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apwic/
nerui-seq_bn-rf64-0

Name: nerui-seq_bn-rf64-0
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-seq_bn-rf64-0

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: 0.0421
Location Precision: 0.9091
Location Recall: 0.9574
Location F1: 0.9326
Location Number: 94
Organization Precision: 0.9042
Organization Recall: 0.9042
Organization F1: 0.9042
Organization Number: 167
Person Precision: 0.9853
Person Recall: 0.9781
Person F1: 0.9817
Person Number: 137
Overall Precision: 0.9328
Overall Recall: 0.9422
Overall F1: 0.9375
Overall Accuracy: 0.9856

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
1.0601	1.0	96	0.6564	0.0	0.0	0.0	94	0.0	0.0	0.0	167	0.0	0.0	0.0	137	0.0	0.0	0.0	0.8343
0.5969	2.0	192	0.4559	0.0	0.0	0.0	94	0.3571	0.0599	0.1026	167	0.3542	0.1241	0.1838	137	0.3506	0.0678	0.1137	0.8453
0.4292	3.0	288	0.3352	0.3871	0.1277	0.192	94	0.3830	0.4311	0.4056	167	0.3350	0.5036	0.4023	137	0.36	0.3844	0.3718	0.8914
0.3453	4.0	384	0.2879	0.4062	0.2766	0.3291	94	0.4267	0.5749	0.4898	167	0.4271	0.6204	0.5060	137	0.4242	0.5201	0.4673	0.9124
0.2994	5.0	480	0.2501	0.4578	0.4043	0.4294	94	0.4843	0.6467	0.5538	167	0.5	0.7007	0.5836	137	0.4859	0.6080	0.5402	0.9290
0.2653	6.0	576	0.2132	0.5465	0.5	0.5222	94	0.5333	0.7186	0.6122	167	0.6570	0.8248	0.7314	137	0.5797	0.7035	0.6356	0.9423
0.2223	7.0	672	0.1735	0.5833	0.5957	0.5895	94	0.6212	0.7365	0.6740	167	0.7922	0.8905	0.8385	137	0.6719	0.7563	0.7116	0.9541
0.1861	8.0	768	0.1423	0.6667	0.7021	0.6839	94	0.6809	0.7665	0.7211	167	0.8105	0.9051	0.8552	137	0.7227	0.7990	0.7589	0.9599
0.1623	9.0	864	0.1244	0.7358	0.8298	0.78	94	0.7211	0.8204	0.7675	167	0.8658	0.9416	0.9021	137	0.7730	0.8643	0.8161	0.9652
0.1406	10.0	960	0.1061	0.7714	0.8617	0.8141	94	0.7556	0.8144	0.7839	167	0.9041	0.9635	0.9329	137	0.8097	0.8769	0.8420	0.9702
0.1277	11.0	1056	0.0965	0.82	0.8723	0.8454	94	0.7816	0.8144	0.7977	167	0.9041	0.9635	0.9329	137	0.8333	0.8794	0.8557	0.9735
0.1217	12.0	1152	0.0922	0.7778	0.8936	0.8317	94	0.7650	0.8383	0.8000	167	0.9172	0.9708	0.9433	137	0.8188	0.8970	0.8561	0.9691
0.1126	13.0	1248	0.0824	0.84	0.8936	0.8660	94	0.8343	0.8443	0.8393	167	0.9568	0.9708	0.9638	137	0.8775	0.8995	0.8883	0.9768
0.1064	14.0	1344	0.0791	0.8155	0.8936	0.8528	94	0.7989	0.8563	0.8266	167	0.9366	0.9708	0.9534	137	0.8491	0.9045	0.8759	0.9738
0.1032	15.0	1440	0.0750	0.8515	0.9149	0.8821	94	0.8192	0.8683	0.8430	167	0.9640	0.9781	0.9710	137	0.8753	0.9171	0.8957	0.9771
0.1016	16.0	1536	0.0726	0.8155	0.8936	0.8528	94	0.8305	0.8802	0.8547	167	0.95	0.9708	0.9603	137	0.8667	0.9146	0.8900	0.9768
0.0945	17.0	1632	0.0692	0.8416	0.9043	0.8718	94	0.8266	0.8563	0.8412	167	0.9710	0.9781	0.9745	137	0.8786	0.9095	0.8938	0.9773
0.0924	18.0	1728	0.0676	0.8416	0.9043	0.8718	94	0.8266	0.8563	0.8412	167	0.9710	0.9781	0.9745	137	0.8786	0.9095	0.8938	0.9771
0.0839	19.0	1824	0.0649	0.8416	0.9043	0.8718	94	0.8430	0.8683	0.8555	167	0.9710	0.9781	0.9745	137	0.8856	0.9146	0.8999	0.9782
0.0862	20.0	1920	0.0633	0.8485	0.8936	0.8705	94	0.8324	0.8623	0.8471	167	0.9640	0.9781	0.9710	137	0.8808	0.9095	0.8949	0.9773
0.0828	21.0	2016	0.0632	0.8269	0.9149	0.8687	94	0.8324	0.8623	0.8471	167	0.9640	0.9781	0.9710	137	0.875	0.9146	0.8943	0.9765
0.0812	22.0	2112	0.0592	0.8673	0.9043	0.8854	94	0.8497	0.8802	0.8647	167	0.9710	0.9781	0.9745	137	0.8949	0.9196	0.9071	0.9790
0.0782	23.0	2208	0.0589	0.8614	0.9255	0.8923	94	0.8480	0.8683	0.8580	167	0.9640	0.9781	0.9710	137	0.8905	0.9196	0.9048	0.9787
0.0737	24.0	2304	0.0571	0.8687	0.9149	0.8912	94	0.8596	0.8802	0.8698	167	0.9710	0.9781	0.9745	137	0.8995	0.9221	0.9107	0.9801
0.0752	25.0	2400	0.0570	0.8627	0.9362	0.8980	94	0.8631	0.8683	0.8657	167	0.9710	0.9781	0.9745	137	0.8995	0.9221	0.9107	0.9796
0.069	26.0	2496	0.0556	0.88	0.9362	0.9072	94	0.8596	0.8802	0.8698	167	0.9710	0.9781	0.9745	137	0.9022	0.9271	0.9145	0.9804
0.0717	27.0	2592	0.0538	0.87	0.9255	0.8969	94	0.8448	0.8802	0.8622	167	0.9710	0.9781	0.9745	137	0.8932	0.9246	0.9086	0.9809
0.0688	28.0	2688	0.0533	0.8788	0.9255	0.9016	94	0.8588	0.8743	0.8665	167	0.9710	0.9781	0.9745	137	0.9017	0.9221	0.9118	0.9818
0.0652	29.0	2784	0.0519	0.8889	0.9362	0.9119	94	0.8448	0.8802	0.8622	167	0.9710	0.9781	0.9745	137	0.8978	0.9271	0.9122	0.9812
0.0668	30.0	2880	0.0529	0.8614	0.9255	0.8923	94	0.8343	0.8743	0.8538	167	0.9640	0.9781	0.9710	137	0.8843	0.9221	0.9028	0.9801
0.0642	31.0	2976	0.0498	0.89	0.9468	0.9175	94	0.8538	0.8743	0.8639	167	0.9781	0.9781	0.9781	137	0.9044	0.9271	0.9156	0.9820
0.0627	32.0	3072	0.0509	0.8911	0.9574	0.9231	94	0.875	0.8802	0.8776	167	0.9781	0.9781	0.9781	137	0.9138	0.9322	0.9229	0.9829
0.0603	33.0	3168	0.0510	0.8713	0.9362	0.9026	94	0.8480	0.8683	0.8580	167	0.9710	0.9781	0.9745	137	0.8951	0.9221	0.9084	0.9809
0.0593	34.0	3264	0.0474	0.8889	0.9362	0.9119	94	0.8869	0.8922	0.8896	167	0.9853	0.9781	0.9817	137	0.9206	0.9322	0.9263	0.9834
0.0603	35.0	3360	0.0474	0.88	0.9362	0.9072	94	0.8621	0.8982	0.8798	167	0.9781	0.9781	0.9781	137	0.9051	0.9347	0.9197	0.9829
0.0583	36.0	3456	0.0490	0.8713	0.9362	0.9026	94	0.8605	0.8862	0.8732	167	0.9781	0.9781	0.9781	137	0.9024	0.9296	0.9158	0.9818
0.0557	37.0	3552	0.0478	0.8889	0.9362	0.9119	94	0.8675	0.8623	0.8649	167	0.9853	0.9781	0.9817	137	0.9127	0.9196	0.9161	0.9823
0.0587	38.0	3648	0.0472	0.8788	0.9255	0.9016	94	0.8655	0.8862	0.8757	167	0.9781	0.9781	0.9781	137	0.9066	0.9271	0.9168	0.9826
0.0576	39.0	3744	0.0477	0.8614	0.9255	0.8923	94	0.8667	0.8563	0.8614	167	0.9853	0.9781	0.9817	137	0.9055	0.9146	0.91	0.9818
0.0567	40.0	3840	0.0474	0.8713	0.9362	0.9026	94	0.8916	0.8862	0.8889	167	0.9781	0.9781	0.9781	137	0.9158	0.9296	0.9227	0.9831
0.0544	41.0	3936	0.0483	0.8627	0.9362	0.8980	94	0.8862	0.8862	0.8862	167	0.9781	0.9781	0.9781	137	0.9113	0.9296	0.9204	0.9826
0.0532	42.0	4032	0.0461	0.8990	0.9468	0.9223	94	0.8909	0.8802	0.8855	167	0.9853	0.9781	0.9817	137	0.925	0.9296	0.9273	0.9831
0.053	43.0	4128	0.0468	0.8812	0.9468	0.9128	94	0.8841	0.8683	0.8761	167	0.9853	0.9781	0.9817	137	0.9177	0.9246	0.9212	0.9831
0.0519	44.0	4224	0.0451	0.8889	0.9362	0.9119	94	0.8916	0.8862	0.8889	167	0.9853	0.9781	0.9817	137	0.9227	0.9296	0.9262	0.9837
0.0511	45.0	4320	0.0447	0.8889	0.9362	0.9119	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9254	0.9347	0.93	0.9837
0.0494	46.0	4416	0.0461	0.88	0.9362	0.9072	94	0.8902	0.8743	0.8822	167	0.9853	0.9781	0.9817	137	0.92	0.9246	0.9223	0.9826
0.0486	47.0	4512	0.0458	0.88	0.9362	0.9072	94	0.8802	0.8802	0.8802	167	0.9853	0.9781	0.9817	137	0.9156	0.9271	0.9213	0.9823
0.048	48.0	4608	0.0453	0.8980	0.9362	0.9167	94	0.8909	0.8802	0.8855	167	0.9853	0.9781	0.9817	137	0.9248	0.9271	0.9260	0.9834
0.0485	49.0	4704	0.0441	0.88	0.9362	0.9072	94	0.9157	0.9102	0.9129	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9848
0.0478	50.0	4800	0.0444	0.8889	0.9362	0.9119	94	0.8922	0.8922	0.8922	167	0.9853	0.9781	0.9817	137	0.9229	0.9322	0.9275	0.9845
0.0466	51.0	4896	0.0454	0.8990	0.9468	0.9223	94	0.8970	0.8862	0.8916	167	0.9853	0.9781	0.9817	137	0.9275	0.9322	0.9298	0.9837
0.0468	52.0	4992	0.0441	0.89	0.9468	0.9175	94	0.8976	0.8922	0.8949	167	0.9853	0.9781	0.9817	137	0.9254	0.9347	0.93	0.9843
0.0459	53.0	5088	0.0442	0.8980	0.9362	0.9167	94	0.8976	0.8922	0.8949	167	0.9853	0.9781	0.9817	137	0.9275	0.9322	0.9298	0.9837
0.0465	54.0	5184	0.0441	0.8980	0.9362	0.9167	94	0.9024	0.8862	0.8943	167	0.9853	0.9781	0.9817	137	0.9296	0.9296	0.9296	0.9843
0.0453	55.0	5280	0.0427	0.8980	0.9362	0.9167	94	0.8988	0.9042	0.9015	167	0.9853	0.9781	0.9817	137	0.9279	0.9372	0.9325	0.9851
0.0432	56.0	5376	0.0438	0.8980	0.9362	0.9167	94	0.8916	0.8862	0.8889	167	0.9853	0.9781	0.9817	137	0.925	0.9296	0.9273	0.9845
0.0431	57.0	5472	0.0457	0.8627	0.9362	0.8980	94	0.9024	0.8862	0.8943	167	0.9853	0.9781	0.9817	137	0.9204	0.9296	0.925	0.9837
0.0424	58.0	5568	0.0439	0.8980	0.9362	0.9167	94	0.8976	0.8922	0.8949	167	0.9853	0.9781	0.9817	137	0.9275	0.9322	0.9298	0.9845
0.0434	59.0	5664	0.0441	0.8980	0.9362	0.9167	94	0.8909	0.8802	0.8855	167	0.9853	0.9781	0.9817	137	0.9248	0.9271	0.9260	0.9840
0.0429	60.0	5760	0.0444	0.9082	0.9468	0.9271	94	0.9091	0.8982	0.9036	167	0.9853	0.9781	0.9817	137	0.9348	0.9372	0.9360	0.9851
0.0421	61.0	5856	0.0442	0.8990	0.9468	0.9223	94	0.9172	0.9281	0.9226	167	0.9781	0.9781	0.9781	137	0.9333	0.9497	0.9415	0.9856
0.0414	62.0	5952	0.0435	0.8990	0.9468	0.9223	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9327	0.9397	0.9362	0.9848
0.0425	63.0	6048	0.0432	0.8980	0.9362	0.9167	94	0.8976	0.8922	0.8949	167	0.9853	0.9781	0.9817	137	0.9275	0.9322	0.9298	0.9843
0.0412	64.0	6144	0.0451	0.89	0.9468	0.9175	94	0.8889	0.8623	0.8754	167	0.9853	0.9781	0.9817	137	0.9221	0.9221	0.9221	0.9834
0.0422	65.0	6240	0.0423	0.9082	0.9468	0.9271	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9851
0.0409	66.0	6336	0.0439	0.8980	0.9362	0.9167	94	0.8916	0.8862	0.8889	167	0.9853	0.9781	0.9817	137	0.925	0.9296	0.9273	0.9843
0.0379	67.0	6432	0.0446	0.8980	0.9362	0.9167	94	0.8902	0.8743	0.8822	167	0.9853	0.9781	0.9817	137	0.9246	0.9246	0.9246	0.9840
0.0383	68.0	6528	0.0438	0.8980	0.9362	0.9167	94	0.8869	0.8922	0.8896	167	0.9853	0.9781	0.9817	137	0.9229	0.9322	0.9275	0.9843
0.0404	69.0	6624	0.0437	0.9082	0.9468	0.9271	94	0.8869	0.8922	0.8896	167	0.9853	0.9781	0.9817	137	0.9254	0.9347	0.93	0.9848
0.0392	70.0	6720	0.0427	0.8980	0.9362	0.9167	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9851
0.0389	71.0	6816	0.0423	0.8990	0.9468	0.9223	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9848
0.0401	72.0	6912	0.0426	0.8990	0.9468	0.9223	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9848
0.0376	73.0	7008	0.0422	0.8980	0.9362	0.9167	94	0.9048	0.9102	0.9075	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9854
0.0364	74.0	7104	0.0427	0.8990	0.9468	0.9223	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9327	0.9397	0.9362	0.9854
0.0389	75.0	7200	0.0432	0.8990	0.9468	0.9223	94	0.9102	0.9102	0.9102	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9851
0.0384	76.0	7296	0.0427	0.8889	0.9362	0.9119	94	0.9152	0.9042	0.9096	167	0.9853	0.9781	0.9817	137	0.9325	0.9372	0.9348	0.9845
0.0384	77.0	7392	0.0434	0.8990	0.9468	0.9223	94	0.9048	0.9102	0.9075	167	0.9853	0.9781	0.9817	137	0.9305	0.9422	0.9363	0.9854
0.0368	78.0	7488	0.0428	0.8990	0.9468	0.9223	94	0.8988	0.9042	0.9015	167	0.9853	0.9781	0.9817	137	0.9280	0.9397	0.9338	0.9851
0.0374	79.0	7584	0.0429	0.9082	0.9468	0.9271	94	0.9091	0.8982	0.9036	167	0.9853	0.9781	0.9817	137	0.9348	0.9372	0.9360	0.9848
0.0371	80.0	7680	0.0423	0.89	0.9468	0.9175	94	0.9162	0.9162	0.9162	167	0.9853	0.9781	0.9817	137	0.9330	0.9447	0.9388	0.9856
0.0359	81.0	7776	0.0418	0.9082	0.9468	0.9271	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9854
0.0357	82.0	7872	0.0429	0.9091	0.9574	0.9326	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9851
0.0354	83.0	7968	0.0426	0.9091	0.9574	0.9326	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9352	0.9422	0.9387	0.9854
0.0354	84.0	8064	0.0419	0.9091	0.9574	0.9326	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9352	0.9422	0.9387	0.9854
0.0358	85.0	8160	0.0425	0.9091	0.9574	0.9326	94	0.8976	0.8922	0.8949	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9848
0.0384	86.0	8256	0.0423	0.9091	0.9574	0.9326	94	0.8922	0.8922	0.8922	167	0.9853	0.9781	0.9817	137	0.9279	0.9372	0.9325	0.9848
0.0354	87.0	8352	0.0422	0.9091	0.9574	0.9326	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9352	0.9422	0.9387	0.9854
0.036	88.0	8448	0.0415	0.9082	0.9468	0.9271	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9851
0.0354	89.0	8544	0.0419	0.9	0.9574	0.9278	94	0.9048	0.9102	0.9075	167	0.9853	0.9781	0.9817	137	0.9307	0.9447	0.9377	0.9856
0.0348	90.0	8640	0.0423	0.9091	0.9574	0.9326	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9851
0.0355	91.0	8736	0.0427	0.9091	0.9574	0.9326	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9851
0.0359	92.0	8832	0.0427	0.9091	0.9574	0.9326	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9303	0.9397	0.9350	0.9851
0.0366	93.0	8928	0.0423	0.9091	0.9574	0.9326	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9856
0.0328	94.0	9024	0.0421	0.9091	0.9574	0.9326	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9352	0.9422	0.9387	0.9859
0.0332	95.0	9120	0.0422	0.9091	0.9574	0.9326	94	0.9096	0.9042	0.9069	167	0.9853	0.9781	0.9817	137	0.9352	0.9422	0.9387	0.9854
0.0354	96.0	9216	0.0420	0.9082	0.9468	0.9271	94	0.8982	0.8982	0.8982	167	0.9853	0.9781	0.9817	137	0.9302	0.9372	0.9337	0.9854
0.0361	97.0	9312	0.0423	0.9091	0.9574	0.9326	94	0.9152	0.9042	0.9096	167	0.9853	0.9781	0.9817	137	0.9375	0.9422	0.9398	0.9856
0.0349	98.0	9408	0.0421	0.9091	0.9574	0.9326	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9856
0.0355	99.0	9504	0.0420	0.9091	0.9574	0.9326	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9856
0.035	100.0	9600	0.0421	0.9091	0.9574	0.9326	94	0.9042	0.9042	0.9042	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9856

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-seq_bn-rf64-0

nerui-seq_bn-rf64-0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-seq_bn-rf64-0