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apwic/
nerui-lora-r16-2

Name: nerui-lora-r16-2
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-lora-r16-2

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: 0.0417
Location Precision: 0.8713
Location Recall: 0.9462
Location F1: 0.9072
Location Number: 93
Organization Precision: 0.8909
Organization Recall: 0.8855
Organization F1: 0.8882
Organization Number: 166
Person Precision: 0.9787
Person Recall: 0.9718
Person F1: 0.9753
Person Number: 142
Overall Precision: 0.9165
Overall Recall: 0.9302
Overall F1: 0.9233
Overall Accuracy: 0.9868

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
1.0607	1.0	96	0.6772	0.0	0.0	0.0	93	0.0	0.0	0.0	166	0.0	0.0	0.0	142	0.0	0.0	0.0	0.8343
0.6351	2.0	192	0.5251	0.0	0.0	0.0	93	0.5	0.0120	0.0235	166	0.0	0.0	0.0	142	0.3333	0.0050	0.0098	0.8348
0.4897	3.0	288	0.3649	0.0	0.0	0.0	93	0.3529	0.2169	0.2687	166	0.3286	0.3239	0.3262	142	0.3267	0.2045	0.2515	0.8763
0.335	4.0	384	0.2323	0.3684	0.3011	0.3314	93	0.5099	0.6205	0.5598	166	0.5683	0.7324	0.6400	142	0.5098	0.5860	0.5452	0.9289
0.2342	5.0	480	0.1642	0.5895	0.6022	0.5957	93	0.6396	0.7590	0.6942	166	0.8269	0.9085	0.8658	142	0.6942	0.7756	0.7326	0.9564
0.1832	6.0	576	0.1316	0.7027	0.8387	0.7647	93	0.7432	0.8193	0.7794	166	0.9257	0.9648	0.9448	142	0.7941	0.8753	0.8327	0.9657
0.1526	7.0	672	0.1085	0.7692	0.8602	0.8122	93	0.7433	0.8373	0.7875	166	0.9079	0.9718	0.9388	142	0.8059	0.8903	0.8460	0.9690
0.136	8.0	768	0.0910	0.75	0.8710	0.8060	93	0.8011	0.8494	0.8246	166	0.9262	0.9718	0.9485	142	0.8314	0.8978	0.8633	0.9734
0.1234	9.0	864	0.0817	0.7981	0.8925	0.8426	93	0.8229	0.8675	0.8446	166	0.9133	0.9648	0.9384	142	0.8485	0.9077	0.8771	0.9753
0.1123	10.0	960	0.0774	0.7981	0.8925	0.8426	93	0.8207	0.9096	0.8629	166	0.9388	0.9718	0.9550	142	0.8552	0.9277	0.8900	0.9772
0.1042	11.0	1056	0.0683	0.8039	0.8817	0.8410	93	0.8371	0.8976	0.8663	166	0.9448	0.9648	0.9547	142	0.8659	0.9177	0.8910	0.9789
0.1	12.0	1152	0.0661	0.8317	0.9032	0.8660	93	0.8436	0.9096	0.8754	166	0.9514	0.9648	0.9580	142	0.8774	0.9277	0.9018	0.9800
0.0949	13.0	1248	0.0622	0.8416	0.9140	0.8763	93	0.8571	0.9036	0.8798	166	0.9580	0.9648	0.9614	142	0.8878	0.9277	0.9073	0.9811
0.091	14.0	1344	0.0597	0.8173	0.9140	0.8629	93	0.8788	0.8735	0.8761	166	0.9580	0.9648	0.9614	142	0.8908	0.9152	0.9028	0.9802
0.0852	15.0	1440	0.0593	0.84	0.9032	0.8705	93	0.8306	0.9157	0.8711	166	0.9650	0.9718	0.9684	142	0.8779	0.9327	0.9045	0.9800
0.0874	16.0	1536	0.0591	0.7838	0.9355	0.8529	93	0.8538	0.8795	0.8665	166	0.9514	0.9648	0.9580	142	0.8685	0.9227	0.8948	0.9797
0.0817	17.0	1632	0.0538	0.8350	0.9247	0.8776	93	0.8876	0.9036	0.8955	166	0.9580	0.9648	0.9614	142	0.8988	0.9302	0.9142	0.9830
0.0784	18.0	1728	0.0511	0.8350	0.9247	0.8776	93	0.8830	0.9096	0.8961	166	0.9580	0.9648	0.9614	142	0.8969	0.9327	0.9144	0.9833
0.0764	19.0	1824	0.0523	0.7890	0.9247	0.8515	93	0.8841	0.8735	0.8788	166	0.9718	0.9718	0.9718	142	0.8892	0.9202	0.9044	0.9822
0.0735	20.0	1920	0.0524	0.8018	0.9570	0.8725	93	0.8889	0.8675	0.8780	166	0.9718	0.9718	0.9718	142	0.8940	0.9252	0.9093	0.9819
0.074	21.0	2016	0.0519	0.8	0.9462	0.8670	93	0.8788	0.8735	0.8761	166	0.9718	0.9718	0.9718	142	0.8897	0.9252	0.9071	0.9822
0.0695	22.0	2112	0.0529	0.7857	0.9462	0.8585	93	0.8353	0.8554	0.8452	166	0.9718	0.9718	0.9718	142	0.8679	0.9177	0.8921	0.9805
0.0673	23.0	2208	0.0519	0.8056	0.9355	0.8657	93	0.9045	0.8554	0.8793	166	0.9718	0.9718	0.9718	142	0.9017	0.9152	0.9084	0.9824
0.0677	24.0	2304	0.0530	0.7982	0.9355	0.8614	93	0.9045	0.8554	0.8793	166	0.9718	0.9718	0.9718	142	0.8995	0.9152	0.9073	0.9811
0.0649	25.0	2400	0.0501	0.8018	0.9570	0.8725	93	0.8994	0.8614	0.8800	166	0.9718	0.9718	0.9718	142	0.8981	0.9227	0.9102	0.9822
0.0647	26.0	2496	0.0478	0.8365	0.9355	0.8832	93	0.9057	0.8675	0.8862	166	0.9718	0.9718	0.9718	142	0.9111	0.9202	0.9156	0.9838
0.0579	27.0	2592	0.0466	0.8208	0.9355	0.8744	93	0.8963	0.8855	0.8909	166	0.9718	0.9718	0.9718	142	0.9029	0.9277	0.9151	0.9835
0.0627	28.0	2688	0.0488	0.8131	0.9355	0.8700	93	0.8855	0.8855	0.8855	166	0.9718	0.9718	0.9718	142	0.8964	0.9277	0.9118	0.9819
0.0601	29.0	2784	0.0487	0.8131	0.9355	0.8700	93	0.8882	0.9096	0.8988	166	0.9718	0.9718	0.9718	142	0.8974	0.9377	0.9171	0.9827
0.0575	30.0	2880	0.0459	0.8286	0.9355	0.8788	93	0.8922	0.8976	0.8949	166	0.9718	0.9718	0.9718	142	0.9034	0.9327	0.9178	0.9833
0.0569	31.0	2976	0.0455	0.8073	0.9462	0.8713	93	0.8951	0.8735	0.8841	166	0.9718	0.9718	0.9718	142	0.8983	0.9252	0.9115	0.9841
0.0548	32.0	3072	0.0445	0.8224	0.9462	0.88	93	0.8889	0.8675	0.8780	166	0.9718	0.9718	0.9718	142	0.9002	0.9227	0.9113	0.9846
0.0528	33.0	3168	0.0471	0.7946	0.9570	0.8683	93	0.8944	0.8675	0.8807	166	0.9858	0.9789	0.9823	142	0.8986	0.9277	0.9129	0.9827
0.0533	34.0	3264	0.0445	0.8073	0.9462	0.8713	93	0.8802	0.8855	0.8829	166	0.9789	0.9789	0.9789	142	0.8947	0.9327	0.9133	0.9833
0.0503	35.0	3360	0.0425	0.8286	0.9355	0.8788	93	0.8922	0.8976	0.8949	166	0.9718	0.9718	0.9718	142	0.9034	0.9327	0.9178	0.9852
0.0531	36.0	3456	0.0447	0.7928	0.9462	0.8627	93	0.8957	0.8795	0.8875	166	0.9648	0.9648	0.9648	142	0.8918	0.9252	0.9082	0.9830
0.0493	37.0	3552	0.0442	0.8365	0.9355	0.8832	93	0.8970	0.8916	0.8943	166	0.9718	0.9718	0.9718	142	0.9075	0.9302	0.9187	0.9841
0.05	38.0	3648	0.0423	0.87	0.9355	0.9016	93	0.9042	0.9096	0.9069	166	0.9718	0.9718	0.9718	142	0.9193	0.9377	0.9284	0.9857
0.0489	39.0	3744	0.0416	0.8529	0.9355	0.8923	93	0.8994	0.9157	0.9075	166	0.9718	0.9718	0.9718	142	0.9128	0.9401	0.9263	0.9855
0.0481	40.0	3840	0.0411	0.8544	0.9462	0.8980	93	0.9068	0.8795	0.8930	166	0.9718	0.9718	0.9718	142	0.9163	0.9277	0.9219	0.9852
0.0462	41.0	3936	0.0429	0.8286	0.9355	0.8788	93	0.9036	0.9036	0.9036	166	0.9787	0.9718	0.9753	142	0.9102	0.9352	0.9225	0.9855
0.0468	42.0	4032	0.0435	0.8302	0.9462	0.8844	93	0.9030	0.8976	0.9003	166	0.9858	0.9789	0.9823	142	0.9126	0.9377	0.9250	0.9846
0.0469	43.0	4128	0.0423	0.8878	0.9355	0.9110	93	0.8976	0.8976	0.8976	166	0.9858	0.9789	0.9823	142	0.9259	0.9352	0.9305	0.9860
0.0472	44.0	4224	0.0460	0.8148	0.9462	0.8756	93	0.8938	0.8614	0.8773	166	0.9718	0.9718	0.9718	142	0.9	0.9202	0.9100	0.9830
0.0468	45.0	4320	0.0420	0.8713	0.9462	0.9072	93	0.9062	0.8735	0.8896	166	0.9858	0.9789	0.9823	142	0.9254	0.9277	0.9265	0.9852
0.0453	46.0	4416	0.0425	0.8462	0.9462	0.8934	93	0.8994	0.8614	0.8800	166	0.9718	0.9718	0.9718	142	0.9111	0.9202	0.9156	0.9852
0.0428	47.0	4512	0.0432	0.8788	0.9355	0.9062	93	0.8902	0.9277	0.9086	166	0.9787	0.9718	0.9753	142	0.9177	0.9451	0.9312	0.9855
0.043	48.0	4608	0.0433	0.8381	0.9462	0.8889	93	0.8924	0.8494	0.8704	166	0.9718	0.9718	0.9718	142	0.9062	0.9152	0.9107	0.9841
0.0443	49.0	4704	0.0437	0.8529	0.9355	0.8923	93	0.8929	0.9036	0.8982	166	0.9648	0.9648	0.9648	142	0.9078	0.9327	0.9200	0.9846
0.0466	50.0	4800	0.0430	0.8627	0.9462	0.9026	93	0.8922	0.8976	0.8949	166	0.9787	0.9718	0.9753	142	0.9146	0.9352	0.9248	0.9860
0.0419	51.0	4896	0.0430	0.8462	0.9462	0.8934	93	0.8951	0.8735	0.8841	166	0.9787	0.9718	0.9753	142	0.9115	0.9252	0.9183	0.9852
0.0421	52.0	4992	0.0404	0.9158	0.9355	0.9255	93	0.8953	0.9277	0.9112	166	0.9787	0.9718	0.9753	142	0.9289	0.9451	0.9370	0.9874
0.0409	53.0	5088	0.0431	0.8462	0.9462	0.8934	93	0.8982	0.9036	0.9009	166	0.9787	0.9718	0.9753	142	0.9126	0.9377	0.9250	0.9857
0.0391	54.0	5184	0.0417	0.8969	0.9355	0.9158	93	0.9012	0.9337	0.9172	166	0.9787	0.9718	0.9753	142	0.9268	0.9476	0.9371	0.9868
0.0383	55.0	5280	0.0402	0.8980	0.9462	0.9215	93	0.9053	0.9217	0.9134	166	0.9787	0.9718	0.9753	142	0.9289	0.9451	0.9370	0.9877
0.0399	56.0	5376	0.0431	0.8627	0.9462	0.9026	93	0.9048	0.9157	0.9102	166	0.9787	0.9718	0.9753	142	0.9197	0.9426	0.9310	0.9855
0.04	57.0	5472	0.0425	0.8544	0.9462	0.8980	93	0.9024	0.8916	0.8970	166	0.9787	0.9718	0.9753	142	0.9167	0.9327	0.9246	0.9855
0.04	58.0	5568	0.0422	0.8713	0.9462	0.9072	93	0.9146	0.9036	0.9091	166	0.9787	0.9718	0.9753	142	0.9261	0.9377	0.9318	0.9868
0.0372	59.0	5664	0.0425	0.8713	0.9462	0.9072	93	0.9036	0.9036	0.9036	166	0.9787	0.9718	0.9753	142	0.9216	0.9377	0.9295	0.9863
0.0384	60.0	5760	0.0422	0.8713	0.9462	0.9072	93	0.9146	0.9036	0.9091	166	0.9787	0.9718	0.9753	142	0.9261	0.9377	0.9318	0.9866
0.0379	61.0	5856	0.0402	0.8627	0.9462	0.9026	93	0.9091	0.9036	0.9063	166	0.9787	0.9718	0.9753	142	0.9216	0.9377	0.9295	0.9877
0.0362	62.0	5952	0.0387	0.8889	0.9462	0.9167	93	0.9036	0.9036	0.9036	166	0.9648	0.9648	0.9648	142	0.9214	0.9352	0.9282	0.9871
0.036	63.0	6048	0.0424	0.8381	0.9462	0.8889	93	0.9030	0.8976	0.9003	166	0.9787	0.9718	0.9753	142	0.9124	0.9352	0.9236	0.9852
0.036	64.0	6144	0.0404	0.88	0.9462	0.9119	93	0.9024	0.8916	0.8970	166	0.9580	0.9648	0.9614	142	0.9165	0.9302	0.9233	0.9857
0.033	65.0	6240	0.0419	0.8544	0.9462	0.8980	93	0.9030	0.8976	0.9003	166	0.9787	0.9718	0.9753	142	0.9169	0.9352	0.9259	0.9857
0.0348	66.0	6336	0.0396	0.88	0.9462	0.9119	93	0.9024	0.8916	0.8970	166	0.9787	0.9718	0.9753	142	0.9235	0.9327	0.9280	0.9868
0.0346	67.0	6432	0.0410	0.8627	0.9462	0.9026	93	0.8862	0.8916	0.8889	166	0.9648	0.9648	0.9648	142	0.9075	0.9302	0.9187	0.9849
0.0337	68.0	6528	0.0416	0.8544	0.9462	0.8980	93	0.9030	0.8976	0.9003	166	0.9787	0.9718	0.9753	142	0.9169	0.9352	0.9259	0.9857
0.0355	69.0	6624	0.0418	0.8627	0.9462	0.9026	93	0.8909	0.8855	0.8882	166	0.9787	0.9718	0.9753	142	0.9142	0.9302	0.9221	0.9855
0.0337	70.0	6720	0.0408	0.8713	0.9462	0.9072	93	0.9146	0.9036	0.9091	166	0.9718	0.9718	0.9718	142	0.9238	0.9377	0.9307	0.9863
0.0351	71.0	6816	0.0411	0.8713	0.9462	0.9072	93	0.9152	0.9096	0.9124	166	0.9787	0.9718	0.9753	142	0.9263	0.9401	0.9332	0.9860
0.0337	72.0	6912	0.0411	0.9072	0.9462	0.9263	93	0.8929	0.9036	0.8982	166	0.9787	0.9718	0.9753	142	0.9261	0.9377	0.9318	0.9866
0.0317	73.0	7008	0.0415	0.8713	0.9462	0.9072	93	0.9036	0.9036	0.9036	166	0.9787	0.9718	0.9753	142	0.9216	0.9377	0.9295	0.9860
0.0308	74.0	7104	0.0442	0.8558	0.9570	0.9036	93	0.9202	0.9036	0.9119	166	0.9787	0.9718	0.9753	142	0.9240	0.9401	0.9320	0.9860
0.0331	75.0	7200	0.0416	0.9072	0.9462	0.9263	93	0.9053	0.9217	0.9134	166	0.9787	0.9718	0.9753	142	0.9312	0.9451	0.9381	0.9879
0.0307	76.0	7296	0.0426	0.8725	0.9570	0.9128	93	0.8963	0.8855	0.8909	166	0.9787	0.9718	0.9753	142	0.9189	0.9327	0.9257	0.9860
0.0311	77.0	7392	0.0411	0.8889	0.9462	0.9167	93	0.8869	0.8976	0.8922	166	0.9787	0.9718	0.9753	142	0.9191	0.9352	0.9271	0.9871
0.0321	78.0	7488	0.0421	0.8713	0.9462	0.9072	93	0.8862	0.8916	0.8889	166	0.9787	0.9718	0.9753	142	0.9144	0.9327	0.9235	0.9863
0.0314	79.0	7584	0.0419	0.88	0.9462	0.9119	93	0.8869	0.8976	0.8922	166	0.9787	0.9718	0.9753	142	0.9169	0.9352	0.9259	0.9866
0.0327	80.0	7680	0.0420	0.88	0.9462	0.9119	93	0.9096	0.9096	0.9096	166	0.9787	0.9718	0.9753	142	0.9263	0.9401	0.9332	0.9868
0.0338	81.0	7776	0.0423	0.8713	0.9462	0.9072	93	0.9091	0.9036	0.9063	166	0.9787	0.9718	0.9753	142	0.9238	0.9377	0.9307	0.9871
0.0326	82.0	7872	0.0430	0.8713	0.9462	0.9072	93	0.9080	0.8916	0.8997	166	0.9787	0.9718	0.9753	142	0.9235	0.9327	0.9280	0.9857
0.0311	83.0	7968	0.0420	0.8889	0.9462	0.9167	93	0.8970	0.8916	0.8943	166	0.9787	0.9718	0.9753	142	0.9235	0.9327	0.9280	0.9857
0.0319	84.0	8064	0.0435	0.8462	0.9462	0.8934	93	0.8970	0.8916	0.8943	166	0.9787	0.9718	0.9753	142	0.9122	0.9327	0.9223	0.9855
0.0312	85.0	8160	0.0414	0.88	0.9462	0.9119	93	0.8909	0.8855	0.8882	166	0.9787	0.9718	0.9753	142	0.9187	0.9302	0.9244	0.9863
0.0313	86.0	8256	0.0418	0.88	0.9462	0.9119	93	0.8862	0.8916	0.8889	166	0.9787	0.9718	0.9753	142	0.9167	0.9327	0.9246	0.9866
0.0315	87.0	8352	0.0414	0.88	0.9462	0.9119	93	0.8916	0.8916	0.8916	166	0.9787	0.9718	0.9753	142	0.9189	0.9327	0.9257	0.9868
0.0314	88.0	8448	0.0415	0.88	0.9462	0.9119	93	0.9024	0.8916	0.8970	166	0.9787	0.9718	0.9753	142	0.9235	0.9327	0.9280	0.9866
0.0301	89.0	8544	0.0416	0.88	0.9462	0.9119	93	0.8970	0.8916	0.8943	166	0.9787	0.9718	0.9753	142	0.9212	0.9327	0.9269	0.9868
0.0303	90.0	8640	0.0410	0.88	0.9462	0.9119	93	0.9030	0.8976	0.9003	166	0.9787	0.9718	0.9753	142	0.9236	0.9352	0.9294	0.9866
0.0292	91.0	8736	0.0412	0.8713	0.9462	0.9072	93	0.8909	0.8855	0.8882	166	0.9787	0.9718	0.9753	142	0.9165	0.9302	0.9233	0.9863
0.0292	92.0	8832	0.0424	0.88	0.9462	0.9119	93	0.9080	0.8916	0.8997	166	0.9787	0.9718	0.9753	142	0.9257	0.9327	0.9292	0.9868
0.0295	93.0	8928	0.0426	0.88	0.9462	0.9119	93	0.9080	0.8916	0.8997	166	0.9787	0.9718	0.9753	142	0.9257	0.9327	0.9292	0.9866
0.0304	94.0	9024	0.0422	0.88	0.9462	0.9119	93	0.8963	0.8855	0.8909	166	0.9787	0.9718	0.9753	142	0.9210	0.9302	0.9256	0.9866
0.0304	95.0	9120	0.0415	0.8713	0.9462	0.9072	93	0.8855	0.8855	0.8855	166	0.9787	0.9718	0.9753	142	0.9142	0.9302	0.9221	0.9866
0.0312	96.0	9216	0.0415	0.88	0.9462	0.9119	93	0.8862	0.8916	0.8889	166	0.9787	0.9718	0.9753	142	0.9167	0.9327	0.9246	0.9868
0.0291	97.0	9312	0.0418	0.8713	0.9462	0.9072	93	0.8855	0.8855	0.8855	166	0.9787	0.9718	0.9753	142	0.9142	0.9302	0.9221	0.9866
0.0306	98.0	9408	0.0417	0.88	0.9462	0.9119	93	0.8916	0.8916	0.8916	166	0.9787	0.9718	0.9753	142	0.9189	0.9327	0.9257	0.9871
0.0293	99.0	9504	0.0417	0.8713	0.9462	0.9072	93	0.8909	0.8855	0.8882	166	0.9787	0.9718	0.9753	142	0.9165	0.9302	0.9233	0.9868
0.0302	100.0	9600	0.0417	0.8713	0.9462	0.9072	93	0.8909	0.8855	0.8882	166	0.9787	0.9718	0.9753	142	0.9165	0.9302	0.9233	0.9868

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-lora-r16-2

nerui-lora-r16-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-lora-r16-2