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apwic/
nerui-seq_bn-0

Name: nerui-seq_bn-0
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-seq_bn-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: 0.0567
Location Precision: 0.89
Location Recall: 0.9468
Location F1: 0.9175
Location Number: 94
Organization Precision: 0.8848
Organization Recall: 0.8743
Organization F1: 0.8795
Organization Number: 167
Person Precision: 0.9854
Person Recall: 0.9854
Person F1: 0.9854
Person Number: 137
Overall Precision: 0.9204
Overall Recall: 0.9296
Overall F1: 0.925
Overall Accuracy: 0.9840

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
0.8465	1.0	96	0.5495	0.0	0.0	0.0	94	0.3333	0.0060	0.0118	167	0.0	0.0	0.0	137	0.1667	0.0025	0.0050	0.8345
0.4598	2.0	192	0.3268	0.3939	0.1383	0.2047	94	0.3670	0.4132	0.3887	167	0.3086	0.5474	0.3947	137	0.3384	0.3945	0.3643	0.8923
0.3128	3.0	288	0.2301	0.4235	0.3830	0.4022	94	0.5357	0.6287	0.5785	167	0.6011	0.7810	0.6794	137	0.5403	0.6231	0.5788	0.9329
0.2169	4.0	384	0.1409	0.5644	0.6064	0.5846	94	0.6615	0.7725	0.7127	167	0.8707	0.9343	0.9014	137	0.7088	0.7889	0.7467	0.9577
0.1458	5.0	480	0.1037	0.7685	0.8830	0.8218	94	0.7625	0.7305	0.7462	167	0.9161	0.9562	0.9357	137	0.8175	0.8442	0.8307	0.9685
0.1248	6.0	576	0.0899	0.7981	0.8830	0.8384	94	0.7778	0.8383	0.8069	167	0.9306	0.9781	0.9537	137	0.8341	0.8970	0.8644	0.9718
0.1067	7.0	672	0.0815	0.7838	0.9255	0.8488	94	0.8072	0.8024	0.8048	167	0.9306	0.9781	0.9537	137	0.8432	0.8920	0.8669	0.9729
0.0964	8.0	768	0.0750	0.8381	0.9362	0.8844	94	0.8084	0.8084	0.8084	167	0.9437	0.9781	0.9606	137	0.8623	0.8970	0.8793	0.9751
0.0926	9.0	864	0.0684	0.8190	0.9149	0.8643	94	0.8372	0.8623	0.8496	167	0.9574	0.9854	0.9712	137	0.8732	0.9171	0.8946	0.9762
0.082	10.0	960	0.0670	0.8108	0.9574	0.8780	94	0.8274	0.8323	0.8299	167	0.9643	0.9854	0.9747	137	0.8687	0.9146	0.8911	0.9765
0.0787	11.0	1056	0.0617	0.87	0.9255	0.8969	94	0.8545	0.8443	0.8494	167	0.9640	0.9781	0.9710	137	0.8960	0.9095	0.9027	0.9793
0.0759	12.0	1152	0.0591	0.8318	0.9468	0.8856	94	0.8421	0.8623	0.8521	167	0.9712	0.9854	0.9783	137	0.8825	0.9246	0.9031	0.9782
0.069	13.0	1248	0.0581	0.8411	0.9574	0.8955	94	0.8642	0.8383	0.8511	167	0.9710	0.9781	0.9745	137	0.8943	0.9146	0.9043	0.9798
0.0648	14.0	1344	0.0534	0.8318	0.9468	0.8856	94	0.8580	0.8683	0.8631	167	0.9783	0.9854	0.9818	137	0.8913	0.9271	0.9089	0.9801
0.0637	15.0	1440	0.0509	0.8476	0.9468	0.8945	94	0.875	0.8802	0.8776	167	0.9854	0.9854	0.9854	137	0.9049	0.9322	0.9183	0.9820
0.061	16.0	1536	0.0484	0.8763	0.9043	0.8901	94	0.8629	0.9042	0.8830	167	1.0	0.9854	0.9926	137	0.9115	0.9322	0.9217	0.9826
0.056	17.0	1632	0.0502	0.8558	0.9468	0.8990	94	0.8580	0.8683	0.8631	167	0.9853	0.9781	0.9817	137	0.8998	0.9246	0.9120	0.9823
0.0566	18.0	1728	0.0482	0.8544	0.9362	0.8934	94	0.8690	0.8743	0.8716	167	0.9853	0.9781	0.9817	137	0.9042	0.9246	0.9143	0.9826
0.0508	19.0	1824	0.0475	0.8614	0.9255	0.8923	94	0.8743	0.8743	0.8743	167	0.9853	0.9781	0.9817	137	0.9084	0.9221	0.9152	0.9831
0.05	20.0	1920	0.0500	0.8381	0.9362	0.8844	94	0.8614	0.8563	0.8589	167	0.9926	0.9854	0.9890	137	0.8993	0.9196	0.9093	0.9820
0.0481	21.0	2016	0.0526	0.8148	0.9362	0.8713	94	0.8371	0.8922	0.8638	167	0.9854	0.9854	0.9854	137	0.8794	0.9347	0.9062	0.9807
0.0456	22.0	2112	0.0465	0.8922	0.9681	0.9286	94	0.8655	0.8862	0.8757	167	0.9853	0.9781	0.9817	137	0.9120	0.9372	0.9244	0.9843
0.0438	23.0	2208	0.0555	0.8571	0.9574	0.9045	94	0.8553	0.8144	0.8344	167	0.9926	0.9854	0.9890	137	0.9025	0.9070	0.9048	0.9809
0.0417	24.0	2304	0.0477	0.8812	0.9468	0.9128	94	0.8947	0.9162	0.9053	167	1.0	0.9854	0.9926	137	0.9263	0.9472	0.9366	0.9862
0.0412	25.0	2400	0.0511	0.8411	0.9574	0.8955	94	0.8614	0.8563	0.8589	167	0.9926	0.9854	0.9890	137	0.8998	0.9246	0.9120	0.9829
0.0377	26.0	2496	0.0482	0.8922	0.9681	0.9286	94	0.8802	0.8802	0.8802	167	0.9854	0.9854	0.9854	137	0.9187	0.9372	0.9279	0.9845
0.0394	27.0	2592	0.0450	0.8990	0.9468	0.9223	94	0.8862	0.8862	0.8862	167	0.9853	0.9781	0.9817	137	0.9229	0.9322	0.9275	0.9843
0.0352	28.0	2688	0.0461	0.9	0.9574	0.9278	94	0.8855	0.8802	0.8829	167	0.9781	0.9781	0.9781	137	0.9206	0.9322	0.9263	0.9843
0.0337	29.0	2784	0.0465	0.8641	0.9468	0.9036	94	0.8810	0.8862	0.8836	167	0.9926	0.9854	0.9890	137	0.9140	0.9347	0.9242	0.9845
0.0329	30.0	2880	0.0463	0.88	0.9362	0.9072	94	0.8869	0.8922	0.8896	167	0.9926	0.9854	0.9890	137	0.9208	0.9347	0.9277	0.9840
0.0323	31.0	2976	0.0475	0.8911	0.9574	0.9231	94	0.8902	0.8743	0.8822	167	0.9854	0.9854	0.9854	137	0.9229	0.9322	0.9275	0.9851
0.0297	32.0	3072	0.0488	0.8667	0.9681	0.9146	94	0.8841	0.8683	0.8761	167	0.9781	0.9781	0.9781	137	0.9113	0.9296	0.9204	0.9831
0.0306	33.0	3168	0.0485	0.8824	0.9574	0.9184	94	0.8909	0.8802	0.8855	167	0.9854	0.9854	0.9854	137	0.9208	0.9347	0.9277	0.9843
0.0288	34.0	3264	0.0525	0.8558	0.9468	0.8990	94	0.8834	0.8623	0.8727	167	0.9854	0.9854	0.9854	137	0.9109	0.9246	0.9177	0.9815
0.0283	35.0	3360	0.0485	0.8911	0.9574	0.9231	94	0.8951	0.8683	0.8815	167	0.9854	0.9854	0.9854	137	0.925	0.9296	0.9273	0.9845
0.0289	36.0	3456	0.0485	0.8529	0.9255	0.8878	94	0.8671	0.8982	0.8824	167	0.9854	0.9854	0.9854	137	0.9029	0.9347	0.9185	0.9820
0.0266	37.0	3552	0.0528	0.8182	0.9574	0.8824	94	0.8861	0.8383	0.8615	167	0.9854	0.9854	0.9854	137	0.9012	0.9171	0.9091	0.9812
0.0268	38.0	3648	0.0474	0.91	0.9681	0.9381	94	0.9006	0.8683	0.8841	167	0.9568	0.9708	0.9638	137	0.9225	0.9271	0.9248	0.9848
0.0263	39.0	3744	0.0530	0.7965	0.9574	0.8696	94	0.8831	0.8144	0.8474	167	0.9710	0.9781	0.9745	137	0.8889	0.9045	0.8966	0.9809
0.0256	40.0	3840	0.0482	0.8558	0.9468	0.8990	94	0.8655	0.8862	0.8757	167	0.9710	0.9781	0.9745	137	0.8983	0.9322	0.9149	0.9831
0.0241	41.0	3936	0.0499	0.8396	0.9468	0.89	94	0.8606	0.8503	0.8554	167	0.9568	0.9708	0.9638	137	0.8878	0.9146	0.9010	0.9820
0.0241	42.0	4032	0.0463	0.8911	0.9574	0.9231	94	0.8795	0.8743	0.8769	167	0.9854	0.9854	0.9854	137	0.9183	0.9322	0.9252	0.9845
0.0223	43.0	4128	0.0519	0.8835	0.9681	0.9239	94	0.8951	0.8683	0.8815	167	0.9781	0.9781	0.9781	137	0.9204	0.9296	0.925	0.9848
0.0211	44.0	4224	0.0498	0.91	0.9681	0.9381	94	0.9119	0.8683	0.8896	167	0.9781	0.9781	0.9781	137	0.9343	0.9296	0.9320	0.9856
0.0209	45.0	4320	0.0450	0.9	0.9574	0.9278	94	0.9030	0.8922	0.8976	167	0.9710	0.9781	0.9745	137	0.9256	0.9372	0.9313	0.9845
0.0194	46.0	4416	0.0522	0.8824	0.9574	0.9184	94	0.8951	0.8683	0.8815	167	0.9710	0.9781	0.9745	137	0.9179	0.9271	0.9225	0.9837
0.0203	47.0	4512	0.0493	0.9	0.9574	0.9278	94	0.8976	0.8922	0.8949	167	0.9781	0.9781	0.9781	137	0.9256	0.9372	0.9313	0.9854
0.0198	48.0	4608	0.0532	0.8911	0.9574	0.9231	94	0.8951	0.8683	0.8815	167	0.9710	0.9781	0.9745	137	0.9202	0.9271	0.9237	0.9845
0.019	49.0	4704	0.0495	0.8641	0.9468	0.9036	94	0.8795	0.8743	0.8769	167	0.9854	0.9854	0.9854	137	0.9113	0.9296	0.9204	0.9840
0.0188	50.0	4800	0.0510	0.8654	0.9574	0.9091	94	0.8735	0.8683	0.8709	167	0.9854	0.9854	0.9854	137	0.9091	0.9296	0.9193	0.9840
0.0174	51.0	4896	0.0542	0.8824	0.9574	0.9184	94	0.8889	0.8623	0.8754	167	0.9854	0.9854	0.9854	137	0.9202	0.9271	0.9237	0.9843
0.0176	52.0	4992	0.0516	0.8911	0.9574	0.9231	94	0.8976	0.8922	0.8949	167	0.9854	0.9854	0.9854	137	0.9257	0.9397	0.9327	0.9848
0.0177	53.0	5088	0.0543	0.8738	0.9574	0.9137	94	0.8788	0.8683	0.8735	167	0.9854	0.9854	0.9854	137	0.9136	0.9296	0.9215	0.9840
0.0177	54.0	5184	0.0510	0.8824	0.9574	0.9184	94	0.8698	0.8802	0.8750	167	0.9854	0.9854	0.9854	137	0.9118	0.9347	0.9231	0.9840
0.0159	55.0	5280	0.0502	0.8571	0.9574	0.9045	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9115	0.9322	0.9217	0.9837
0.0176	56.0	5376	0.0518	0.9	0.9574	0.9278	94	0.9012	0.8743	0.8875	167	0.9854	0.9854	0.9854	137	0.9298	0.9322	0.9310	0.9854
0.016	57.0	5472	0.0561	0.8182	0.9574	0.8824	94	0.8797	0.8323	0.8554	167	0.9854	0.9854	0.9854	137	0.8988	0.9146	0.9066	0.9823
0.0149	58.0	5568	0.0513	0.8738	0.9574	0.9137	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9160	0.9322	0.9240	0.9845
0.0154	59.0	5664	0.0543	0.8824	0.9574	0.9184	94	0.8957	0.8743	0.8848	167	0.9854	0.9854	0.9854	137	0.9229	0.9322	0.9275	0.9848
0.0156	60.0	5760	0.0560	0.8411	0.9574	0.8955	94	0.8861	0.8383	0.8615	167	0.9854	0.9854	0.9854	137	0.9080	0.9171	0.9125	0.9829
0.0148	61.0	5856	0.0563	0.8241	0.9468	0.8812	94	0.8841	0.8683	0.8761	167	0.9854	0.9854	0.9854	137	0.9022	0.9271	0.9145	0.9823
0.0138	62.0	5952	0.0551	0.8725	0.9468	0.9082	94	0.8882	0.8563	0.8720	167	0.9854	0.9854	0.9854	137	0.9175	0.9221	0.9198	0.9843
0.014	63.0	6048	0.0553	0.8558	0.9468	0.8990	94	0.8802	0.8802	0.8802	167	0.9854	0.9854	0.9854	137	0.9093	0.9322	0.9206	0.9834
0.0133	64.0	6144	0.0588	0.8318	0.9468	0.8856	94	0.8642	0.8383	0.8511	167	0.9854	0.9854	0.9854	137	0.8966	0.9146	0.9055	0.9818
0.0132	65.0	6240	0.0547	0.8411	0.9574	0.8955	94	0.8875	0.8503	0.8685	167	0.9854	0.9854	0.9854	137	0.9084	0.9221	0.9152	0.9829
0.0139	66.0	6336	0.0580	0.8558	0.9468	0.8990	94	0.8758	0.8443	0.8598	167	0.9926	0.9854	0.9890	137	0.9102	0.9171	0.9136	0.9831
0.0127	67.0	6432	0.0560	0.8725	0.9468	0.9082	94	0.8743	0.8743	0.8743	167	0.9854	0.9854	0.9854	137	0.9113	0.9296	0.9204	0.9837
0.0124	68.0	6528	0.0570	0.8558	0.9468	0.8990	94	0.8841	0.8683	0.8761	167	0.9926	0.9854	0.9890	137	0.9134	0.9271	0.9202	0.9840
0.0132	69.0	6624	0.0525	0.8476	0.9468	0.8945	94	0.9080	0.8862	0.8970	167	0.9854	0.9854	0.9854	137	0.9185	0.9347	0.9265	0.9845
0.0131	70.0	6720	0.0511	0.8812	0.9468	0.9128	94	0.8902	0.8743	0.8822	167	0.9854	0.9854	0.9854	137	0.9204	0.9296	0.925	0.9854
0.0125	71.0	6816	0.0503	0.89	0.9468	0.9175	94	0.9030	0.8922	0.8976	167	0.9854	0.9854	0.9854	137	0.9279	0.9372	0.9325	0.9859
0.014	72.0	6912	0.0527	0.8558	0.9468	0.8990	94	0.8957	0.8743	0.8848	167	0.9926	0.9854	0.9890	137	0.9181	0.9296	0.9238	0.9848
0.0123	73.0	7008	0.0536	0.8725	0.9468	0.9082	94	0.8780	0.8623	0.8701	167	0.9854	0.9854	0.9854	137	0.9132	0.9246	0.9189	0.9840
0.0121	74.0	7104	0.0542	0.8990	0.9468	0.9223	94	0.8902	0.8743	0.8822	167	0.9854	0.9854	0.9854	137	0.925	0.9296	0.9273	0.9843
0.0115	75.0	7200	0.0526	0.8911	0.9574	0.9231	94	0.9074	0.8802	0.8936	167	0.9854	0.9854	0.9854	137	0.93	0.9347	0.9323	0.9851
0.0115	76.0	7296	0.0533	0.89	0.9468	0.9175	94	0.8963	0.8802	0.8882	167	0.9854	0.9854	0.9854	137	0.9252	0.9322	0.9287	0.9843
0.0129	77.0	7392	0.0554	0.8476	0.9468	0.8945	94	0.8938	0.8563	0.8746	167	0.9854	0.9854	0.9854	137	0.9129	0.9221	0.9175	0.9831
0.0117	78.0	7488	0.0548	0.8641	0.9468	0.9036	94	0.8916	0.8862	0.8889	167	0.9854	0.9854	0.9854	137	0.9163	0.9347	0.9254	0.9843
0.0107	79.0	7584	0.0551	0.8990	0.9468	0.9223	94	0.8970	0.8862	0.8916	167	0.9854	0.9854	0.9854	137	0.9277	0.9347	0.9312	0.9848
0.012	80.0	7680	0.0539	0.8641	0.9468	0.9036	94	0.8757	0.8862	0.8810	167	0.9854	0.9854	0.9854	137	0.9095	0.9347	0.9219	0.9834
0.0107	81.0	7776	0.0558	0.8558	0.9468	0.8990	94	0.8931	0.8503	0.8712	167	0.9854	0.9854	0.9854	137	0.915	0.9196	0.9173	0.9837
0.0107	82.0	7872	0.0563	0.8641	0.9468	0.9036	94	0.8827	0.8563	0.8693	167	0.9854	0.9854	0.9854	137	0.9129	0.9221	0.9175	0.9834
0.0102	83.0	7968	0.0569	0.8725	0.9468	0.9082	94	0.8944	0.8623	0.8780	167	0.9854	0.9854	0.9854	137	0.92	0.9246	0.9223	0.9840
0.0103	84.0	8064	0.0567	0.89	0.9468	0.9175	94	0.8889	0.8623	0.8754	167	0.9854	0.9854	0.9854	137	0.9223	0.9246	0.9235	0.9840
0.0105	85.0	8160	0.0597	0.8812	0.9468	0.9128	94	0.8882	0.8563	0.8720	167	0.9926	0.9854	0.9890	137	0.9221	0.9221	0.9221	0.9840
0.0113	86.0	8256	0.0552	0.89	0.9468	0.9175	94	0.8902	0.8743	0.8822	167	0.9854	0.9854	0.9854	137	0.9227	0.9296	0.9262	0.9843
0.0103	87.0	8352	0.0564	0.8725	0.9468	0.9082	94	0.8780	0.8623	0.8701	167	0.9854	0.9854	0.9854	137	0.9132	0.9246	0.9189	0.9834
0.0109	88.0	8448	0.0550	0.8812	0.9468	0.9128	94	0.8896	0.8683	0.8788	167	0.9854	0.9854	0.9854	137	0.9202	0.9271	0.9237	0.9837
0.0099	89.0	8544	0.0541	0.8812	0.9468	0.9128	94	0.8963	0.8802	0.8882	167	0.9854	0.9854	0.9854	137	0.9229	0.9322	0.9275	0.9843
0.0096	90.0	8640	0.0557	0.8812	0.9468	0.9128	94	0.8675	0.8623	0.8649	167	0.9854	0.9854	0.9854	137	0.9109	0.9246	0.9177	0.9831
0.0101	91.0	8736	0.0582	0.8812	0.9468	0.9128	94	0.8773	0.8563	0.8667	167	0.9854	0.9854	0.9854	137	0.9152	0.9221	0.9186	0.9834
0.0106	92.0	8832	0.0563	0.8725	0.9468	0.9082	94	0.8727	0.8623	0.8675	167	0.9854	0.9854	0.9854	137	0.9109	0.9246	0.9177	0.9831
0.0101	93.0	8928	0.0559	0.8725	0.9468	0.9082	94	0.8841	0.8683	0.8761	167	0.9854	0.9854	0.9854	137	0.9156	0.9271	0.9213	0.9834
0.0088	94.0	9024	0.0562	0.89	0.9468	0.9175	94	0.8896	0.8683	0.8788	167	0.9854	0.9854	0.9854	137	0.9225	0.9271	0.9248	0.9840
0.0086	95.0	9120	0.0571	0.89	0.9468	0.9175	94	0.8780	0.8623	0.8701	167	0.9854	0.9854	0.9854	137	0.9177	0.9246	0.9212	0.9837
0.0101	96.0	9216	0.0561	0.89	0.9468	0.9175	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9204	0.9296	0.925	0.9840
0.0101	97.0	9312	0.0580	0.8812	0.9468	0.9128	94	0.8720	0.8563	0.8640	167	0.9854	0.9854	0.9854	137	0.9129	0.9221	0.9175	0.9831
0.0099	98.0	9408	0.0573	0.89	0.9468	0.9175	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9204	0.9296	0.925	0.9837
0.0113	99.0	9504	0.0567	0.89	0.9468	0.9175	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9204	0.9296	0.925	0.9840
0.0091	100.0	9600	0.0567	0.89	0.9468	0.9175	94	0.8848	0.8743	0.8795	167	0.9854	0.9854	0.9854	137	0.9204	0.9296	0.925	0.9840

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-seq_bn-0

nerui-seq_bn-0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-seq_bn-0