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apwic/
nerui-seq_bn-rf16-2

Name: nerui-seq_bn-rf16-2
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-seq_bn-rf16-2

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: 0.0508
Location Precision: 0.8835
Location Recall: 0.9785
Location F1: 0.9286
Location Number: 93
Organization Precision: 0.9268
Organization Recall: 0.9157
Organization F1: 0.9212
Organization Number: 166
Person Precision: 0.9789
Person Recall: 0.9789
Person F1: 0.9789
Person Number: 142
Overall Precision: 0.9340
Overall Recall: 0.9526
Overall F1: 0.9432
Overall Accuracy: 0.9877

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
0.8344	1.0	96	0.5347	0.0	0.0	0.0	93	0.1667	0.0060	0.0116	166	0.0	0.0	0.0	142	0.1111	0.0025	0.0049	0.8348
0.4476	2.0	192	0.3089	0.3061	0.1613	0.2113	93	0.3575	0.4759	0.4083	166	0.3249	0.5423	0.4063	142	0.3373	0.4264	0.3767	0.9001
0.2971	3.0	288	0.2109	0.3962	0.4516	0.4221	93	0.5707	0.7048	0.6307	166	0.6630	0.8451	0.7430	142	0.5671	0.6958	0.6249	0.9438
0.21	4.0	384	0.1358	0.5741	0.6667	0.6169	93	0.6935	0.7771	0.7330	166	0.8701	0.9437	0.9054	142	0.7254	0.8105	0.7656	0.9627
0.1444	5.0	480	0.0974	0.7064	0.8280	0.7624	93	0.7545	0.7590	0.7568	166	0.9054	0.9437	0.9241	142	0.7948	0.8404	0.8170	0.9701
0.1206	6.0	576	0.0884	0.7456	0.9140	0.8213	93	0.7989	0.8373	0.8176	166	0.9448	0.9648	0.9547	142	0.8337	0.9002	0.8657	0.9739
0.1044	7.0	672	0.0804	0.7748	0.9247	0.8431	93	0.8056	0.8735	0.8382	166	0.9388	0.9718	0.9550	142	0.8425	0.9202	0.8796	0.9761
0.0937	8.0	768	0.0711	0.7727	0.9140	0.8374	93	0.8352	0.8855	0.8596	166	0.9384	0.9648	0.9514	142	0.8542	0.9202	0.8860	0.9786
0.0906	9.0	864	0.0635	0.8365	0.9355	0.8832	93	0.8295	0.8795	0.8538	166	0.9388	0.9718	0.9550	142	0.8689	0.9252	0.8961	0.9805
0.086	10.0	960	0.0629	0.8190	0.9247	0.8687	93	0.8232	0.8976	0.8588	166	0.9257	0.9648	0.9448	142	0.8571	0.9277	0.8910	0.9802
0.0759	11.0	1056	0.0562	0.8969	0.9355	0.9158	93	0.8278	0.8976	0.8613	166	0.9320	0.9648	0.9481	142	0.8797	0.9302	0.9042	0.9808
0.071	12.0	1152	0.0537	0.8673	0.9140	0.8901	93	0.8361	0.9217	0.8768	166	0.9384	0.9648	0.9514	142	0.8782	0.9352	0.9058	0.9819
0.0669	13.0	1248	0.0504	0.88	0.9462	0.9119	93	0.8765	0.8976	0.8869	166	0.9384	0.9648	0.9514	142	0.8990	0.9327	0.9155	0.9841
0.0639	14.0	1344	0.0516	0.8462	0.9462	0.8934	93	0.8810	0.8916	0.8862	166	0.9517	0.9718	0.9617	142	0.8969	0.9327	0.9144	0.9833
0.061	15.0	1440	0.0463	0.8788	0.9355	0.9062	93	0.8678	0.9096	0.8882	166	0.9583	0.9718	0.9650	142	0.9017	0.9377	0.9193	0.9855
0.0594	16.0	1536	0.0477	0.8627	0.9462	0.9026	93	0.8678	0.9096	0.8882	166	0.9650	0.9718	0.9684	142	0.8998	0.9401	0.9195	0.9852
0.0567	17.0	1632	0.0467	0.8627	0.9462	0.9026	93	0.8571	0.9036	0.8798	166	0.9650	0.9718	0.9684	142	0.8952	0.9377	0.9160	0.9844
0.0526	18.0	1728	0.0412	0.9263	0.9462	0.9362	93	0.8786	0.9157	0.8968	166	0.9650	0.9718	0.9684	142	0.9197	0.9426	0.9310	0.9871
0.05	19.0	1824	0.0427	0.8990	0.9570	0.9271	93	0.8935	0.9096	0.9015	166	0.9650	0.9718	0.9684	142	0.9197	0.9426	0.9310	0.9868
0.0474	20.0	1920	0.0438	0.8725	0.9570	0.9128	93	0.8701	0.9277	0.8980	166	0.9650	0.9718	0.9684	142	0.9028	0.9501	0.9259	0.9855
0.0472	21.0	2016	0.0415	0.9	0.9677	0.9326	93	0.8844	0.9217	0.9027	166	0.9650	0.9718	0.9684	142	0.9159	0.9501	0.9327	0.9874
0.0426	22.0	2112	0.0416	0.89	0.9570	0.9223	93	0.8686	0.9157	0.8915	166	0.9650	0.9718	0.9684	142	0.9067	0.9451	0.9255	0.9868
0.0422	23.0	2208	0.0421	0.8911	0.9677	0.9278	93	0.9036	0.9036	0.9036	166	0.9650	0.9718	0.9684	142	0.9220	0.9426	0.9322	0.9868
0.0418	24.0	2304	0.0450	0.8738	0.9677	0.9184	93	0.9096	0.9096	0.9096	166	0.9650	0.9718	0.9684	142	0.9199	0.9451	0.9323	0.9849
0.038	25.0	2400	0.0422	0.8812	0.9570	0.9175	93	0.8786	0.9157	0.8968	166	0.9650	0.9718	0.9684	142	0.9089	0.9451	0.9267	0.9852
0.037	26.0	2496	0.0401	0.8812	0.9570	0.9175	93	0.8686	0.9157	0.8915	166	0.9650	0.9718	0.9684	142	0.9045	0.9451	0.9244	0.9857
0.0346	27.0	2592	0.0395	0.8812	0.9570	0.9175	93	0.8935	0.9096	0.9015	166	0.9650	0.9718	0.9684	142	0.9153	0.9426	0.9287	0.9868
0.0363	28.0	2688	0.0427	0.8889	0.9462	0.9167	93	0.8539	0.9157	0.8837	166	0.9718	0.9718	0.9718	142	0.9021	0.9426	0.9220	0.9846
0.0366	29.0	2784	0.0419	0.8911	0.9677	0.9278	93	0.9048	0.9157	0.9102	166	0.9650	0.9718	0.9684	142	0.9223	0.9476	0.9348	0.9874
0.0323	30.0	2880	0.0411	0.89	0.9570	0.9223	93	0.8953	0.9277	0.9112	166	0.9722	0.9859	0.9790	142	0.9207	0.9551	0.9376	0.9857
0.0325	31.0	2976	0.0397	0.8824	0.9677	0.9231	93	0.8935	0.9096	0.9015	166	0.9650	0.9718	0.9684	142	0.9155	0.9451	0.9301	0.9871
0.0295	32.0	3072	0.0414	0.8812	0.9570	0.9175	93	0.8779	0.9096	0.8935	166	0.9718	0.9718	0.9718	142	0.9108	0.9426	0.9265	0.9857
0.0288	33.0	3168	0.0425	0.8641	0.9570	0.9082	93	0.8922	0.8976	0.8949	166	0.9650	0.9718	0.9684	142	0.9104	0.9377	0.9238	0.9844
0.0288	34.0	3264	0.0404	0.89	0.9570	0.9223	93	0.8793	0.9217	0.9	166	0.9789	0.9789	0.9789	142	0.9159	0.9501	0.9327	0.9857
0.0264	35.0	3360	0.0397	0.9082	0.9570	0.9319	93	0.8941	0.9157	0.9048	166	0.9650	0.9718	0.9684	142	0.9221	0.9451	0.9335	0.9868
0.028	36.0	3456	0.0431	0.8824	0.9677	0.9231	93	0.9198	0.8976	0.9085	166	0.9650	0.9718	0.9684	142	0.9263	0.9401	0.9332	0.9860
0.0245	37.0	3552	0.0393	0.8980	0.9462	0.9215	93	0.8895	0.9217	0.9053	166	0.9650	0.9718	0.9684	142	0.9177	0.9451	0.9312	0.9868
0.0238	38.0	3648	0.0424	0.9	0.9677	0.9326	93	0.9107	0.9217	0.9162	166	0.9650	0.9718	0.9684	142	0.9270	0.9501	0.9384	0.9866
0.0238	39.0	3744	0.0411	0.8812	0.9570	0.9175	93	0.9102	0.9157	0.9129	166	0.9650	0.9718	0.9684	142	0.9221	0.9451	0.9335	0.9871
0.0233	40.0	3840	0.0407	0.8990	0.9570	0.9271	93	0.9	0.9217	0.9107	166	0.9650	0.9718	0.9684	142	0.9223	0.9476	0.9348	0.9863
0.023	41.0	3936	0.0403	0.8889	0.9462	0.9167	93	0.8736	0.9157	0.8941	166	0.9789	0.9789	0.9789	142	0.9133	0.9451	0.9289	0.9863
0.0217	42.0	4032	0.0416	0.89	0.9570	0.9223	93	0.9036	0.9036	0.9036	166	0.9650	0.9718	0.9684	142	0.9218	0.9401	0.9309	0.9863
0.0213	43.0	4128	0.0427	0.89	0.9570	0.9223	93	0.9198	0.8976	0.9085	166	0.9650	0.9718	0.9684	142	0.9284	0.9377	0.9330	0.9855
0.0216	44.0	4224	0.0420	0.9091	0.9677	0.9375	93	0.9102	0.9157	0.9129	166	0.9650	0.9718	0.9684	142	0.9291	0.9476	0.9383	0.9871
0.02	45.0	4320	0.0422	0.9010	0.9785	0.9381	93	0.9207	0.9096	0.9152	166	0.9650	0.9718	0.9684	142	0.9314	0.9476	0.9394	0.9863
0.0187	46.0	4416	0.0419	0.9010	0.9785	0.9381	93	0.8982	0.9036	0.9009	166	0.9718	0.9718	0.9718	142	0.9244	0.9451	0.9346	0.9874
0.0195	47.0	4512	0.0416	0.8788	0.9355	0.9062	93	0.8837	0.9157	0.8994	166	0.9789	0.9789	0.9789	142	0.9153	0.9426	0.9287	0.9868
0.0183	48.0	4608	0.0421	0.8911	0.9677	0.9278	93	0.9157	0.9157	0.9157	166	0.9650	0.9718	0.9684	142	0.9268	0.9476	0.9371	0.9871
0.0182	49.0	4704	0.0412	0.8713	0.9462	0.9072	93	0.8935	0.9096	0.9015	166	0.9789	0.9789	0.9789	142	0.9175	0.9426	0.9299	0.9874
0.0154	50.0	4800	0.0424	0.8990	0.9570	0.9271	93	0.9162	0.9217	0.9189	166	0.9789	0.9789	0.9789	142	0.9338	0.9501	0.9419	0.9879
0.017	51.0	4896	0.0436	0.8725	0.9570	0.9128	93	0.9202	0.9036	0.9119	166	0.9789	0.9789	0.9789	142	0.9287	0.9426	0.9356	0.9868
0.0165	52.0	4992	0.0437	0.8738	0.9677	0.9184	93	0.9107	0.9217	0.9162	166	0.9789	0.9789	0.9789	142	0.9249	0.9526	0.9386	0.9874
0.0161	53.0	5088	0.0460	0.8990	0.9570	0.9271	93	0.9102	0.9157	0.9129	166	0.9789	0.9789	0.9789	142	0.9314	0.9476	0.9394	0.9874
0.0149	54.0	5184	0.0453	0.9	0.9677	0.9326	93	0.9112	0.9277	0.9194	166	0.9720	0.9789	0.9754	142	0.9296	0.9551	0.9422	0.9877
0.0149	55.0	5280	0.0462	0.8824	0.9677	0.9231	93	0.9317	0.9036	0.9174	166	0.9720	0.9789	0.9754	142	0.9335	0.9451	0.9393	0.9860
0.0155	56.0	5376	0.0445	0.8738	0.9677	0.9184	93	0.9102	0.9157	0.9129	166	0.9789	0.9789	0.9789	142	0.9248	0.9501	0.9373	0.9877
0.0151	57.0	5472	0.0473	0.8922	0.9785	0.9333	93	0.8982	0.9036	0.9009	166	0.9789	0.9789	0.9789	142	0.9246	0.9476	0.9360	0.9868
0.0164	58.0	5568	0.0492	0.8835	0.9785	0.9286	93	0.9102	0.9157	0.9129	166	0.9789	0.9789	0.9789	142	0.9272	0.9526	0.9397	0.9871
0.0144	59.0	5664	0.0464	0.9109	0.9892	0.9485	93	0.9268	0.9157	0.9212	166	0.9720	0.9789	0.9754	142	0.9387	0.9551	0.9468	0.9877
0.014	60.0	5760	0.0499	0.875	0.9785	0.9239	93	0.9367	0.8916	0.9136	166	0.9789	0.9789	0.9789	142	0.9356	0.9426	0.9391	0.9863
0.0137	61.0	5856	0.0452	0.9020	0.9892	0.9436	93	0.9152	0.9096	0.9124	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9874
0.0132	62.0	5952	0.0468	0.9020	0.9892	0.9436	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9363	0.9526	0.9444	0.9874
0.0125	63.0	6048	0.0456	0.8835	0.9785	0.9286	93	0.9048	0.9157	0.9102	166	0.9789	0.9789	0.9789	142	0.9249	0.9526	0.9386	0.9877
0.0131	64.0	6144	0.0464	0.9020	0.9892	0.9436	93	0.9317	0.9036	0.9174	166	0.9789	0.9789	0.9789	142	0.9407	0.9501	0.9454	0.9871
0.0119	65.0	6240	0.0464	0.9020	0.9892	0.9436	93	0.9259	0.9036	0.9146	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9874
0.0135	66.0	6336	0.0454	0.8922	0.9785	0.9333	93	0.9202	0.9036	0.9119	166	0.9789	0.9789	0.9789	142	0.9337	0.9476	0.9406	0.9877
0.0115	67.0	6432	0.0471	0.9010	0.9785	0.9381	93	0.9152	0.9096	0.9124	166	0.9720	0.9789	0.9754	142	0.9315	0.9501	0.9407	0.9874
0.0124	68.0	6528	0.0468	0.8922	0.9785	0.9333	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9338	0.9501	0.9419	0.9871
0.0124	69.0	6624	0.0464	0.8835	0.9785	0.9286	93	0.9107	0.9217	0.9162	166	0.9720	0.9789	0.9754	142	0.9251	0.9551	0.9399	0.9877
0.0107	70.0	6720	0.0470	0.8932	0.9892	0.9388	93	0.9325	0.9157	0.9240	166	0.9720	0.9789	0.9754	142	0.9364	0.9551	0.9457	0.9879
0.011	71.0	6816	0.0476	0.8922	0.9785	0.9333	93	0.9226	0.9337	0.9281	166	0.9720	0.9789	0.9754	142	0.9322	0.9601	0.9459	0.9885
0.0102	72.0	6912	0.0485	0.8932	0.9892	0.9388	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9363	0.9526	0.9444	0.9877
0.0096	73.0	7008	0.0485	0.8932	0.9892	0.9388	93	0.9383	0.9157	0.9268	166	0.9789	0.9789	0.9789	142	0.9410	0.9551	0.9480	0.9877
0.0107	74.0	7104	0.0472	0.8835	0.9785	0.9286	93	0.9379	0.9096	0.9235	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9874
0.0119	75.0	7200	0.0504	0.8824	0.9677	0.9231	93	0.9317	0.9036	0.9174	166	0.9789	0.9789	0.9789	142	0.9358	0.9451	0.9404	0.9871
0.0105	76.0	7296	0.0488	0.875	0.9785	0.9239	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9315	0.9501	0.9407	0.9874
0.0102	77.0	7392	0.0504	0.8835	0.9785	0.9286	93	0.9273	0.9217	0.9245	166	0.9789	0.9789	0.9789	142	0.9341	0.9551	0.9445	0.9877
0.0098	78.0	7488	0.0484	0.9020	0.9892	0.9436	93	0.9321	0.9096	0.9207	166	0.9789	0.9789	0.9789	142	0.9409	0.9526	0.9467	0.9885
0.0089	79.0	7584	0.0468	0.8990	0.9570	0.9271	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9358	0.9451	0.9404	0.9882
0.0113	80.0	7680	0.0493	0.8911	0.9677	0.9278	93	0.9152	0.9096	0.9124	166	0.9720	0.9789	0.9754	142	0.9291	0.9476	0.9383	0.9877
0.0093	81.0	7776	0.0489	0.8911	0.9677	0.9278	93	0.9217	0.9217	0.9217	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9879
0.0094	82.0	7872	0.0519	0.8654	0.9677	0.9137	93	0.9136	0.8916	0.9024	166	0.9789	0.9789	0.9789	142	0.9240	0.9401	0.9320	0.9866
0.0104	83.0	7968	0.0508	0.8738	0.9677	0.9184	93	0.9379	0.9096	0.9235	166	0.9789	0.9789	0.9789	142	0.9360	0.9476	0.9418	0.9871
0.0098	84.0	8064	0.0511	0.875	0.9785	0.9239	93	0.9317	0.9036	0.9174	166	0.9789	0.9789	0.9789	142	0.9337	0.9476	0.9406	0.9877
0.0092	85.0	8160	0.0493	0.9020	0.9892	0.9436	93	0.9325	0.9157	0.9240	166	0.9789	0.9789	0.9789	142	0.9410	0.9551	0.9480	0.9888
0.0099	86.0	8256	0.0502	0.8824	0.9677	0.9231	93	0.9383	0.9157	0.9268	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9879
0.0092	87.0	8352	0.0496	0.8738	0.9677	0.9184	93	0.9321	0.9096	0.9207	166	0.9789	0.9789	0.9789	142	0.9337	0.9476	0.9406	0.9874
0.0091	88.0	8448	0.0490	0.8835	0.9785	0.9286	93	0.9321	0.9096	0.9207	166	0.9789	0.9789	0.9789	142	0.9361	0.9501	0.9431	0.9877
0.0091	89.0	8544	0.0497	0.8835	0.9785	0.9286	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9338	0.9501	0.9419	0.9874
0.0093	90.0	8640	0.0487	0.9020	0.9892	0.9436	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9386	0.9526	0.9455	0.9879
0.0096	91.0	8736	0.0500	0.8922	0.9785	0.9333	93	0.9321	0.9096	0.9207	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9879
0.0088	92.0	8832	0.0506	0.8824	0.9677	0.9231	93	0.9259	0.9036	0.9146	166	0.9789	0.9789	0.9789	142	0.9335	0.9451	0.9393	0.9874
0.0088	93.0	8928	0.0510	0.8835	0.9785	0.9286	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9338	0.9501	0.9419	0.9874
0.0089	94.0	9024	0.0513	0.8824	0.9677	0.9231	93	0.9202	0.9036	0.9119	166	0.9789	0.9789	0.9789	142	0.9312	0.9451	0.9381	0.9871
0.0087	95.0	9120	0.0509	0.8824	0.9677	0.9231	93	0.9259	0.9036	0.9146	166	0.9789	0.9789	0.9789	142	0.9335	0.9451	0.9393	0.9874
0.0082	96.0	9216	0.0506	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9789	0.9789	0.9789	142	0.9335	0.9451	0.9393	0.9874
0.0093	97.0	9312	0.0512	0.8835	0.9785	0.9286	93	0.9268	0.9157	0.9212	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9877
0.0083	98.0	9408	0.0509	0.8835	0.9785	0.9286	93	0.9268	0.9157	0.9212	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9877
0.0075	99.0	9504	0.0507	0.8835	0.9785	0.9286	93	0.9268	0.9157	0.9212	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9877
0.0094	100.0	9600	0.0508	0.8835	0.9785	0.9286	93	0.9268	0.9157	0.9212	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9877

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-seq_bn-rf16-2

nerui-seq_bn-rf16-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-seq_bn-rf16-2