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apwic/
nerui-seq_bn-rf16-4

Name: nerui-seq_bn-rf16-4
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-seq_bn-rf16-4

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: 0.0476
Location Precision: 0.8899
Location Recall: 0.9417
Location F1: 0.9151
Location Number: 103
Organization Precision: 0.9006
Organization Recall: 0.9006
Organization F1: 0.9006
Organization Number: 171
Person Precision: 0.9697
Person Recall: 0.9771
Person F1: 0.9734
Person Number: 131
Overall Precision: 0.9199
Overall Recall: 0.9358
Overall F1: 0.9278
Overall Accuracy: 0.9859

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
0.8449	1.0	96	0.5421	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	0.8379
0.4618	2.0	192	0.3207	0.4444	0.1553	0.2302	103	0.3839	0.5029	0.4354	171	0.3259	0.5573	0.4113	131	0.3616	0.4321	0.3937	0.8995
0.3133	3.0	288	0.2317	0.4598	0.3883	0.4211	103	0.5872	0.7485	0.6581	171	0.5882	0.7634	0.6645	131	0.5642	0.6617	0.6091	0.9362
0.219	4.0	384	0.1434	0.5833	0.6117	0.5972	103	0.7208	0.8304	0.7717	171	0.8435	0.9466	0.8921	131	0.7279	0.8123	0.7678	0.9624
0.1518	5.0	480	0.1125	0.7368	0.8155	0.7742	103	0.7841	0.8070	0.7954	171	0.9044	0.9389	0.9213	131	0.8099	0.8519	0.8303	0.9699
0.1247	6.0	576	0.0863	0.7739	0.8641	0.8165	103	0.8372	0.8421	0.8397	171	0.9333	0.9618	0.9474	131	0.8507	0.8864	0.8682	0.9743
0.1071	7.0	672	0.0788	0.8230	0.9029	0.8611	103	0.8409	0.8655	0.8530	171	0.9137	0.9695	0.9407	131	0.8598	0.9086	0.8836	0.9760
0.0973	8.0	768	0.0731	0.8034	0.9126	0.8545	103	0.8512	0.8363	0.8437	171	0.9478	0.9695	0.9585	131	0.8687	0.8988	0.8835	0.9782
0.0908	9.0	864	0.0648	0.8727	0.9320	0.9014	103	0.8743	0.8947	0.8844	171	0.9338	0.9695	0.9513	131	0.8931	0.9284	0.9104	0.9804
0.0806	10.0	960	0.0618	0.8991	0.9515	0.9245	103	0.8793	0.8947	0.8870	171	0.9478	0.9695	0.9585	131	0.9065	0.9333	0.9197	0.9815
0.0744	11.0	1056	0.0592	0.9048	0.9223	0.9135	103	0.8652	0.9006	0.8825	171	0.9549	0.9695	0.9621	131	0.9038	0.9284	0.9160	0.9820
0.0725	12.0	1152	0.0561	0.9159	0.9515	0.9333	103	0.8715	0.9123	0.8914	171	0.9549	0.9695	0.9621	131	0.9093	0.9407	0.9248	0.9823
0.0689	13.0	1248	0.0552	0.9143	0.9320	0.9231	103	0.9048	0.8889	0.8968	171	0.9478	0.9695	0.9585	131	0.9214	0.9259	0.9236	0.9829
0.0648	14.0	1344	0.0568	0.8727	0.9320	0.9014	103	0.9051	0.8363	0.8693	171	0.9478	0.9695	0.9585	131	0.9104	0.9037	0.9071	0.9812
0.0606	15.0	1440	0.0477	0.9320	0.9320	0.9320	103	0.9053	0.8947	0.9000	171	0.9549	0.9695	0.9621	131	0.9284	0.9284	0.9284	0.9851
0.0559	16.0	1536	0.0462	0.8981	0.9417	0.9194	103	0.8988	0.8830	0.8909	171	0.9549	0.9695	0.9621	131	0.9169	0.9259	0.9214	0.9840
0.056	17.0	1632	0.0483	0.9495	0.9126	0.9307	103	0.8927	0.9240	0.9080	171	0.9478	0.9695	0.9585	131	0.9244	0.9358	0.9301	0.9845
0.0518	18.0	1728	0.0455	0.8981	0.9417	0.9194	103	0.9112	0.9006	0.9059	171	0.9621	0.9695	0.9658	131	0.9242	0.9333	0.9287	0.9848
0.049	19.0	1824	0.0502	0.9057	0.9320	0.9187	103	0.9212	0.8889	0.9048	171	0.9478	0.9695	0.9585	131	0.9259	0.9259	0.9259	0.9834
0.0495	20.0	1920	0.0447	0.9151	0.9417	0.9282	103	0.88	0.9006	0.8902	171	0.9549	0.9695	0.9621	131	0.9130	0.9333	0.9231	0.9851
0.0444	21.0	2016	0.0477	0.8991	0.9515	0.9245	103	0.9096	0.8830	0.8961	171	0.9549	0.9695	0.9621	131	0.9216	0.9284	0.9250	0.9843
0.0432	22.0	2112	0.0419	0.9151	0.9417	0.9282	103	0.8807	0.9064	0.8934	171	0.9697	0.9771	0.9734	131	0.9179	0.9383	0.9280	0.9856
0.0419	23.0	2208	0.0434	0.8991	0.9515	0.9245	103	0.8941	0.8889	0.8915	171	0.9549	0.9695	0.9621	131	0.9150	0.9309	0.9229	0.9854
0.0401	24.0	2304	0.0464	0.875	0.9515	0.9116	103	0.8830	0.8830	0.8830	171	0.9549	0.9695	0.9621	131	0.9038	0.9284	0.9160	0.9845
0.0388	25.0	2400	0.0469	0.8909	0.9515	0.9202	103	0.9091	0.8772	0.8929	171	0.9549	0.9695	0.9621	131	0.9191	0.9259	0.9225	0.9837
0.0364	26.0	2496	0.0399	0.9238	0.9417	0.9327	103	0.9012	0.9064	0.9038	171	0.9697	0.9771	0.9734	131	0.9291	0.9383	0.9337	0.9867
0.036	27.0	2592	0.0420	0.9245	0.9515	0.9378	103	0.9006	0.9006	0.9006	171	0.9549	0.9695	0.9621	131	0.9244	0.9358	0.9301	0.9859
0.0349	28.0	2688	0.0393	0.9151	0.9417	0.9282	103	0.8920	0.9181	0.9049	171	0.9549	0.9695	0.9621	131	0.9181	0.9407	0.9293	0.9862
0.0328	29.0	2784	0.0462	0.9151	0.9417	0.9282	103	0.9107	0.8947	0.9027	171	0.9549	0.9695	0.9621	131	0.9263	0.9309	0.9286	0.9843
0.0334	30.0	2880	0.0413	0.9238	0.9417	0.9327	103	0.8983	0.9298	0.9138	171	0.9697	0.9771	0.9734	131	0.9275	0.9481	0.9377	0.9862
0.0334	31.0	2976	0.0401	0.8889	0.9320	0.9100	103	0.8960	0.9064	0.9012	171	0.9697	0.9771	0.9734	131	0.9177	0.9358	0.9267	0.9854
0.0311	32.0	3072	0.0398	0.8919	0.9612	0.9252	103	0.9112	0.9006	0.9059	171	0.9697	0.9771	0.9734	131	0.9248	0.9407	0.9327	0.9862
0.0281	33.0	3168	0.0389	0.8909	0.9515	0.9202	103	0.9059	0.9006	0.9032	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9865
0.0326	34.0	3264	0.0407	0.9065	0.9417	0.9238	103	0.8908	0.9064	0.8986	171	0.9697	0.9771	0.9734	131	0.9201	0.9383	0.9291	0.9862
0.026	35.0	3360	0.0437	0.8909	0.9515	0.9202	103	0.9102	0.8889	0.8994	171	0.9697	0.9771	0.9734	131	0.9242	0.9333	0.9287	0.9854
0.0278	36.0	3456	0.0416	0.8919	0.9612	0.9252	103	0.9222	0.9006	0.9112	171	0.9697	0.9771	0.9734	131	0.9293	0.9407	0.9350	0.9862
0.0263	37.0	3552	0.0406	0.9307	0.9126	0.9216	103	0.8883	0.9298	0.9086	171	0.9697	0.9771	0.9734	131	0.9248	0.9407	0.9327	0.9862
0.0241	38.0	3648	0.0433	0.8761	0.9612	0.9167	103	0.9112	0.9006	0.9059	171	0.9697	0.9771	0.9734	131	0.9203	0.9407	0.9304	0.9862
0.0231	39.0	3744	0.0409	0.8909	0.9515	0.9202	103	0.9172	0.9064	0.9118	171	0.9697	0.9771	0.9734	131	0.9270	0.9407	0.9338	0.9856
0.0229	40.0	3840	0.0437	0.9245	0.9515	0.9378	103	0.9128	0.9181	0.9155	171	0.9697	0.9771	0.9734	131	0.9341	0.9457	0.9399	0.9862
0.0221	41.0	3936	0.0396	0.9143	0.9320	0.9231	103	0.9017	0.9123	0.9070	171	0.9697	0.9771	0.9734	131	0.9268	0.9383	0.9325	0.9859
0.0229	42.0	4032	0.0428	0.8991	0.9515	0.9245	103	0.9157	0.8889	0.9021	171	0.9697	0.9771	0.9734	131	0.9287	0.9333	0.9310	0.9854
0.0225	43.0	4128	0.0408	0.8909	0.9515	0.9202	103	0.9172	0.9064	0.9118	171	0.9697	0.9771	0.9734	131	0.9270	0.9407	0.9338	0.9862
0.0217	44.0	4224	0.0491	0.8991	0.9515	0.9245	103	0.9317	0.8772	0.9036	171	0.9697	0.9771	0.9734	131	0.9353	0.9284	0.9318	0.9851
0.0209	45.0	4320	0.0460	0.8761	0.9612	0.9167	103	0.9157	0.8889	0.9021	171	0.9697	0.9771	0.9734	131	0.9221	0.9358	0.9289	0.9854
0.0196	46.0	4416	0.0433	0.8839	0.9612	0.9209	103	0.9123	0.9123	0.9123	171	0.9697	0.9771	0.9734	131	0.9229	0.9457	0.9341	0.9865
0.0201	47.0	4512	0.0405	0.8962	0.9223	0.9091	103	0.8857	0.9064	0.8960	171	0.9697	0.9771	0.9734	131	0.9153	0.9333	0.9242	0.9862
0.0187	48.0	4608	0.0459	0.8919	0.9612	0.9252	103	0.9222	0.9006	0.9112	171	0.9697	0.9771	0.9734	131	0.9293	0.9407	0.9350	0.9865
0.0172	49.0	4704	0.0445	0.8889	0.9320	0.9100	103	0.9012	0.9064	0.9038	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9854
0.0171	50.0	4800	0.0462	0.8889	0.9320	0.9100	103	0.9064	0.9064	0.9064	171	0.9697	0.9771	0.9734	131	0.9221	0.9358	0.9289	0.9856
0.018	51.0	4896	0.0490	0.9065	0.9417	0.9238	103	0.9337	0.9064	0.9199	171	0.9697	0.9771	0.9734	131	0.9383	0.9383	0.9383	0.9865
0.0165	52.0	4992	0.0449	0.8889	0.9320	0.9100	103	0.9064	0.9064	0.9064	171	0.9697	0.9771	0.9734	131	0.9221	0.9358	0.9289	0.9854
0.0156	53.0	5088	0.0445	0.8879	0.9223	0.9048	103	0.8960	0.9064	0.9012	171	0.9697	0.9771	0.9734	131	0.9175	0.9333	0.9253	0.9851
0.0159	54.0	5184	0.0444	0.9135	0.9223	0.9179	103	0.8977	0.9240	0.9107	171	0.9697	0.9771	0.9734	131	0.9248	0.9407	0.9327	0.9854
0.0171	55.0	5280	0.0511	0.8919	0.9612	0.9252	103	0.9207	0.8830	0.9015	171	0.9697	0.9771	0.9734	131	0.9287	0.9333	0.9310	0.9856
0.0174	56.0	5376	0.0471	0.9048	0.9223	0.9135	103	0.8864	0.9123	0.8991	171	0.9697	0.9771	0.9734	131	0.9177	0.9358	0.9267	0.9848
0.0157	57.0	5472	0.0476	0.8909	0.9515	0.9202	103	0.9017	0.9123	0.9070	171	0.9697	0.9771	0.9734	131	0.9205	0.9432	0.9317	0.9856
0.0162	58.0	5568	0.0458	0.8899	0.9417	0.9151	103	0.9064	0.9064	0.9064	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9856
0.0141	59.0	5664	0.0440	0.9038	0.9126	0.9082	103	0.8971	0.9181	0.9075	171	0.9697	0.9771	0.9734	131	0.9221	0.9358	0.9289	0.9856
0.014	60.0	5760	0.0467	0.8899	0.9417	0.9151	103	0.9167	0.9006	0.9086	171	0.9697	0.9771	0.9734	131	0.9267	0.9358	0.9312	0.9856
0.014	61.0	5856	0.0497	0.8972	0.9320	0.9143	103	0.8895	0.8947	0.8921	171	0.9697	0.9771	0.9734	131	0.9173	0.9309	0.9240	0.9845
0.0139	62.0	5952	0.0457	0.8981	0.9417	0.9194	103	0.9012	0.9064	0.9038	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9859
0.0142	63.0	6048	0.0467	0.8818	0.9417	0.9108	103	0.9222	0.9006	0.9112	171	0.9697	0.9771	0.9734	131	0.9267	0.9358	0.9312	0.9865
0.0137	64.0	6144	0.0475	0.8818	0.9417	0.9108	103	0.9162	0.8947	0.9053	171	0.9697	0.9771	0.9734	131	0.9242	0.9333	0.9287	0.9854
0.0129	65.0	6240	0.0478	0.8899	0.9417	0.9151	103	0.9222	0.9006	0.9112	171	0.9697	0.9771	0.9734	131	0.9289	0.9358	0.9323	0.9859
0.0119	66.0	6336	0.0467	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9856
0.0132	67.0	6432	0.0486	0.8899	0.9417	0.9151	103	0.8895	0.8947	0.8921	171	0.9697	0.9771	0.9734	131	0.9153	0.9333	0.9242	0.9845
0.0137	68.0	6528	0.0449	0.8818	0.9417	0.9108	103	0.9064	0.9064	0.9064	171	0.9697	0.9771	0.9734	131	0.9201	0.9383	0.9291	0.9854
0.0121	69.0	6624	0.0459	0.8981	0.9417	0.9194	103	0.8851	0.9006	0.8928	171	0.9697	0.9771	0.9734	131	0.9155	0.9358	0.9255	0.9848
0.012	70.0	6720	0.0467	0.8899	0.9417	0.9151	103	0.9107	0.8947	0.9027	171	0.9697	0.9771	0.9734	131	0.9242	0.9333	0.9287	0.9859
0.0116	71.0	6816	0.0449	0.9057	0.9320	0.9187	103	0.9023	0.9181	0.9101	171	0.9697	0.9771	0.9734	131	0.9248	0.9407	0.9327	0.9862
0.012	72.0	6912	0.0516	0.8909	0.9515	0.9202	103	0.9152	0.8830	0.8988	171	0.9697	0.9771	0.9734	131	0.9263	0.9309	0.9286	0.9851
0.0116	73.0	7008	0.0487	0.8899	0.9417	0.9151	103	0.8953	0.9006	0.8980	171	0.9697	0.9771	0.9734	131	0.9177	0.9358	0.9267	0.9845
0.0118	74.0	7104	0.0488	0.8981	0.9417	0.9194	103	0.9012	0.9064	0.9038	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9848
0.0126	75.0	7200	0.0507	0.8818	0.9417	0.9108	103	0.9217	0.8947	0.9080	171	0.9697	0.9771	0.9734	131	0.9265	0.9333	0.9299	0.9848
0.0111	76.0	7296	0.0514	0.8899	0.9417	0.9151	103	0.9112	0.9006	0.9059	171	0.9697	0.9771	0.9734	131	0.9244	0.9358	0.9301	0.9851
0.0107	77.0	7392	0.0541	0.8991	0.9515	0.9245	103	0.8922	0.8713	0.8817	171	0.9697	0.9771	0.9734	131	0.9191	0.9259	0.9225	0.9851
0.0109	78.0	7488	0.0491	0.8899	0.9417	0.9151	103	0.8953	0.9006	0.8980	171	0.9697	0.9771	0.9734	131	0.9177	0.9358	0.9267	0.9845
0.0107	79.0	7584	0.0529	0.8818	0.9417	0.9108	103	0.9048	0.8889	0.8968	171	0.9697	0.9771	0.9734	131	0.9195	0.9309	0.9252	0.9845
0.0099	80.0	7680	0.0501	0.8899	0.9417	0.9151	103	0.8895	0.8947	0.8921	171	0.9697	0.9771	0.9734	131	0.9153	0.9333	0.9242	0.9843
0.0109	81.0	7776	0.0484	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9851
0.011	82.0	7872	0.0503	0.8899	0.9417	0.9151	103	0.9064	0.9064	0.9064	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9851
0.0119	83.0	7968	0.0488	0.8972	0.9320	0.9143	103	0.9112	0.9006	0.9059	171	0.9697	0.9771	0.9734	131	0.9265	0.9333	0.9299	0.9859
0.0102	84.0	8064	0.0498	0.8818	0.9417	0.9108	103	0.9226	0.9064	0.9145	171	0.9697	0.9771	0.9734	131	0.9268	0.9383	0.9325	0.9865
0.012	85.0	8160	0.0485	0.8991	0.9515	0.9245	103	0.9172	0.9064	0.9118	171	0.9697	0.9771	0.9734	131	0.9293	0.9407	0.9350	0.9865
0.0105	86.0	8256	0.0489	0.8829	0.9515	0.9159	103	0.9157	0.8889	0.9021	171	0.9697	0.9771	0.9734	131	0.9242	0.9333	0.9287	0.9854
0.0113	87.0	8352	0.0481	0.8981	0.9417	0.9194	103	0.9226	0.9064	0.9145	171	0.9697	0.9771	0.9734	131	0.9314	0.9383	0.9348	0.9865
0.0106	88.0	8448	0.0477	0.8909	0.9515	0.9202	103	0.9172	0.9064	0.9118	171	0.9697	0.9771	0.9734	131	0.9270	0.9407	0.9338	0.9865
0.0108	89.0	8544	0.0473	0.8909	0.9515	0.9202	103	0.9118	0.9064	0.9091	171	0.9697	0.9771	0.9734	131	0.9248	0.9407	0.9327	0.9859
0.0107	90.0	8640	0.0482	0.8981	0.9417	0.9194	103	0.9118	0.9064	0.9091	171	0.9697	0.9771	0.9734	131	0.9268	0.9383	0.9325	0.9859
0.0097	91.0	8736	0.0480	0.8991	0.9515	0.9245	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9223	0.9383	0.9302	0.9865
0.0104	92.0	8832	0.0477	0.8899	0.9417	0.9151	103	0.9176	0.9123	0.9150	171	0.9697	0.9771	0.9734	131	0.9270	0.9407	0.9338	0.9870
0.0101	93.0	8928	0.0479	0.8899	0.9417	0.9151	103	0.9176	0.9123	0.9150	171	0.9697	0.9771	0.9734	131	0.9270	0.9407	0.9338	0.9867
0.0099	94.0	9024	0.0488	0.8818	0.9417	0.9108	103	0.9107	0.8947	0.9027	171	0.9697	0.9771	0.9734	131	0.9220	0.9333	0.9276	0.9856
0.0098	95.0	9120	0.0473	0.8899	0.9417	0.9151	103	0.9118	0.9064	0.9091	171	0.9697	0.9771	0.9734	131	0.9246	0.9383	0.9314	0.9867
0.0085	96.0	9216	0.0475	0.8899	0.9417	0.9151	103	0.9118	0.9064	0.9091	171	0.9697	0.9771	0.9734	131	0.9246	0.9383	0.9314	0.9867
0.0096	97.0	9312	0.0476	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9862
0.0097	98.0	9408	0.0477	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9859
0.0087	99.0	9504	0.0478	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9859
0.0085	100.0	9600	0.0476	0.8899	0.9417	0.9151	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9199	0.9358	0.9278	0.9859

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-seq_bn-rf16-4

nerui-seq_bn-rf16-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-seq_bn-rf16-4