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apwic/
nerui-pt-pl20-2

Name: nerui-pt-pl20-2
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl20-2

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: 0.0659
Location Precision: 0.875
Location Recall: 0.9785
Location F1: 0.9239
Location Number: 93
Organization Precision: 0.9430
Organization Recall: 0.8976
Organization F1: 0.9198
Organization Number: 166
Person Precision: 0.9650
Person Recall: 0.9718
Person F1: 0.9684
Person Number: 142
Overall Precision: 0.9333
Overall Recall: 0.9426
Overall F1: 0.9380
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
0.8355	1.0	96	0.3860	0.125	0.0108	0.0198	93	0.2687	0.3253	0.2943	166	0.2477	0.3873	0.3022	142	0.2552	0.2743	0.2644	0.8705
0.3665	2.0	192	0.2359	0.2975	0.3871	0.3364	93	0.5309	0.5181	0.5244	166	0.4516	0.6901	0.5460	142	0.44	0.5486	0.4883	0.9243
0.201	3.0	288	0.0994	0.8523	0.8065	0.8287	93	0.6919	0.8253	0.7527	166	0.9589	0.9859	0.9722	142	0.8148	0.8778	0.8451	0.9701
0.1372	4.0	384	0.0864	0.7193	0.8817	0.7923	93	0.7737	0.8855	0.8258	166	0.9589	0.9859	0.9722	142	0.82	0.9202	0.8672	0.9734
0.1141	5.0	480	0.0641	0.8542	0.8817	0.8677	93	0.8333	0.8735	0.8529	166	0.9655	0.9859	0.9756	142	0.8843	0.9152	0.8995	0.9813
0.0964	6.0	576	0.0601	0.7521	0.9462	0.8381	93	0.8710	0.8133	0.8411	166	0.9790	0.9859	0.9825	142	0.8747	0.9052	0.8897	0.9811
0.0877	7.0	672	0.0540	0.83	0.8925	0.8601	93	0.8793	0.9217	0.9	166	0.9655	0.9859	0.9756	142	0.8974	0.9377	0.9171	0.9813
0.0764	8.0	768	0.0547	0.8214	0.9892	0.8976	93	0.9193	0.8916	0.9052	166	0.9858	0.9789	0.9823	142	0.9155	0.9451	0.9301	0.9827
0.0681	9.0	864	0.0442	0.8812	0.9570	0.9175	93	0.8902	0.9277	0.9086	166	0.9858	0.9789	0.9823	142	0.9205	0.9526	0.9363	0.9846
0.066	10.0	960	0.0491	0.8505	0.9785	0.91	93	0.9487	0.8916	0.9193	166	0.9789	0.9789	0.9789	142	0.9333	0.9426	0.9380	0.9855
0.0604	11.0	1056	0.0464	0.8889	0.9462	0.9167	93	0.9042	0.9096	0.9069	166	0.9789	0.9789	0.9789	142	0.9265	0.9426	0.9345	0.9860
0.059	12.0	1152	0.0488	0.8958	0.9247	0.9101	93	0.8889	0.9157	0.9021	166	0.9789	0.9789	0.9789	142	0.9218	0.9401	0.9309	0.9860
0.0545	13.0	1248	0.0419	0.8824	0.9677	0.9231	93	0.9212	0.9157	0.9184	166	0.9789	0.9789	0.9789	142	0.9315	0.9501	0.9407	0.9874
0.0525	14.0	1344	0.0446	0.8980	0.9462	0.9215	93	0.9118	0.9337	0.9226	166	0.9720	0.9789	0.9754	142	0.9294	0.9526	0.9409	0.9871
0.0478	15.0	1440	0.0429	0.8889	0.9462	0.9167	93	0.9036	0.9036	0.9036	166	0.9789	0.9789	0.9789	142	0.9263	0.9401	0.9332	0.9882
0.0466	16.0	1536	0.0460	0.8627	0.9462	0.9026	93	0.9091	0.9036	0.9063	166	0.9720	0.9789	0.9754	142	0.9195	0.9401	0.9297	0.9863
0.0442	17.0	1632	0.0399	0.8585	0.9785	0.9146	93	0.9198	0.8976	0.9085	166	0.9858	0.9789	0.9823	142	0.9267	0.9451	0.9358	0.9879
0.042	18.0	1728	0.0402	0.8654	0.9677	0.9137	93	0.9371	0.8976	0.9169	166	0.9789	0.9789	0.9789	142	0.9333	0.9426	0.9380	0.9882
0.0384	19.0	1824	0.0404	0.8585	0.9785	0.9146	93	0.9371	0.8976	0.9169	166	0.9789	0.9789	0.9789	142	0.9312	0.9451	0.9381	0.9882
0.0365	20.0	1920	0.0457	0.8812	0.9570	0.9175	93	0.8851	0.9277	0.9059	166	0.9718	0.9718	0.9718	142	0.9137	0.9501	0.9315	0.9860
0.0363	21.0	2016	0.0477	0.8273	0.9785	0.8966	93	0.9290	0.8675	0.8972	166	0.9789	0.9789	0.9789	142	0.9189	0.9327	0.9257	0.9855
0.0344	22.0	2112	0.0464	0.8725	0.9570	0.9128	93	0.8988	0.9096	0.9042	166	0.9653	0.9789	0.9720	142	0.9155	0.9451	0.9301	0.9863
0.0313	23.0	2208	0.0449	0.8824	0.9677	0.9231	93	0.9379	0.9096	0.9235	166	0.9789	0.9789	0.9789	142	0.9383	0.9476	0.9429	0.9885
0.0311	24.0	2304	0.0442	0.8824	0.9677	0.9231	93	0.9273	0.9217	0.9245	166	0.9789	0.9789	0.9789	142	0.9340	0.9526	0.9432	0.9879
0.031	25.0	2400	0.0506	0.8738	0.9677	0.9184	93	0.9494	0.9036	0.9259	166	0.9720	0.9789	0.9754	142	0.9381	0.9451	0.9416	0.9860
0.0294	26.0	2496	0.0510	0.8812	0.9570	0.9175	93	0.9539	0.8735	0.9119	166	0.9720	0.9789	0.9754	142	0.9419	0.9302	0.9360	0.9874
0.0283	27.0	2592	0.0515	0.8318	0.9570	0.89	93	0.9030	0.8976	0.9003	166	0.9789	0.9789	0.9789	142	0.9106	0.9401	0.9252	0.9852
0.0286	28.0	2688	0.0476	0.8571	0.9677	0.9091	93	0.9141	0.8976	0.9058	166	0.9789	0.9789	0.9789	142	0.9220	0.9426	0.9322	0.9857
0.0252	29.0	2784	0.0472	0.8476	0.9570	0.8990	93	0.9375	0.9036	0.9202	166	0.9720	0.9789	0.9754	142	0.9265	0.9426	0.9345	0.9868
0.0262	30.0	2880	0.0478	0.8713	0.9462	0.9072	93	0.9053	0.9217	0.9134	166	0.9720	0.9789	0.9754	142	0.9201	0.9476	0.9337	0.9877
0.0252	31.0	2976	0.0459	0.9	0.9677	0.9326	93	0.9202	0.9036	0.9119	166	0.9720	0.9789	0.9754	142	0.9335	0.9451	0.9393	0.9893
0.0228	32.0	3072	0.0474	0.8585	0.9785	0.9146	93	0.9363	0.8855	0.9102	166	0.9789	0.9789	0.9789	142	0.9309	0.9401	0.9355	0.9863
0.0226	33.0	3168	0.0494	0.8824	0.9677	0.9231	93	0.9255	0.8976	0.9113	166	0.9720	0.9789	0.9754	142	0.9310	0.9426	0.9368	0.9879
0.0212	34.0	3264	0.0513	0.8364	0.9892	0.9064	93	0.9477	0.8735	0.9091	166	0.9789	0.9789	0.9789	142	0.9284	0.9377	0.9330	0.9877
0.0221	35.0	3360	0.0475	0.8667	0.9785	0.9192	93	0.9375	0.9036	0.9202	166	0.9789	0.9789	0.9789	142	0.9337	0.9476	0.9406	0.9877
0.0216	36.0	3456	0.0497	0.8426	0.9785	0.9055	93	0.9477	0.8735	0.9091	166	0.9720	0.9789	0.9754	142	0.9282	0.9352	0.9317	0.9866
0.0197	37.0	3552	0.0447	0.8990	0.9570	0.9271	93	0.9273	0.9217	0.9245	166	0.9789	0.9789	0.9789	142	0.9384	0.9501	0.9442	0.9882
0.0201	38.0	3648	0.0532	0.8491	0.9677	0.9045	93	0.9241	0.8795	0.9012	166	0.9789	0.9789	0.9789	142	0.9236	0.9352	0.9294	0.9863
0.0179	39.0	3744	0.0524	0.8598	0.9892	0.9200	93	0.9542	0.8795	0.9154	166	0.9789	0.9789	0.9789	142	0.9378	0.9401	0.9390	0.9879
0.0174	40.0	3840	0.0529	0.8911	0.9677	0.9278	93	0.9434	0.9036	0.9231	166	0.9718	0.9718	0.9718	142	0.9403	0.9426	0.9415	0.9882
0.0173	41.0	3936	0.0475	0.9263	0.9462	0.9362	93	0.9212	0.9157	0.9184	166	0.9720	0.9789	0.9754	142	0.9404	0.9451	0.9428	0.9893
0.0169	42.0	4032	0.0544	0.8585	0.9785	0.9146	93	0.9363	0.8855	0.9102	166	0.9583	0.9718	0.9650	142	0.9238	0.9377	0.9307	0.9874
0.0166	43.0	4128	0.0543	0.8654	0.9677	0.9137	93	0.9359	0.8795	0.9068	166	0.9650	0.9718	0.9684	142	0.9280	0.9327	0.9303	0.9877
0.0173	44.0	4224	0.0546	0.89	0.9570	0.9223	93	0.9268	0.9157	0.9212	166	0.9583	0.9718	0.9650	142	0.9289	0.9451	0.9370	0.9877
0.0166	45.0	4320	0.0575	0.8654	0.9677	0.9137	93	0.9355	0.8735	0.9034	166	0.9650	0.9718	0.9684	142	0.9279	0.9302	0.9290	0.9868
0.0166	46.0	4416	0.0603	0.8571	0.9677	0.9091	93	0.9255	0.8976	0.9113	166	0.9720	0.9789	0.9754	142	0.9242	0.9426	0.9333	0.9863
0.0159	47.0	4512	0.0573	0.8911	0.9677	0.9278	93	0.95	0.9157	0.9325	166	0.9583	0.9718	0.9650	142	0.9383	0.9476	0.9429	0.9885
0.0146	48.0	4608	0.0633	0.8667	0.9785	0.9192	93	0.9542	0.8795	0.9154	166	0.9718	0.9718	0.9718	142	0.9375	0.9352	0.9363	0.9877
0.0129	49.0	4704	0.0630	0.8911	0.9677	0.9278	93	0.9207	0.9096	0.9152	166	0.9650	0.9718	0.9684	142	0.9289	0.9451	0.9370	0.9866
0.0141	50.0	4800	0.0630	0.8846	0.9892	0.9340	93	0.9371	0.8976	0.9169	166	0.9517	0.9718	0.9617	142	0.9289	0.9451	0.9370	0.9871
0.0135	51.0	4896	0.0608	0.8654	0.9677	0.9137	93	0.9551	0.8976	0.9255	166	0.9583	0.9718	0.9650	142	0.9332	0.9401	0.9366	0.9866
0.0134	52.0	4992	0.0661	0.8990	0.9570	0.9271	93	0.9329	0.9217	0.9273	166	0.9650	0.9718	0.9684	142	0.9360	0.9476	0.9418	0.9871
0.0131	53.0	5088	0.0592	0.9278	0.9677	0.9474	93	0.9390	0.9277	0.9333	166	0.9583	0.9718	0.9650	142	0.9432	0.9526	0.9479	0.9890
0.0127	54.0	5184	0.0612	0.875	0.9785	0.9239	93	0.9308	0.8916	0.9108	166	0.9720	0.9789	0.9754	142	0.9310	0.9426	0.9368	0.9868
0.0136	55.0	5280	0.0612	0.8835	0.9785	0.9286	93	0.9494	0.9036	0.9259	166	0.9517	0.9718	0.9617	142	0.9335	0.9451	0.9393	0.9877
0.0133	56.0	5376	0.0566	0.8585	0.9785	0.9146	93	0.9551	0.8976	0.9255	166	0.9583	0.9718	0.9650	142	0.9310	0.9426	0.9368	0.9885
0.0131	57.0	5472	0.0581	0.8932	0.9892	0.9388	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9381	0.9451	0.9416	0.9882
0.0124	58.0	5568	0.0581	0.875	0.9785	0.9239	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9333	0.9426	0.9380	0.9874
0.0131	59.0	5664	0.0589	0.8846	0.9892	0.9340	93	0.9494	0.9036	0.9259	166	0.9718	0.9718	0.9718	142	0.9406	0.9476	0.9441	0.9882
0.0108	60.0	5760	0.0575	0.8932	0.9892	0.9388	93	0.9623	0.9217	0.9415	166	0.9650	0.9718	0.9684	142	0.9457	0.9551	0.9504	0.9898
0.0097	61.0	5856	0.0581	0.91	0.9785	0.9430	93	0.95	0.9157	0.9325	166	0.9718	0.9718	0.9718	142	0.9478	0.9501	0.9489	0.9893
0.0102	62.0	5952	0.0607	0.9010	0.9785	0.9381	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9403	0.9426	0.9415	0.9879
0.0112	63.0	6048	0.0644	0.8585	0.9785	0.9146	93	0.9363	0.8855	0.9102	166	0.9650	0.9718	0.9684	142	0.9261	0.9377	0.9318	0.9857
0.009	64.0	6144	0.0635	0.8824	0.9677	0.9231	93	0.9379	0.9096	0.9235	166	0.9650	0.9718	0.9684	142	0.9335	0.9451	0.9393	0.9885
0.0095	65.0	6240	0.0660	0.875	0.9785	0.9239	93	0.9375	0.9036	0.9202	166	0.9517	0.9718	0.9617	142	0.9267	0.9451	0.9358	0.9866
0.0092	66.0	6336	0.0665	0.8835	0.9785	0.9286	93	0.9430	0.8976	0.9198	166	0.9517	0.9718	0.9617	142	0.9310	0.9426	0.9368	0.9871
0.0103	67.0	6432	0.0615	0.8571	0.9677	0.9091	93	0.9313	0.8976	0.9141	166	0.9517	0.9718	0.9617	142	0.9195	0.9401	0.9297	0.9868
0.0086	68.0	6528	0.0615	0.8558	0.9570	0.9036	93	0.9430	0.8976	0.9198	166	0.9583	0.9718	0.9650	142	0.9261	0.9377	0.9318	0.9877
0.0097	69.0	6624	0.0620	0.8835	0.9785	0.9286	93	0.9423	0.8855	0.9130	166	0.9517	0.9718	0.9617	142	0.9307	0.9377	0.9342	0.9879
0.0083	70.0	6720	0.0625	0.8667	0.9785	0.9192	93	0.9304	0.8855	0.9074	166	0.9517	0.9718	0.9617	142	0.9216	0.9377	0.9295	0.9874
0.0079	71.0	6816	0.0575	0.9192	0.9785	0.9479	93	0.95	0.9157	0.9325	166	0.9583	0.9718	0.9650	142	0.9454	0.9501	0.9478	0.9896
0.0082	72.0	6912	0.0683	0.8585	0.9785	0.9146	93	0.9542	0.8795	0.9154	166	0.9650	0.9718	0.9684	142	0.9328	0.9352	0.9340	0.9874
0.0087	73.0	7008	0.0593	0.8762	0.9892	0.9293	93	0.9419	0.8795	0.9097	166	0.9517	0.9718	0.9617	142	0.9284	0.9377	0.9330	0.9882
0.007	74.0	7104	0.0607	0.8762	0.9892	0.9293	93	0.9490	0.8976	0.9226	166	0.9583	0.9718	0.9650	142	0.9335	0.9451	0.9393	0.9877
0.0082	75.0	7200	0.0617	0.8932	0.9892	0.9388	93	0.9487	0.8916	0.9193	166	0.9720	0.9789	0.9754	142	0.9428	0.9451	0.9440	0.9893
0.0075	76.0	7296	0.0615	0.9020	0.9892	0.9436	93	0.9563	0.9217	0.9387	166	0.9789	0.9789	0.9789	142	0.9505	0.9576	0.9540	0.9896
0.0073	77.0	7392	0.0674	0.8679	0.9892	0.9246	93	0.9542	0.8795	0.9154	166	0.9517	0.9718	0.9617	142	0.9307	0.9377	0.9342	0.9882
0.0073	78.0	7488	0.0666	0.8654	0.9677	0.9137	93	0.9363	0.8855	0.9102	166	0.9653	0.9789	0.9720	142	0.9284	0.9377	0.9330	0.9868
0.0084	79.0	7584	0.0674	0.8667	0.9785	0.9192	93	0.9427	0.8916	0.9164	166	0.9720	0.9789	0.9754	142	0.9333	0.9426	0.9380	0.9874
0.0078	80.0	7680	0.0658	0.8762	0.9892	0.9293	93	0.9487	0.8916	0.9193	166	0.9720	0.9789	0.9754	142	0.9381	0.9451	0.9416	0.9885
0.0083	81.0	7776	0.0630	0.875	0.9785	0.9239	93	0.9490	0.8976	0.9226	166	0.9583	0.9718	0.9650	142	0.9333	0.9426	0.9380	0.9879
0.0069	82.0	7872	0.0661	0.875	0.9785	0.9239	93	0.9481	0.8795	0.9125	166	0.9517	0.9718	0.9617	142	0.9305	0.9352	0.9328	0.9877
0.0068	83.0	7968	0.0675	0.8571	0.9677	0.9091	93	0.9487	0.8916	0.9193	166	0.9583	0.9718	0.9650	142	0.9284	0.9377	0.9330	0.9874
0.0062	84.0	8064	0.0648	0.8738	0.9677	0.9184	93	0.9613	0.8976	0.9283	166	0.9650	0.9718	0.9684	142	0.9401	0.9401	0.9401	0.9885
0.0066	85.0	8160	0.0631	0.9091	0.9677	0.9375	93	0.9451	0.9337	0.9394	166	0.9718	0.9718	0.9718	142	0.9457	0.9551	0.9504	0.9896
0.0061	86.0	8256	0.0701	0.8505	0.9785	0.91	93	0.9610	0.8916	0.9250	166	0.9650	0.9718	0.9684	142	0.9332	0.9401	0.9366	0.9871
0.0069	87.0	8352	0.0652	0.8725	0.9570	0.9128	93	0.9375	0.9036	0.9202	166	0.9650	0.9718	0.9684	142	0.9309	0.9401	0.9355	0.9879
0.0064	88.0	8448	0.0647	0.8654	0.9677	0.9137	93	0.9430	0.8976	0.9198	166	0.9789	0.9789	0.9789	142	0.9356	0.9426	0.9391	0.9879
0.0058	89.0	8544	0.0624	0.91	0.9785	0.9430	93	0.9437	0.9096	0.9264	166	0.9650	0.9718	0.9684	142	0.9429	0.9476	0.9453	0.9890
0.0058	90.0	8640	0.0643	0.8846	0.9892	0.9340	93	0.9490	0.8976	0.9226	166	0.9650	0.9718	0.9684	142	0.9381	0.9451	0.9416	0.9882
0.0067	91.0	8736	0.0670	0.8762	0.9892	0.9293	93	0.9551	0.8976	0.9255	166	0.9650	0.9718	0.9684	142	0.9381	0.9451	0.9416	0.9888
0.0058	92.0	8832	0.0675	0.8654	0.9677	0.9137	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9309	0.9401	0.9355	0.9877
0.0054	93.0	8928	0.0676	0.8738	0.9677	0.9184	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9332	0.9401	0.9366	0.9877
0.0054	94.0	9024	0.0666	0.875	0.9785	0.9239	93	0.9371	0.8976	0.9169	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9879
0.0062	95.0	9120	0.0664	0.8835	0.9785	0.9286	93	0.9434	0.9036	0.9231	166	0.9650	0.9718	0.9684	142	0.9358	0.9451	0.9404	0.9879
0.0057	96.0	9216	0.0657	0.875	0.9785	0.9239	93	0.9371	0.8976	0.9169	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9877
0.0059	97.0	9312	0.0659	0.875	0.9785	0.9239	93	0.9375	0.9036	0.9202	166	0.9650	0.9718	0.9684	142	0.9312	0.9451	0.9381	0.9879
0.0063	98.0	9408	0.0656	0.875	0.9785	0.9239	93	0.9371	0.8976	0.9169	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9879
0.0056	99.0	9504	0.0658	0.875	0.9785	0.9239	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9333	0.9426	0.9380	0.9879
0.0055	100.0	9600	0.0659	0.875	0.9785	0.9239	93	0.9430	0.8976	0.9198	166	0.9650	0.9718	0.9684	142	0.9333	0.9426	0.9380	0.9879

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl20-2

nerui-pt-pl20-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl20-2