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apwic/
nerui-unipelt-2

Name: nerui-unipelt-2
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-unipelt-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: 0.0711
Location Precision: 0.9184
Location Recall: 0.9677
Location F1: 0.9424
Location Number: 93
Organization Precision: 0.9152
Organization Recall: 0.9096
Organization F1: 0.9124
Organization Number: 166
Person Precision: 0.9650
Person Recall: 0.9718
Person F1: 0.9684
Person Number: 142
Overall Precision: 0.9335
Overall Recall: 0.9451
Overall F1: 0.9393
Overall Accuracy: 0.9879

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
0.8565	1.0	96	0.4994	0.0	0.0	0.0	93	0.3333	0.0120	0.0233	166	0.0	0.0	0.0	142	0.2222	0.0050	0.0098	0.8351
0.3861	2.0	192	0.2398	0.3150	0.4301	0.3636	93	0.5410	0.5964	0.5673	166	0.5238	0.7746	0.625	142	0.4788	0.6209	0.5407	0.9309
0.1946	3.0	288	0.1054	0.7742	0.7742	0.7742	93	0.7189	0.8012	0.7578	166	0.9139	0.9718	0.9420	142	0.7995	0.8554	0.8265	0.9695
0.1288	4.0	384	0.0858	0.6412	0.9032	0.75	93	0.7955	0.8434	0.8187	166	0.9456	0.9789	0.9619	142	0.7996	0.9052	0.8491	0.9709
0.0996	5.0	480	0.0602	0.7810	0.8817	0.8283	93	0.8855	0.8855	0.8855	166	0.9720	0.9789	0.9754	142	0.8889	0.9177	0.9031	0.9816
0.0883	6.0	576	0.0645	0.7273	0.9462	0.8224	93	0.9247	0.8133	0.8654	166	0.9653	0.9789	0.9720	142	0.8808	0.9027	0.8916	0.9789
0.0736	7.0	672	0.0552	0.8252	0.9140	0.8673	93	0.8793	0.9217	0.9	166	0.9789	0.9789	0.9789	142	0.8998	0.9401	0.9195	0.9827
0.0677	8.0	768	0.0476	0.8835	0.9785	0.9286	93	0.9157	0.9157	0.9157	166	0.9789	0.9789	0.9789	142	0.9294	0.9526	0.9409	0.9857
0.0588	9.0	864	0.0466	0.8667	0.9785	0.9192	93	0.9325	0.9157	0.9240	166	0.9858	0.9789	0.9823	142	0.9340	0.9526	0.9432	0.9857
0.0558	10.0	960	0.0521	0.7857	0.9462	0.8585	93	0.8862	0.8916	0.8889	166	0.9790	0.9859	0.9825	142	0.8910	0.9377	0.9137	0.9830
0.051	11.0	1056	0.0407	0.8515	0.9247	0.8866	93	0.9172	0.9337	0.9254	166	0.9789	0.9789	0.9789	142	0.9223	0.9476	0.9348	0.9871
0.0456	12.0	1152	0.0446	0.8558	0.9570	0.9036	93	0.9068	0.8795	0.8930	166	0.9720	0.9789	0.9754	142	0.9167	0.9327	0.9246	0.9849
0.0423	13.0	1248	0.0421	0.8571	0.9677	0.9091	93	0.9255	0.8976	0.9113	166	0.9858	0.9789	0.9823	142	0.9287	0.9426	0.9356	0.9866
0.0412	14.0	1344	0.0549	0.7739	0.9570	0.8558	93	0.92	0.8313	0.8734	166	0.9720	0.9789	0.9754	142	0.8971	0.9127	0.9048	0.9811
0.0383	15.0	1440	0.0400	0.8889	0.9462	0.9167	93	0.9212	0.9157	0.9184	166	0.9858	0.9789	0.9823	142	0.9358	0.9451	0.9404	0.9874
0.0341	16.0	1536	0.0489	0.8426	0.9785	0.9055	93	0.9057	0.8675	0.8862	166	0.9789	0.9789	0.9789	142	0.9144	0.9327	0.9235	0.9835
0.0348	17.0	1632	0.0456	0.8571	0.9677	0.9091	93	0.9146	0.9036	0.9091	166	0.9789	0.9789	0.9789	142	0.9221	0.9451	0.9335	0.9857
0.0317	18.0	1728	0.0449	0.8447	0.9355	0.8878	93	0.8841	0.8735	0.8788	166	0.9789	0.9789	0.9789	142	0.9071	0.9252	0.9160	0.9844
0.0282	19.0	1824	0.0486	0.8958	0.9247	0.9101	93	0.9074	0.8855	0.8963	166	0.9720	0.9789	0.9754	142	0.9277	0.9277	0.9277	0.9860
0.0272	20.0	1920	0.0504	0.8641	0.9570	0.9082	93	0.9317	0.9036	0.9174	166	0.9583	0.9718	0.9650	142	0.9240	0.9401	0.9320	0.9844
0.0256	21.0	2016	0.0461	0.8846	0.9892	0.9340	93	0.9259	0.9036	0.9146	166	0.9720	0.9789	0.9754	142	0.9315	0.9501	0.9407	0.9860
0.0246	22.0	2112	0.0486	0.9	0.9677	0.9326	93	0.9048	0.9157	0.9102	166	0.9653	0.9789	0.9720	142	0.9248	0.9501	0.9373	0.9849
0.023	23.0	2208	0.0429	0.8878	0.9355	0.9110	93	0.9107	0.9217	0.9162	166	0.9720	0.9789	0.9754	142	0.9267	0.9451	0.9358	0.9866
0.0234	24.0	2304	0.0489	0.9072	0.9462	0.9263	93	0.9176	0.9398	0.9286	166	0.9720	0.9789	0.9754	142	0.9341	0.9551	0.9445	0.9868
0.0214	25.0	2400	0.0578	0.8824	0.9677	0.9231	93	0.9241	0.8795	0.9012	166	0.9789	0.9789	0.9789	142	0.9328	0.9352	0.9340	0.9852
0.0193	26.0	2496	0.0503	0.91	0.9785	0.9430	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9871
0.0165	27.0	2592	0.0521	0.91	0.9785	0.9430	93	0.9390	0.9277	0.9333	166	0.9720	0.9789	0.9754	142	0.9435	0.9576	0.9505	0.9871
0.0185	28.0	2688	0.0514	0.8980	0.9462	0.9215	93	0.8935	0.9096	0.9015	166	0.9789	0.9789	0.9789	142	0.9242	0.9426	0.9333	0.9857
0.0156	29.0	2784	0.0486	0.8990	0.9570	0.9271	93	0.9091	0.9036	0.9063	166	0.9653	0.9789	0.9720	142	0.9265	0.9426	0.9345	0.9868
0.0159	30.0	2880	0.0513	0.8835	0.9785	0.9286	93	0.8935	0.9096	0.9015	166	0.9653	0.9789	0.9720	142	0.9159	0.9501	0.9327	0.9863
0.0146	31.0	2976	0.0495	0.8911	0.9677	0.9278	93	0.9030	0.8976	0.9003	166	0.9789	0.9789	0.9789	142	0.9265	0.9426	0.9345	0.9868
0.0138	32.0	3072	0.0492	0.89	0.9570	0.9223	93	0.9313	0.8976	0.9141	166	0.9858	0.9789	0.9823	142	0.9401	0.9401	0.9401	0.9885
0.0133	33.0	3168	0.0494	0.9175	0.9570	0.9368	93	0.8982	0.9036	0.9009	166	0.9653	0.9789	0.9720	142	0.9265	0.9426	0.9345	0.9877
0.0138	34.0	3264	0.0502	0.91	0.9785	0.9430	93	0.9383	0.9157	0.9268	166	0.9858	0.9789	0.9823	142	0.9479	0.9526	0.9502	0.9885
0.0135	35.0	3360	0.0503	0.9091	0.9677	0.9375	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9312	0.9451	0.9381	0.9866
0.0123	36.0	3456	0.0552	0.9010	0.9785	0.9381	93	0.9437	0.9096	0.9264	166	0.9789	0.9789	0.9789	142	0.9454	0.9501	0.9478	0.9879
0.0129	37.0	3552	0.0468	0.9375	0.9677	0.9524	93	0.9112	0.9277	0.9194	166	0.9789	0.9789	0.9789	142	0.9410	0.9551	0.9480	0.9890
0.0103	38.0	3648	0.0517	0.91	0.9785	0.9430	93	0.9080	0.8916	0.8997	166	0.9858	0.9789	0.9823	142	0.9356	0.9426	0.9391	0.9882
0.0101	39.0	3744	0.0496	0.89	0.9570	0.9223	93	0.8994	0.9157	0.9075	166	0.9787	0.9718	0.9753	142	0.9244	0.9451	0.9346	0.9882
0.0094	40.0	3840	0.0470	0.9278	0.9677	0.9474	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9429	0.9476	0.9453	0.9890
0.0084	41.0	3936	0.0556	0.9271	0.9570	0.9418	93	0.9042	0.9096	0.9069	166	0.9789	0.9789	0.9789	142	0.9358	0.9451	0.9404	0.9882
0.0094	42.0	4032	0.0609	0.8969	0.9355	0.9158	93	0.9146	0.9036	0.9091	166	0.9580	0.9648	0.9614	142	0.9257	0.9327	0.9292	0.9855
0.0098	43.0	4128	0.0554	0.9286	0.9785	0.9529	93	0.9030	0.8976	0.9003	166	0.9789	0.9789	0.9789	142	0.9358	0.9451	0.9404	0.9882
0.0087	44.0	4224	0.0571	0.9286	0.9785	0.9529	93	0.9152	0.9096	0.9124	166	0.9789	0.9789	0.9789	142	0.9407	0.9501	0.9454	0.9877
0.0084	45.0	4320	0.0642	0.9091	0.9677	0.9375	93	0.9313	0.8976	0.9141	166	0.9789	0.9789	0.9789	142	0.9426	0.9426	0.9426	0.9871
0.0087	46.0	4416	0.0656	0.9091	0.9677	0.9375	93	0.9259	0.9036	0.9146	166	0.9720	0.9789	0.9754	142	0.9381	0.9451	0.9416	0.9860
0.0086	47.0	4512	0.0597	0.9192	0.9785	0.9479	93	0.9091	0.9036	0.9063	166	0.9858	0.9789	0.9823	142	0.9383	0.9476	0.9429	0.9874
0.007	48.0	4608	0.0635	0.8922	0.9785	0.9333	93	0.9080	0.8916	0.8997	166	0.9720	0.9789	0.9754	142	0.9265	0.9426	0.9345	0.9871
0.0079	49.0	4704	0.0611	0.9082	0.9570	0.9319	93	0.8982	0.9036	0.9009	166	0.9720	0.9789	0.9754	142	0.9265	0.9426	0.9345	0.9868
0.0069	50.0	4800	0.0602	0.9082	0.9570	0.9319	93	0.8988	0.9096	0.9042	166	0.9653	0.9789	0.9720	142	0.9244	0.9451	0.9346	0.9868
0.0068	51.0	4896	0.0651	0.9	0.9677	0.9326	93	0.9202	0.9036	0.9119	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9866
0.0054	52.0	4992	0.0533	0.8980	0.9462	0.9215	93	0.9096	0.9096	0.9096	166	0.9653	0.9789	0.9720	142	0.9265	0.9426	0.9345	0.9871
0.0059	53.0	5088	0.0594	0.9271	0.9570	0.9418	93	0.9268	0.9157	0.9212	166	0.9858	0.9789	0.9823	142	0.9476	0.9476	0.9476	0.9888
0.0058	54.0	5184	0.0636	0.9010	0.9785	0.9381	93	0.9136	0.8916	0.9024	166	0.9720	0.9789	0.9754	142	0.9310	0.9426	0.9368	0.9877
0.0063	55.0	5280	0.0577	0.9278	0.9677	0.9474	93	0.9162	0.9217	0.9189	166	0.9789	0.9789	0.9789	142	0.9409	0.9526	0.9467	0.9882
0.0045	56.0	5376	0.0619	0.9082	0.9570	0.9319	93	0.9146	0.9036	0.9091	166	0.9789	0.9789	0.9789	142	0.9356	0.9426	0.9391	0.9879
0.0072	57.0	5472	0.0723	0.9175	0.9570	0.9368	93	0.9255	0.8976	0.9113	166	0.9653	0.9789	0.9720	142	0.9378	0.9401	0.9390	0.9871
0.0059	58.0	5568	0.0681	0.91	0.9785	0.9430	93	0.9313	0.8976	0.9141	166	0.9720	0.9789	0.9754	142	0.9404	0.9451	0.9428	0.9882
0.0048	59.0	5664	0.0655	0.9	0.9677	0.9326	93	0.9259	0.9036	0.9146	166	0.9789	0.9789	0.9789	142	0.9381	0.9451	0.9416	0.9877
0.0046	60.0	5760	0.0759	0.8835	0.9785	0.9286	93	0.9085	0.8976	0.9030	166	0.9789	0.9789	0.9789	142	0.9267	0.9451	0.9358	0.9860
0.0051	61.0	5856	0.0614	0.9381	0.9785	0.9579	93	0.95	0.9157	0.9325	166	0.9789	0.9789	0.9789	142	0.9574	0.9526	0.955	0.9893
0.0046	62.0	5952	0.0698	0.9192	0.9785	0.9479	93	0.9371	0.8976	0.9169	166	0.9789	0.9789	0.9789	142	0.9475	0.9451	0.9463	0.9888
0.0056	63.0	6048	0.0614	0.9286	0.9785	0.9529	93	0.9317	0.9036	0.9174	166	0.9789	0.9789	0.9789	142	0.9476	0.9476	0.9476	0.9888
0.004	64.0	6144	0.0680	0.9192	0.9785	0.9479	93	0.9255	0.8976	0.9113	166	0.9720	0.9789	0.9754	142	0.9404	0.9451	0.9428	0.9882
0.0034	65.0	6240	0.0743	0.9184	0.9677	0.9424	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9429	0.9476	0.9453	0.9877
0.005	66.0	6336	0.0712	0.9192	0.9785	0.9479	93	0.9259	0.9036	0.9146	166	0.9720	0.9789	0.9754	142	0.9406	0.9476	0.9441	0.9879
0.0034	67.0	6432	0.0715	0.9010	0.9785	0.9381	93	0.9085	0.8976	0.9030	166	0.9720	0.9789	0.9754	142	0.9289	0.9451	0.9370	0.9871
0.0044	68.0	6528	0.0722	0.9091	0.9677	0.9375	93	0.9202	0.9036	0.9119	166	0.9720	0.9789	0.9754	142	0.9358	0.9451	0.9404	0.9866
0.0034	69.0	6624	0.0667	0.8990	0.9570	0.9271	93	0.9198	0.8976	0.9085	166	0.9650	0.9718	0.9684	142	0.9307	0.9377	0.9342	0.9868
0.0037	70.0	6720	0.0798	0.8911	0.9677	0.9278	93	0.9193	0.8916	0.9052	166	0.9720	0.9789	0.9754	142	0.9309	0.9401	0.9355	0.9852
0.0038	71.0	6816	0.0615	0.9462	0.9462	0.9462	93	0.9217	0.9217	0.9217	166	0.9650	0.9718	0.9684	142	0.9428	0.9451	0.9440	0.9885
0.0036	72.0	6912	0.0726	0.9	0.9677	0.9326	93	0.9255	0.8976	0.9113	166	0.9720	0.9789	0.9754	142	0.9356	0.9426	0.9391	0.9863
0.004	73.0	7008	0.0673	0.9184	0.9677	0.9424	93	0.9317	0.9036	0.9174	166	0.9720	0.9789	0.9754	142	0.9428	0.9451	0.9440	0.9877
0.0031	74.0	7104	0.0689	0.9175	0.9570	0.9368	93	0.9152	0.9096	0.9124	166	0.9789	0.9789	0.9789	142	0.9381	0.9451	0.9416	0.9863
0.0035	75.0	7200	0.0677	0.9184	0.9677	0.9424	93	0.9259	0.9036	0.9146	166	0.9720	0.9789	0.9754	142	0.9404	0.9451	0.9428	0.9877
0.0025	76.0	7296	0.0668	0.9184	0.9677	0.9424	93	0.9202	0.9036	0.9119	166	0.9720	0.9789	0.9754	142	0.9381	0.9451	0.9416	0.9882
0.0028	77.0	7392	0.0716	0.9184	0.9677	0.9424	93	0.9157	0.9157	0.9157	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9877
0.003	78.0	7488	0.0737	0.9	0.9677	0.9326	93	0.9080	0.8916	0.8997	166	0.9720	0.9789	0.9754	142	0.9286	0.9401	0.9343	0.9860
0.0043	79.0	7584	0.0663	0.9286	0.9785	0.9529	93	0.9152	0.9096	0.9124	166	0.9720	0.9789	0.9754	142	0.9384	0.9501	0.9442	0.9879
0.0025	80.0	7680	0.0699	0.9184	0.9677	0.9424	93	0.9091	0.9036	0.9063	166	0.9720	0.9789	0.9754	142	0.9335	0.9451	0.9393	0.9879
0.0034	81.0	7776	0.0677	0.9278	0.9677	0.9474	93	0.9207	0.9096	0.9152	166	0.9650	0.9718	0.9684	142	0.9381	0.9451	0.9416	0.9868
0.003	82.0	7872	0.0684	0.9381	0.9785	0.9579	93	0.9207	0.9096	0.9152	166	0.9720	0.9789	0.9754	142	0.9431	0.9501	0.9466	0.9877
0.0033	83.0	7968	0.0668	0.9192	0.9785	0.9479	93	0.9085	0.8976	0.9030	166	0.9720	0.9789	0.9754	142	0.9335	0.9451	0.9393	0.9882
0.0033	84.0	8064	0.0656	0.9286	0.9785	0.9529	93	0.9091	0.9036	0.9063	166	0.9720	0.9789	0.9754	142	0.9360	0.9476	0.9418	0.9882
0.0034	85.0	8160	0.0705	0.9184	0.9677	0.9424	93	0.9207	0.9096	0.9152	166	0.9789	0.9789	0.9789	142	0.9406	0.9476	0.9441	0.9877
0.0024	86.0	8256	0.0707	0.9192	0.9785	0.9479	93	0.9146	0.9036	0.9091	166	0.9789	0.9789	0.9789	142	0.9383	0.9476	0.9429	0.9882
0.0033	87.0	8352	0.0658	0.9286	0.9785	0.9529	93	0.9264	0.9096	0.9179	166	0.9789	0.9789	0.9789	142	0.9454	0.9501	0.9478	0.9893
0.0027	88.0	8448	0.0741	0.9184	0.9677	0.9424	93	0.9152	0.9096	0.9124	166	0.9718	0.9718	0.9718	142	0.9358	0.9451	0.9404	0.9877
0.0031	89.0	8544	0.0754	0.9184	0.9677	0.9424	93	0.9091	0.9036	0.9063	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9874
0.0027	90.0	8640	0.0722	0.9082	0.9570	0.9319	93	0.9146	0.9036	0.9091	166	0.9650	0.9718	0.9684	142	0.9309	0.9401	0.9355	0.9877
0.0029	91.0	8736	0.0713	0.9184	0.9677	0.9424	93	0.9202	0.9036	0.9119	166	0.9650	0.9718	0.9684	142	0.9356	0.9426	0.9391	0.9879
0.0032	92.0	8832	0.0720	0.9192	0.9785	0.9479	93	0.9091	0.9036	0.9063	166	0.9650	0.9718	0.9684	142	0.9312	0.9451	0.9381	0.9877
0.0028	93.0	8928	0.0708	0.9184	0.9677	0.9424	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9335	0.9451	0.9393	0.9877
0.0032	94.0	9024	0.0721	0.9091	0.9677	0.9375	93	0.9202	0.9036	0.9119	166	0.9720	0.9789	0.9754	142	0.9358	0.9451	0.9404	0.9882
0.0019	95.0	9120	0.0721	0.9082	0.9570	0.9319	93	0.9091	0.9036	0.9063	166	0.9650	0.9718	0.9684	142	0.9286	0.9401	0.9343	0.9871
0.0031	96.0	9216	0.0707	0.9184	0.9677	0.9424	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9335	0.9451	0.9393	0.9877
0.0024	97.0	9312	0.0704	0.9091	0.9677	0.9375	93	0.9146	0.9036	0.9091	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9874
0.0029	98.0	9408	0.0709	0.9091	0.9677	0.9375	93	0.9146	0.9036	0.9091	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9877
0.002	99.0	9504	0.0713	0.9184	0.9677	0.9424	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9335	0.9451	0.9393	0.9879
0.0017	100.0	9600	0.0711	0.9184	0.9677	0.9424	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9335	0.9451	0.9393	0.9879

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-unipelt-2

nerui-unipelt-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-unipelt-2