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apwic/
nerui-pt-pl30-2

Name: nerui-pt-pl30-2
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl30-2

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: 0.0677
Location Precision: 0.9
Location Recall: 0.9677
Location F1: 0.9326
Location Number: 93
Organization Precision: 0.9162
Organization Recall: 0.9217
Organization F1: 0.9189
Organization Number: 166
Person Precision: 0.9787
Person Recall: 0.9718
Person F1: 0.9753
Person Number: 142
Overall Precision: 0.9338
Overall Recall: 0.9501
Overall F1: 0.9419
Overall Accuracy: 0.9890

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
0.8532	1.0	96	0.3947	0.0	0.0	0.0	93	0.24	0.2169	0.2278	166	0.2622	0.3028	0.2810	142	0.25	0.1970	0.2204	0.8639
0.359	2.0	192	0.2230	0.3176	0.5054	0.3900	93	0.5739	0.6084	0.5906	166	0.6216	0.8099	0.7034	142	0.5167	0.6559	0.5780	0.9342
0.1925	3.0	288	0.1115	0.8133	0.6559	0.7262	93	0.6634	0.8193	0.7332	166	0.9020	0.9718	0.9356	142	0.7737	0.8354	0.8034	0.9613
0.1309	4.0	384	0.0838	0.7143	0.9140	0.8019	93	0.7953	0.8193	0.8071	166	0.9272	0.9859	0.9556	142	0.8186	0.9002	0.8575	0.9720
0.1058	5.0	480	0.0644	0.8804	0.8710	0.8757	93	0.8690	0.8795	0.8743	166	0.9653	0.9789	0.9720	142	0.9059	0.9127	0.9093	0.9811
0.0954	6.0	576	0.0571	0.7719	0.9462	0.8502	93	0.8839	0.8253	0.8536	166	0.9718	0.9718	0.9718	142	0.8832	0.9052	0.8941	0.9819
0.0835	7.0	672	0.0501	0.8350	0.9247	0.8776	93	0.8715	0.9398	0.9043	166	0.9650	0.9718	0.9684	142	0.8941	0.9476	0.9201	0.9846
0.0757	8.0	768	0.0515	0.8241	0.9570	0.8856	93	0.9030	0.8976	0.9003	166	0.9857	0.9718	0.9787	142	0.9104	0.9377	0.9238	0.9833
0.0712	9.0	864	0.0452	0.8365	0.9355	0.8832	93	0.9102	0.9157	0.9129	166	0.9718	0.9718	0.9718	142	0.9128	0.9401	0.9263	0.9852
0.0653	10.0	960	0.0424	0.7982	0.9355	0.8614	93	0.9130	0.8855	0.8991	166	0.9653	0.9789	0.9720	142	0.9010	0.9302	0.9153	0.9838
0.0602	11.0	1056	0.0433	0.8365	0.9355	0.8832	93	0.9102	0.9157	0.9129	166	0.9586	0.9789	0.9686	142	0.9087	0.9426	0.9253	0.9855
0.0517	12.0	1152	0.0398	0.8614	0.9355	0.8969	93	0.9006	0.9277	0.9139	166	0.9857	0.9718	0.9787	142	0.9199	0.9451	0.9323	0.9874
0.0504	13.0	1248	0.0478	0.9348	0.9247	0.9297	93	0.8941	0.9157	0.9048	166	0.9448	0.9648	0.9547	142	0.9214	0.9352	0.9282	0.9855
0.0475	14.0	1344	0.0485	0.8558	0.9570	0.9036	93	0.9497	0.9096	0.9292	166	0.9586	0.9789	0.9686	142	0.9289	0.9451	0.9370	0.9868
0.0467	15.0	1440	0.0451	0.8411	0.9677	0.9000	93	0.9241	0.8795	0.9012	166	0.9720	0.9789	0.9754	142	0.9191	0.9352	0.9271	0.9857
0.044	16.0	1536	0.0426	0.88	0.9462	0.9119	93	0.9146	0.9036	0.9091	166	0.9718	0.9718	0.9718	142	0.9261	0.9377	0.9318	0.9874
0.0409	17.0	1632	0.0428	0.8824	0.9677	0.9231	93	0.9152	0.9096	0.9124	166	0.9650	0.9718	0.9684	142	0.9244	0.9451	0.9346	0.9885
0.037	18.0	1728	0.0448	0.8911	0.9677	0.9278	93	0.9367	0.8916	0.9136	166	0.9718	0.9718	0.9718	142	0.9377	0.9377	0.9377	0.9879
0.0357	19.0	1824	0.0451	0.9474	0.9677	0.9574	93	0.9387	0.9217	0.9301	166	0.9787	0.9718	0.9753	142	0.9549	0.9501	0.9525	0.9896
0.032	20.0	1920	0.0417	0.88	0.9462	0.9119	93	0.9222	0.9277	0.9249	166	0.9789	0.9789	0.9789	142	0.9315	0.9501	0.9407	0.9888
0.0326	21.0	2016	0.0439	0.8980	0.9462	0.9215	93	0.9059	0.9277	0.9167	166	0.9857	0.9718	0.9787	142	0.9314	0.9476	0.9394	0.9885
0.0348	22.0	2112	0.0414	0.8922	0.9785	0.9333	93	0.9102	0.9157	0.9129	166	0.9787	0.9718	0.9753	142	0.9293	0.9501	0.9396	0.9885
0.0288	23.0	2208	0.0401	0.9192	0.9785	0.9479	93	0.9123	0.9398	0.9258	166	0.9789	0.9789	0.9789	142	0.9369	0.9626	0.9496	0.9901
0.0317	24.0	2304	0.0423	0.9271	0.9570	0.9418	93	0.9277	0.9277	0.9277	166	0.9720	0.9789	0.9754	142	0.9432	0.9526	0.9479	0.9896
0.0296	25.0	2400	0.0435	0.9286	0.9785	0.9529	93	0.9390	0.9277	0.9333	166	0.9718	0.9718	0.9718	142	0.9480	0.9551	0.9516	0.9901
0.0269	26.0	2496	0.0430	0.9184	0.9677	0.9424	93	0.9387	0.9217	0.9301	166	0.9718	0.9718	0.9718	142	0.9454	0.9501	0.9478	0.9896
0.0248	27.0	2592	0.0428	0.9278	0.9677	0.9474	93	0.9222	0.9277	0.9249	166	0.9787	0.9718	0.9753	142	0.9432	0.9526	0.9479	0.9898
0.0263	28.0	2688	0.0489	0.8980	0.9462	0.9215	93	0.9123	0.9398	0.9258	166	0.9718	0.9718	0.9718	142	0.9294	0.9526	0.9409	0.9888
0.0245	29.0	2784	0.0465	0.9010	0.9785	0.9381	93	0.9202	0.9036	0.9119	166	0.9718	0.9718	0.9718	142	0.9335	0.9451	0.9393	0.9888
0.0217	30.0	2880	0.0465	0.8911	0.9677	0.9278	93	0.9172	0.9337	0.9254	166	0.9718	0.9718	0.9718	142	0.9296	0.9551	0.9422	0.9882
0.0219	31.0	2976	0.0479	0.9184	0.9677	0.9424	93	0.9162	0.9217	0.9189	166	0.9720	0.9789	0.9754	142	0.9363	0.9526	0.9444	0.9893
0.0209	32.0	3072	0.0500	0.9010	0.9785	0.9381	93	0.9268	0.9157	0.9212	166	0.9787	0.9718	0.9753	142	0.9384	0.9501	0.9442	0.9882
0.0206	33.0	3168	0.0465	0.9286	0.9785	0.9529	93	0.9217	0.9217	0.9217	166	0.9789	0.9789	0.9789	142	0.9433	0.9551	0.9492	0.9893
0.0202	34.0	3264	0.0447	0.9278	0.9677	0.9474	93	0.9448	0.9277	0.9362	166	0.9718	0.9718	0.9718	142	0.9502	0.9526	0.9514	0.9901
0.0187	35.0	3360	0.0518	0.9451	0.9247	0.9348	93	0.9029	0.9518	0.9267	166	0.9650	0.9718	0.9684	142	0.9340	0.9526	0.9432	0.9885
0.019	36.0	3456	0.0499	0.9	0.9677	0.9326	93	0.9268	0.9157	0.9212	166	0.9718	0.9718	0.9718	142	0.9360	0.9476	0.9418	0.9888
0.0197	37.0	3552	0.0472	0.9560	0.9355	0.9457	93	0.9349	0.9518	0.9433	166	0.9650	0.9718	0.9684	142	0.9504	0.9551	0.9527	0.9896
0.0169	38.0	3648	0.0564	0.8725	0.9570	0.9128	93	0.9107	0.9217	0.9162	166	0.9787	0.9718	0.9753	142	0.9246	0.9476	0.9360	0.9860
0.0176	39.0	3744	0.0530	0.8824	0.9677	0.9231	93	0.9202	0.9036	0.9119	166	0.9720	0.9789	0.9754	142	0.9289	0.9451	0.9370	0.9877
0.0184	40.0	3840	0.0491	0.9570	0.9570	0.9570	93	0.9176	0.9398	0.9286	166	0.9650	0.9718	0.9684	142	0.9433	0.9551	0.9492	0.9882
0.0169	41.0	3936	0.0543	0.9462	0.9462	0.9462	93	0.9176	0.9398	0.9286	166	0.9650	0.9718	0.9684	142	0.9409	0.9526	0.9467	0.9879
0.0152	42.0	4032	0.0538	0.9184	0.9677	0.9424	93	0.9394	0.9337	0.9366	166	0.9720	0.9789	0.9754	142	0.9458	0.9576	0.9517	0.9888
0.015	43.0	4128	0.0531	0.9271	0.9570	0.9418	93	0.9341	0.9398	0.9369	166	0.9720	0.9789	0.9754	142	0.9458	0.9576	0.9517	0.9898
0.0154	44.0	4224	0.0561	0.9175	0.9570	0.9368	93	0.9226	0.9337	0.9281	166	0.9650	0.9718	0.9684	142	0.9363	0.9526	0.9444	0.9879
0.0149	45.0	4320	0.0562	0.9082	0.9570	0.9319	93	0.9212	0.9157	0.9184	166	0.9787	0.9718	0.9753	142	0.9381	0.9451	0.9416	0.9879
0.0158	46.0	4416	0.0483	0.9091	0.9677	0.9375	93	0.9277	0.9277	0.9277	166	0.9858	0.9789	0.9823	142	0.9433	0.9551	0.9492	0.9898
0.0131	47.0	4512	0.0485	0.9271	0.9570	0.9418	93	0.9112	0.9277	0.9194	166	0.9718	0.9718	0.9718	142	0.9361	0.9501	0.9431	0.9893
0.0149	48.0	4608	0.0595	0.89	0.9570	0.9223	93	0.9255	0.8976	0.9113	166	0.9857	0.9718	0.9787	142	0.9377	0.9377	0.9377	0.9877
0.0147	49.0	4704	0.0587	0.8738	0.9677	0.9184	93	0.9193	0.8916	0.9052	166	0.9718	0.9718	0.9718	142	0.9261	0.9377	0.9318	0.9863
0.0128	50.0	4800	0.0571	0.9278	0.9677	0.9474	93	0.9112	0.9277	0.9194	166	0.9787	0.9718	0.9753	142	0.9386	0.9526	0.9455	0.9888
0.0107	51.0	4896	0.0608	0.89	0.9570	0.9223	93	0.9212	0.9157	0.9184	166	0.9718	0.9718	0.9718	142	0.9312	0.9451	0.9381	0.9877
0.0127	52.0	4992	0.0599	0.9082	0.9570	0.9319	93	0.9268	0.9157	0.9212	166	0.9718	0.9718	0.9718	142	0.9381	0.9451	0.9416	0.9885
0.012	53.0	5088	0.0573	0.9	0.9677	0.9326	93	0.9268	0.9157	0.9212	166	0.9718	0.9718	0.9718	142	0.9360	0.9476	0.9418	0.9885
0.0114	54.0	5184	0.0572	0.9271	0.9570	0.9418	93	0.9277	0.9277	0.9277	166	0.9718	0.9718	0.9718	142	0.9431	0.9501	0.9466	0.9893
0.0115	55.0	5280	0.0574	0.91	0.9785	0.9430	93	0.9264	0.9096	0.9179	166	0.9787	0.9718	0.9753	142	0.9406	0.9476	0.9441	0.9890
0.0106	56.0	5376	0.0600	0.8911	0.9677	0.9278	93	0.9277	0.9277	0.9277	166	0.9718	0.9718	0.9718	142	0.9340	0.9526	0.9432	0.9885
0.0111	57.0	5472	0.0596	0.89	0.9570	0.9223	93	0.9217	0.9217	0.9217	166	0.9650	0.9718	0.9684	142	0.9291	0.9476	0.9383	0.9879
0.0117	58.0	5568	0.0610	0.9010	0.9785	0.9381	93	0.9329	0.9217	0.9273	166	0.9718	0.9718	0.9718	142	0.9386	0.9526	0.9455	0.9882
0.0091	59.0	5664	0.0595	0.9091	0.9677	0.9375	93	0.9217	0.9217	0.9217	166	0.9718	0.9718	0.9718	142	0.9361	0.9501	0.9431	0.9888
0.0095	60.0	5760	0.0567	0.9091	0.9677	0.9375	93	0.9226	0.9337	0.9281	166	0.9718	0.9718	0.9718	142	0.9364	0.9551	0.9457	0.9890
0.011	61.0	5856	0.0590	0.9462	0.9462	0.9462	93	0.9070	0.9398	0.9231	166	0.9718	0.9718	0.9718	142	0.9386	0.9526	0.9455	0.9888
0.0109	62.0	5952	0.0650	0.89	0.9570	0.9223	93	0.9202	0.9036	0.9119	166	0.9718	0.9718	0.9718	142	0.9309	0.9401	0.9355	0.9866
0.0101	63.0	6048	0.0630	0.91	0.9785	0.9430	93	0.9222	0.9277	0.9249	166	0.9718	0.9718	0.9718	142	0.9364	0.9551	0.9457	0.9885
0.0098	64.0	6144	0.0605	0.9167	0.9462	0.9312	93	0.9172	0.9337	0.9254	166	0.9787	0.9718	0.9753	142	0.9384	0.9501	0.9442	0.9882
0.0093	65.0	6240	0.0651	0.8889	0.9462	0.9167	93	0.9102	0.9157	0.9129	166	0.9583	0.9718	0.9650	142	0.9220	0.9426	0.9322	0.9866
0.0089	66.0	6336	0.0651	0.8911	0.9677	0.9278	93	0.9212	0.9157	0.9184	166	0.9650	0.9718	0.9684	142	0.9291	0.9476	0.9383	0.9879
0.0081	67.0	6432	0.0660	0.8738	0.9677	0.9184	93	0.9259	0.9036	0.9146	166	0.9789	0.9789	0.9789	142	0.9312	0.9451	0.9381	0.9877
0.0093	68.0	6528	0.0646	0.9091	0.9677	0.9375	93	0.9268	0.9157	0.9212	166	0.9718	0.9718	0.9718	142	0.9383	0.9476	0.9429	0.9888
0.0079	69.0	6624	0.0610	0.9368	0.9570	0.9468	93	0.9222	0.9277	0.9249	166	0.9718	0.9718	0.9718	142	0.9431	0.9501	0.9466	0.9896
0.0079	70.0	6720	0.0638	0.9184	0.9677	0.9424	93	0.9157	0.9157	0.9157	166	0.9787	0.9718	0.9753	142	0.9383	0.9476	0.9429	0.9890
0.0077	71.0	6816	0.0633	0.9167	0.9462	0.9312	93	0.9226	0.9337	0.9281	166	0.9720	0.9789	0.9754	142	0.9386	0.9526	0.9455	0.9890
0.0082	72.0	6912	0.0667	0.9	0.9677	0.9326	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9338	0.9501	0.9419	0.9888
0.0086	73.0	7008	0.0638	0.8990	0.9570	0.9271	93	0.9217	0.9217	0.9217	166	0.9787	0.9718	0.9753	142	0.9360	0.9476	0.9418	0.9896
0.0084	74.0	7104	0.0692	0.8889	0.9462	0.9167	93	0.9212	0.9157	0.9184	166	0.9789	0.9789	0.9789	142	0.9335	0.9451	0.9393	0.9874
0.0078	75.0	7200	0.0677	0.89	0.9570	0.9223	93	0.9277	0.9277	0.9277	166	0.9718	0.9718	0.9718	142	0.9338	0.9501	0.9419	0.9882
0.0082	76.0	7296	0.0631	0.9175	0.9570	0.9368	93	0.9333	0.9277	0.9305	166	0.9789	0.9789	0.9789	142	0.9455	0.9526	0.9491	0.9898
0.007	77.0	7392	0.0711	0.8980	0.9462	0.9215	93	0.9255	0.8976	0.9113	166	0.9787	0.9718	0.9753	142	0.9375	0.9352	0.9363	0.9868
0.008	78.0	7488	0.0645	0.9184	0.9677	0.9424	93	0.9451	0.9337	0.9394	166	0.9789	0.9789	0.9789	142	0.9505	0.9576	0.9540	0.9907
0.0066	79.0	7584	0.0623	0.9082	0.9570	0.9319	93	0.9333	0.9277	0.9305	166	0.9858	0.9789	0.9823	142	0.9455	0.9526	0.9491	0.9890
0.0069	80.0	7680	0.0697	0.8812	0.9570	0.9175	93	0.9375	0.9036	0.9202	166	0.9858	0.9789	0.9823	142	0.9403	0.9426	0.9415	0.9877
0.0058	81.0	7776	0.0668	0.8889	0.9462	0.9167	93	0.9273	0.9217	0.9245	166	0.9650	0.9718	0.9684	142	0.9312	0.9451	0.9381	0.9879
0.0069	82.0	7872	0.0683	0.89	0.9570	0.9223	93	0.9325	0.9157	0.9240	166	0.9789	0.9789	0.9789	142	0.9383	0.9476	0.9429	0.9882
0.0068	83.0	7968	0.0706	0.8990	0.9570	0.9271	93	0.9212	0.9157	0.9184	166	0.9789	0.9789	0.9789	142	0.9360	0.9476	0.9418	0.9877
0.0065	84.0	8064	0.0690	0.9	0.9677	0.9326	93	0.9333	0.9277	0.9305	166	0.9858	0.9789	0.9823	142	0.9433	0.9551	0.9492	0.9885
0.0066	85.0	8160	0.0673	0.9	0.9677	0.9326	93	0.9277	0.9277	0.9277	166	0.9789	0.9789	0.9789	142	0.9387	0.9551	0.9468	0.9879
0.0063	86.0	8256	0.0659	0.9184	0.9677	0.9424	93	0.9107	0.9217	0.9162	166	0.9787	0.9718	0.9753	142	0.9361	0.9501	0.9431	0.9890
0.0064	87.0	8352	0.0661	0.9184	0.9677	0.9424	93	0.9273	0.9217	0.9245	166	0.9787	0.9718	0.9753	142	0.9431	0.9501	0.9466	0.9896
0.0056	88.0	8448	0.0643	0.9184	0.9677	0.9424	93	0.9222	0.9277	0.9249	166	0.9787	0.9718	0.9753	142	0.9409	0.9526	0.9467	0.9898
0.0065	89.0	8544	0.0647	0.9091	0.9677	0.9375	93	0.9162	0.9217	0.9189	166	0.9718	0.9718	0.9718	142	0.9338	0.9501	0.9419	0.9893
0.0062	90.0	8640	0.0644	0.9184	0.9677	0.9424	93	0.9273	0.9217	0.9245	166	0.9787	0.9718	0.9753	142	0.9431	0.9501	0.9466	0.9896
0.0058	91.0	8736	0.0660	0.9	0.9677	0.9326	93	0.9217	0.9217	0.9217	166	0.9718	0.9718	0.9718	142	0.9338	0.9501	0.9419	0.9885
0.0051	92.0	8832	0.0647	0.9192	0.9785	0.9479	93	0.9277	0.9277	0.9277	166	0.9718	0.9718	0.9718	142	0.9410	0.9551	0.9480	0.9888
0.0048	93.0	8928	0.0672	0.89	0.9570	0.9223	93	0.9107	0.9217	0.9162	166	0.9718	0.9718	0.9718	142	0.9268	0.9476	0.9371	0.9882
0.0058	94.0	9024	0.0668	0.9091	0.9677	0.9375	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9361	0.9501	0.9431	0.9890
0.0067	95.0	9120	0.0673	0.9091	0.9677	0.9375	93	0.9162	0.9217	0.9189	166	0.9718	0.9718	0.9718	142	0.9338	0.9501	0.9419	0.9885
0.0051	96.0	9216	0.0672	0.9278	0.9677	0.9474	93	0.9217	0.9217	0.9217	166	0.9787	0.9718	0.9753	142	0.9431	0.9501	0.9466	0.9890
0.005	97.0	9312	0.0677	0.9	0.9677	0.9326	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9338	0.9501	0.9419	0.9890
0.0062	98.0	9408	0.0679	0.9	0.9677	0.9326	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9338	0.9501	0.9419	0.9890
0.0053	99.0	9504	0.0678	0.9	0.9677	0.9326	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9338	0.9501	0.9419	0.9890
0.0046	100.0	9600	0.0677	0.9	0.9677	0.9326	93	0.9162	0.9217	0.9189	166	0.9787	0.9718	0.9753	142	0.9338	0.9501	0.9419	0.9890

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl30-2

nerui-pt-pl30-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl30-2