.9688	0.9650	128	0.8793	0.9107	0.8947	0.9784
0.0792	22.0	2112	0.0644	0.7979	0.8721	0.8333	86	0.8333	0.8989	0.8649	178	0.9612	0.9688	0.9650	128	0.8651	0.9158	0.8897	0.9784
0.0734	23.0	2208	0.0628	0.8105	0.8953	0.8508	86	0.8641	0.8933	0.8785	178	0.9612	0.9688	0.9650	128	0.8824	0.9184	0.9	0.9795
0.0741	24.0	2304	0.0637	0.8021	0.8953	0.8462	86	0.8396	0.8820	0.8603	178	0.9612	0.9688	0.9650	128	0.8689	0.9133	0.8905	0.9773
0.0704	25.0	2400	0.0620	0.8191	0.8953	0.8556	86	0.8449	0.8876	0.8658	178	0.9538	0.9688	0.9612	128	0.8735	0.9158	0.8941	0.9784
0.0691	26.0	2496	0.0611	0.8105	0.8953	0.8508	86	0.8478	0.8764	0.8619	178	0.9612	0.9688	0.9650	128	0.875	0.9107	0.8925	0.9784
0.0668	27.0	2592	0.0581	0.7917	0.8837	0.8352	86	0.8663	0.9101	0.8877	178	0.9612	0.9688	0.9650	128	0.8786	0.9235	0.9005	0.9798
0.0658	28.0	2688	0.0581	0.8021	0.8953	0.8462	86	0.8457	0.8933	0.8689	178	0.9612	0.9688	0.9650	128	0.8717	0.9184	0.8944	0.9795
0.0659	29.0	2784	0.0561	0.8280	0.8953	0.8603	86	0.8571	0.9101	0.8828	178	0.9612	0.9688	0.9650	128	0.8832	0.9260	0.9041	0.9814
0.0629	30.0	2880	0.0548	0.8172	0.8837	0.8492	86	0.8571	0.9101	0.8828	178	0.9612	0.9688	0.9650	128	0.8808	0.9235	0.9016	0.9814
0.0606	31.0	2976	0.0562	0.8	0.8837	0.8398	86	0.8579	0.8820	0.8698	178	0.9612	0.9688	0.9650	128	0.8771	0.9107	0.8936	0.9795
0.0596	32.0	3072	0.0550	0.7857	0.8953	0.8370	86	0.8763	0.9157	0.8956	178	0.9612	0.9688	0.9650	128	0.8814	0.9286	0.9043	0.9806
0.0614	33.0	3168	0.0551	0.8085	0.8837	0.8444	86	0.8895	0.9045	0.8969	178	0.9612	0.9688	0.9650	128	0.8936	0.9209	0.9070	0.9803
0.0585	34.0	3264	0.0532	0.8172	0.8837	0.8492	86	0.8763	0.9157	0.8956	178	0.9612	0.9688	0.9650	128	0.8897	0.9260	0.9075	0.9816
0.0568	35.0	3360	0.0541	0.8105	0.8953	0.8508	86	0.8895	0.9045	0.8969	178	0.9612	0.9688	0.9650	128	0.8938	0.9235	0.9084	0.9816
0.0576	36.0	3456	0.0546	0.7980	0.9186	0.8541	86	0.8703	0.9045	0.8871	178	0.9766	0.9766	0.9766	128	0.8859	0.9311	0.9080	0.9803
0.0554	37.0	3552	0.0523	0.8280	0.8953	0.8603	86	0.8852	0.9101	0.8975	178	0.9612	0.9688	0.9650	128	0.8963	0.9260	0.9109	0.9819
0.0551	38.0	3648	0.0538	0.8191	0.8953	0.8556	86	0.8791	0.8989	0.8889	178	0.9612	0.9688	0.9650	128	0.8914	0.9209	0.9059	0.9811
0.0514	39.0	3744	0.0527	0.8125	0.9070	0.8571	86	0.9061	0.9213	0.9136	178	0.9612	0.9688	0.9650	128	0.9015	0.9337	0.9173	0.9819
0.0536	40.0	3840	0.0524	0.8211	0.9070	0.8619	86	0.8950	0.9101	0.9025	178	0.9612	0.9688	0.9650	128	0.8988	0.9286	0.9134	0.9819
0.0514	41.0	3936	0.0512	0.8105	0.8953	0.8508	86	0.8956	0.9157	0.9056	178	0.9688	0.9688	0.9688	128	0.8988	0.9286	0.9134	0.9822
0.0503	42.0	4032	0.0490	0.8191	0.8953	0.8556	86	0.8877	0.9326	0.9096	178	0.9688	0.9688	0.9688	128	0.8973	0.9362	0.9164	0.9825
0.0512	43.0	4128	0.0497	0.8333	0.9302	0.8791	86	0.9016	0.9270	0.9141	178	0.9688	0.9688	0.9688	128	0.9066	0.9413	0.9237	0.9838
0.051	44.0	4224	0.0492	0.8421	0.9302	0.8840	86	0.8967	0.9270	0.9116	178	0.9688	0.9688	0.9688	128	0.9066	0.9413	0.9237	0.9833
0.0489	45.0	4320	0.0494	0.8191	0.8953	0.8556	86	0.8956	0.9157	0.9056	178	0.9688	0.9688	0.9688	128	0.9010	0.9286	0.9146	0.9822
0.05	46.0	4416	0.0509	0.8265	0.9419	0.8804	86	0.8962	0.9213	0.9086	178	0.9688	0.9688	0.9688	128	0.9022	0.9413	0.9213	0.9822
0.0473	47.0	4512	0.0480	0.8191	0.8953	0.8556	86	0.9071	0.9326	0.9197	178	0.9688	0.9688	0.9688	128	0.9062	0.9362	0.9210	0.9827
0.0454	48.0	4608	0.0489	0.8191	0.8953	0.8556	86	0.9116	0.9270	0.9192	178	0.9688	0.9688	0.9688	128	0.9082	0.9337	0.9208	0.9827
0.0468	49.0	4704	0.0498	0.8125	0.9070	0.8571	86	0.9101	0.9101	0.9101	178	0.9688	0.9688	0.9688	128	0.9055	0.9286	0.9169	0.9835
0.044	50.0	4800	0.0488	0.8105	0.8953	0.8508	86	0.9006	0.9157	0.9081	178	0.9688	0.9688	0.9688	128	0.9010	0.9286	0.9146	0.9838
0.0439	51.0	4896	0.0495	0.8526	0.9419	0.8950	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9136	0.9439	0.9285	0.9835
0.0451	52.0	4992	0.0490	0.8172	0.8837	0.8492	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9075	0.9260	0.9167	0.9841
0.0438	53.0	5088	0.0491	0.8351	0.9419	0.8852	86	0.9006	0.9157	0.9081	178	0.9688	0.9688	0.9688	128	0.9064	0.9388	0.9223	0.9827
0.0433	54.0	5184	0.0464	0.8387	0.9070	0.8715	86	0.9121	0.9326	0.9222	178	0.9688	0.9688	0.9688	128	0.9132	0.9388	0.9258	0.9838
0.0441	55.0	5280	0.0477	0.8333	0.9302	0.8791	86	0.8967	0.9270	0.9116	178	0.9688	0.9688	0.9688	128	0.9044	0.9413	0.9225	0.9838
0.0441	56.0	5376	0.0470	0.8333	0.9302	0.8791	86	0.8919	0.9270	0.9091	178	0.9688	0.9688	0.9688	128	0.9022	0.9413	0.9213	0.9841
0.0419	57.0	5472	0.0468	0.8454	0.9535	0.8962	86	0.8871	0.9270	0.9066	178	0.9688	0.9688	0.9688	128	0.9027	0.9464	0.9240	0.9852
0.0421	58.0	5568	0.0474	0.8316	0.9186	0.8729	86	0.9101	0.9101	0.9101	178	0.9688	0.9688	0.9688	128	0.9102	0.9311	0.9206	0.9841
0.0385	59.0	5664	0.0472	0.8298	0.9070	0.8667	86	0.9050	0.9101	0.9076	178	0.9688	0.9688	0.9688	128	0.9077	0.9286	0.9180	0.9841
0.0424	60.0	5760	0.0453	0.8298	0.9070	0.8667	86	0.9011	0.9213	0.9111	178	0.9688	0.9688	0.9688	128	0.9059	0.9337	0.9196	0.9838
0.0399	61.0	5856	0.0446	0.8404	0.9186	0.8778	86	0.8919	0.9270	0.9091	178	0.9688	0.9688	0.9688	128	0.9042	0.9388	0.9212	0.9841
0.0393	62.0	5952	0.0454	0.8421	0.9302	0.8840	86	0.9	0.9101	0.9050	178	0.9688	0.9688	0.9688	128	0.9082	0.9337	0.9208	0.9841
0.0386	63.0	6048	0.0455	0.8454	0.9535	0.8962	86	0.8967	0.9270	0.9116	178	0.9688	0.9688	0.9688	128	0.9071	0.9464	0.9263	0.9846
0.0386	64.0	6144	0.0443	0.8387	0.9070	0.8715	86	0.8817	0.9213	0.9011	178	0.9688	0.9688	0.9688	128	0.8993	0.9337	0.9161	0.9843
0.0378	65.0	6240	0.0457	0.8351	0.9419	0.8852	86	0.8973	0.9326	0.9146	178	0.9688	0.9688	0.9688	128	0.9049	0.9464	0.9252	0.9841
0.0376	66.0	6336	0.0458	0.8421	0.9302	0.8840	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9111	0.9413	0.9260	0.9846
0.0362	67.0	6432	0.0456	0.8421	0.9302	0.8840	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9111	0.9413	0.9260	0.9852
0.0366	68.0	6528	0.0458	0.8617	0.9419	0.9000	86	0.9011	0.9213	0.9111	178	0.9688	0.9688	0.9688	128	0.9134	0.9413	0.9271	0.9841
0.038	69.0	6624	0.0459	0.8617	0.9419	0.9000	86	0.9162	0.9213	0.9188	178	0.9688	0.9688	0.9688	128	0.9202	0.9413	0.9306	0.9849
0.0364	70.0	6720	0.0450	0.8526	0.9419	0.8950	86	0.9016	0.9270	0.9141	178	0.9688	0.9688	0.9688	128	0.9113	0.9439	0.9273	0.9849
0.0361	71.0	6816	0.0453	0.8723	0.9535	0.9111	86	0.9022	0.9326	0.9171	178	0.9688	0.9688	0.9688	128	0.9163	0.9490	0.9323	0.9857
0.0358	72.0	6912	0.0475	0.8632	0.9535	0.9061	86	0.9	0.9101	0.9050	178	0.9612	0.9688	0.9650	128	0.9109	0.9388	0.9246	0.9852
0.0356	73.0	7008	0.0463	0.8316	0.9186	0.8729	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9104	0.9337	0.9219	0.9852
0.0366	74.0	7104	0.0460	0.8723	0.9535	0.9111	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9202	0.9413	0.9306	0.9846
0.0345	75.0	7200	0.0456	0.8542	0.9535	0.9011	86	0.9061	0.9213	0.9136	178	0.9688	0.9688	0.9688	128	0.9136	0.9439	0.9285	0.9846
0.0352	76.0	7296	0.0455	0.8333	0.9302	0.8791	86	0.9061	0.9213	0.9136	178	0.9688	0.9688	0.9688	128	0.9086	0.9388	0.9235	0.9852
0.0344	77.0	7392	0.0444	0.8723	0.9535	0.9111	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9183	0.9464	0.9322	0.9854
0.0364	78.0	7488	0.0462	0.8723	0.9535	0.9111	86	0.9061	0.9213	0.9136	178	0.9612	0.9688	0.9650	128	0.9158	0.9439	0.9296	0.9854
0.0351	79.0	7584	0.0451	0.8737	0.9651	0.9171	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9185	0.9490	0.9335	0.9857
0.0359	80.0	7680	0.0453	0.8737	0.9651	0.9171	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9185	0.9490	0.9335	0.9857
0.0338	81.0	7776	0.0448	0.8542	0.9535	0.9011	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9138	0.9464	0.9298	0.9857
0.0349	82.0	7872	0.0455	0.8632	0.9535	0.9061	86	0.9056	0.9157	0.9106	178	0.9612	0.9688	0.9650	128	0.9134	0.9413	0.9271	0.9849
0.0346	83.0	7968	0.0448	0.8723	0.9535	0.9111	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9183	0.9464	0.9322	0.9852
0.0339	84.0	8064	0.0456	0.8632	0.9535	0.9061	86	0.9061	0.9213	0.9136	178	0.9688	0.9688	0.9688	128	0.9158	0.9439	0.9296	0.9849
0.0336	85.0	8160	0.0456	0.8723	0.9535	0.9111	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9204	0.9439	0.9320	0.9852
0.0333	86.0	8256	0.0453	0.8632	0.9535	0.9061	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9160	0.9464	0.9310	0.9849
0.0354	87.0	8352	0.0452	0.8723	0.9535	0.9111	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9202	0.9413	0.9306	0.9846
0.0348	88.0	8448	0.0451	0.8723	0.9535	0.9111	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9183	0.9464	0.9322	0.9852
0.034	89.0	8544	0.0450	0.8723	0.9535	0.9111	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9183	0.9464	0.9322	0.9852
0.0337	90.0	8640	0.0451	0.8632	0.9535	0.9061	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9160	0.9464	0.9310	0.9849
0.0325	91.0	8736	0.0451	0.8817	0.9535	0.9162	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9225	0.9413	0.9318	0.9852
0.0325	92.0	8832	0.0452	0.8817	0.9535	0.9162	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9225	0.9413	0.9318	0.9852
0.0342	93.0	8928	0.0449	0.8632	0.9535	0.9061	86	0.9066	0.9270	0.9167	178	0.9688	0.9688	0.9688	128	0.9160	0.9464	0.9310	0.9849
0.0321	94.0	9024	0.0449	0.8817	0.9535	0.9162	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9227	0.9439	0.9332	0.9854
0.0335	95.0	9120	0.0450	0.8817	0.9535	0.9162	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9227	0.9439	0.9332	0.9857
0.033	96.0	9216	0.0450	0.8723	0.9535	0.9111	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9204	0.9439	0.9320	0.9852
0.0322	97.0	9312	0.0452	0.8723	0.9535	0.9111	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9204	0.9439	0.9320	0.9854
0.0333	98.0	9408	0.0451	0.8632	0.9535	0.9061	86	0.9116	0.9270	0.9192	178	0.9688	0.9688	0.9688	128	0.9183	0.9464	0.9322	0.9852
0.0317	99.0	9504	0.0452	0.8632	0.9535	0.9061	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9181	0.9439	0.9308	0.9852
0.0332	100.0	9600	0.0452	0.8632	0.9535	0.9061	86	0.9111	0.9213	0.9162	178	0.9688	0.9688	0.9688	128	0.9181	0.9439	0.9308	0.9852

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-seq_bn-rf64-3

nerui-seq_bn-rf64-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-seq_bn-rf64-3