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apwic/
nerui-pt-pl50-4

Name: nerui-pt-pl50-4
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl50-4

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: 0.0479
Location Precision: 0.9333
Location Recall: 0.9515
Location F1: 0.9423
Location Number: 103
Organization Precision: 0.9419
Organization Recall: 0.9474
Organization F1: 0.9446
Organization Number: 171
Person Precision: 0.9771
Person Recall: 0.9771
Person F1: 0.9771
Person Number: 131
Overall Precision: 0.9510
Overall Recall: 0.9580
Overall F1: 0.9545
Overall Accuracy: 0.9912

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
0.8665	1.0	96	0.4224	0.0	0.0	0.0	103	0.1404	0.0468	0.0702	171	0.1818	0.0458	0.0732	131	0.1538	0.0346	0.0565	0.8429
0.3663	2.0	192	0.2041	0.4068	0.4660	0.4344	103	0.6587	0.6433	0.6509	171	0.7261	0.8702	0.7917	131	0.6154	0.6716	0.6423	0.9392
0.1986	3.0	288	0.0988	0.7723	0.7573	0.7647	103	0.7732	0.8772	0.8219	171	0.9474	0.9618	0.9545	131	0.8271	0.8741	0.8499	0.9713
0.1405	4.0	384	0.0792	0.8208	0.8447	0.8325	103	0.8280	0.9006	0.8627	171	0.9343	0.9771	0.9552	131	0.8601	0.9111	0.8849	0.9760
0.1124	5.0	480	0.0597	0.8878	0.8447	0.8657	103	0.8743	0.8947	0.8844	171	0.9621	0.9695	0.9658	131	0.9062	0.9062	0.9062	0.9815
0.0945	6.0	576	0.0551	0.8362	0.9417	0.8858	103	0.9032	0.8187	0.8589	171	0.9697	0.9771	0.9734	131	0.9057	0.9012	0.9035	0.9829
0.0869	7.0	672	0.0455	0.8911	0.8738	0.8824	103	0.8798	0.9415	0.9096	171	0.9845	0.9695	0.9769	131	0.9153	0.9333	0.9242	0.9867
0.0771	8.0	768	0.0429	0.8991	0.9515	0.9245	103	0.9017	0.9123	0.9070	171	0.9697	0.9771	0.9734	131	0.9227	0.9432	0.9328	0.9870
0.0687	9.0	864	0.0448	0.9057	0.9320	0.9187	103	0.8857	0.9064	0.8960	171	0.9549	0.9695	0.9621	131	0.9130	0.9333	0.9231	0.9870
0.0645	10.0	960	0.0380	0.9412	0.9320	0.9366	103	0.9153	0.9474	0.9310	171	0.9692	0.9618	0.9655	131	0.9389	0.9481	0.9435	0.9898
0.0571	11.0	1056	0.0343	0.94	0.9126	0.9261	103	0.8962	0.9591	0.9266	171	0.9769	0.9695	0.9732	131	0.9322	0.9506	0.9413	0.9895
0.056	12.0	1152	0.0425	0.9293	0.8932	0.9109	103	0.8994	0.9415	0.9200	171	0.9621	0.9695	0.9658	131	0.9268	0.9383	0.9325	0.9876
0.0515	13.0	1248	0.0425	0.93	0.9029	0.9163	103	0.8824	0.9649	0.9218	171	0.9545	0.9618	0.9582	131	0.9165	0.9481	0.9320	0.9878
0.0491	14.0	1344	0.0440	0.9238	0.9417	0.9327	103	0.9118	0.9064	0.9091	171	0.9621	0.9695	0.9658	131	0.9312	0.9358	0.9335	0.9878
0.0471	15.0	1440	0.0393	0.9691	0.9126	0.94	103	0.9153	0.9474	0.9310	171	0.9621	0.9695	0.9658	131	0.9433	0.9457	0.9445	0.9890
0.043	16.0	1536	0.0386	0.9057	0.9320	0.9187	103	0.9029	0.9240	0.9133	171	0.9621	0.9695	0.9658	131	0.9225	0.9407	0.9315	0.9873
0.0403	17.0	1632	0.0430	0.97	0.9417	0.9557	103	0.9195	0.9357	0.9275	171	0.9549	0.9695	0.9621	131	0.9435	0.9481	0.9458	0.9898
0.04	18.0	1728	0.0440	0.9245	0.9515	0.9378	103	0.9118	0.9064	0.9091	171	0.9697	0.9771	0.9734	131	0.9338	0.9407	0.9373	0.9881
0.0379	19.0	1824	0.0462	0.8609	0.9612	0.9083	103	0.9434	0.8772	0.9091	171	0.9697	0.9771	0.9734	131	0.9286	0.9309	0.9297	0.9873
0.037	20.0	1920	0.0461	0.8839	0.9612	0.9209	103	0.9107	0.8947	0.9027	171	0.9695	0.9695	0.9695	131	0.9221	0.9358	0.9289	0.9873
0.0368	21.0	2016	0.0481	0.8609	0.9612	0.9083	103	0.9125	0.8538	0.8822	171	0.9621	0.9695	0.9658	131	0.9140	0.9185	0.9163	0.9859
0.0331	22.0	2112	0.0397	0.9238	0.9417	0.9327	103	0.9298	0.9298	0.9298	171	0.9621	0.9695	0.9658	131	0.9387	0.9457	0.9422	0.9887
0.0317	23.0	2208	0.0429	0.9074	0.9515	0.9289	103	0.9286	0.9123	0.9204	171	0.9621	0.9695	0.9658	131	0.9338	0.9407	0.9373	0.9876
0.0339	24.0	2304	0.0436	0.8919	0.9612	0.9252	103	0.9506	0.9006	0.9249	171	0.9621	0.9695	0.9658	131	0.9383	0.9383	0.9383	0.9876
0.0325	25.0	2400	0.0454	0.8839	0.9612	0.9209	103	0.9497	0.8830	0.9152	171	0.9545	0.9618	0.9582	131	0.9330	0.9284	0.9307	0.9873
0.0287	26.0	2496	0.0478	0.9231	0.9320	0.9275	103	0.9123	0.9123	0.9123	171	0.9621	0.9695	0.9658	131	0.9312	0.9358	0.9335	0.9873
0.0288	27.0	2592	0.0451	0.9423	0.9515	0.9469	103	0.9191	0.9298	0.9244	171	0.9621	0.9695	0.9658	131	0.9389	0.9481	0.9435	0.9884
0.0274	28.0	2688	0.0474	0.9159	0.9515	0.9333	103	0.9298	0.9298	0.9298	171	0.9545	0.9618	0.9582	131	0.9341	0.9457	0.9399	0.9867
0.0259	29.0	2784	0.0498	0.9245	0.9515	0.9378	103	0.9249	0.9357	0.9302	171	0.9695	0.9695	0.9695	131	0.9390	0.9506	0.9448	0.9884
0.0248	30.0	2880	0.0496	0.9495	0.9126	0.9307	103	0.8994	0.9415	0.9200	171	0.9621	0.9695	0.9658	131	0.9317	0.9432	0.9374	0.9867
0.0226	31.0	2976	0.0559	0.8829	0.9515	0.9159	103	0.9255	0.8713	0.8976	171	0.9695	0.9695	0.9695	131	0.9280	0.9235	0.9257	0.9854
0.0238	32.0	3072	0.0477	0.9510	0.9417	0.9463	103	0.9138	0.9298	0.9217	171	0.9618	0.9618	0.9618	131	0.9386	0.9432	0.9409	0.9884
0.0218	33.0	3168	0.0555	0.8333	0.9709	0.8969	103	0.9226	0.8363	0.8773	171	0.9695	0.9695	0.9695	131	0.9113	0.9136	0.9125	0.9845
0.0258	34.0	3264	0.0493	0.9083	0.9612	0.9340	103	0.9273	0.8947	0.9107	171	0.9621	0.9695	0.9658	131	0.9335	0.9358	0.9346	0.9873
0.0246	35.0	3360	0.0491	0.8991	0.9515	0.9245	103	0.9118	0.9064	0.9091	171	0.9695	0.9695	0.9695	131	0.9268	0.9383	0.9325	0.9873
0.0215	36.0	3456	0.0474	0.9245	0.9515	0.9378	103	0.9222	0.9006	0.9112	171	0.9618	0.9618	0.9618	131	0.9356	0.9333	0.9345	0.9884
0.0207	37.0	3552	0.0467	0.9406	0.9223	0.9314	103	0.9070	0.9123	0.9096	171	0.9621	0.9695	0.9658	131	0.9333	0.9333	0.9333	0.9876
0.0194	38.0	3648	0.0544	0.9333	0.9515	0.9423	103	0.9128	0.9181	0.9155	171	0.9695	0.9695	0.9695	131	0.9363	0.9432	0.9397	0.9873
0.0204	39.0	3744	0.0415	0.9223	0.9223	0.9223	103	0.92	0.9415	0.9306	171	0.9621	0.9695	0.9658	131	0.9341	0.9457	0.9399	0.9887
0.0184	40.0	3840	0.0441	0.9510	0.9417	0.9463	103	0.8983	0.9298	0.9138	171	0.9695	0.9695	0.9695	131	0.9341	0.9457	0.9399	0.9890
0.0198	41.0	3936	0.0452	0.9688	0.9029	0.9347	103	0.8883	0.9298	0.9086	171	0.9618	0.9618	0.9618	131	0.9310	0.9333	0.9322	0.9884
0.0175	42.0	4032	0.0432	0.9510	0.9417	0.9463	103	0.9253	0.9415	0.9333	171	0.9697	0.9771	0.9734	131	0.9461	0.9531	0.9496	0.9898
0.0158	43.0	4128	0.0483	0.9231	0.9320	0.9275	103	0.9222	0.9006	0.9112	171	0.9695	0.9695	0.9695	131	0.9378	0.9309	0.9343	0.9878
0.0177	44.0	4224	0.0490	0.9245	0.9515	0.9378	103	0.9123	0.9123	0.9123	171	0.9695	0.9695	0.9695	131	0.9338	0.9407	0.9373	0.9884
0.0185	45.0	4320	0.0478	0.9083	0.9612	0.9340	103	0.9226	0.9064	0.9145	171	0.9621	0.9695	0.9658	131	0.9315	0.9407	0.9361	0.9878
0.0164	46.0	4416	0.0473	0.9340	0.9612	0.9474	103	0.9235	0.9181	0.9208	171	0.9697	0.9771	0.9734	131	0.9412	0.9481	0.9446	0.9890
0.0168	47.0	4512	0.0444	0.9406	0.9223	0.9314	103	0.9086	0.9298	0.9191	171	0.9618	0.9618	0.9618	131	0.9337	0.9383	0.9360	0.9887
0.0149	48.0	4608	0.0490	0.9604	0.9417	0.9510	103	0.9138	0.9298	0.9217	171	0.9771	0.9771	0.9771	131	0.9458	0.9481	0.9470	0.9890
0.0154	49.0	4704	0.0490	0.9245	0.9515	0.9378	103	0.9390	0.9006	0.9194	171	0.9697	0.9771	0.9734	131	0.9453	0.9383	0.9418	0.9884
0.0148	50.0	4800	0.0464	0.9252	0.9612	0.9429	103	0.9226	0.9064	0.9145	171	0.9697	0.9771	0.9734	131	0.9386	0.9432	0.9409	0.9895
0.016	51.0	4896	0.0577	0.9083	0.9612	0.9340	103	0.9444	0.8947	0.9189	171	0.9545	0.9618	0.9582	131	0.9380	0.9333	0.9356	0.9876
0.0138	52.0	4992	0.0492	0.9231	0.9320	0.9275	103	0.9226	0.9064	0.9145	171	0.9618	0.9618	0.9618	131	0.9355	0.9309	0.9332	0.9873
0.0137	53.0	5088	0.0522	0.9314	0.9223	0.9268	103	0.9080	0.9240	0.9159	171	0.9618	0.9618	0.9618	131	0.9312	0.9358	0.9335	0.9884
0.0127	54.0	5184	0.0505	0.9159	0.9515	0.9333	103	0.9357	0.9357	0.9357	171	0.9618	0.9618	0.9618	131	0.9389	0.9481	0.9435	0.9887
0.0142	55.0	5280	0.0514	0.9238	0.9417	0.9327	103	0.9345	0.9181	0.9263	171	0.9695	0.9695	0.9695	131	0.9431	0.9407	0.9419	0.9890
0.012	56.0	5376	0.0532	0.9159	0.9515	0.9333	103	0.9286	0.9123	0.9204	171	0.9769	0.9695	0.9732	131	0.9407	0.9407	0.9407	0.9881
0.0141	57.0	5472	0.0524	0.9252	0.9612	0.9429	103	0.9398	0.9123	0.9258	171	0.9542	0.9542	0.9542	131	0.9406	0.9383	0.9394	0.9878
0.0123	58.0	5568	0.0503	0.9159	0.9515	0.9333	103	0.9408	0.9298	0.9353	171	0.9695	0.9695	0.9695	131	0.9435	0.9481	0.9458	0.9887
0.0137	59.0	5664	0.0478	0.9327	0.9417	0.9372	103	0.9235	0.9181	0.9208	171	0.9545	0.9618	0.9582	131	0.9360	0.9383	0.9371	0.9884
0.0112	60.0	5760	0.0517	0.9333	0.9515	0.9423	103	0.9398	0.9123	0.9258	171	0.9695	0.9695	0.9695	131	0.9478	0.9407	0.9442	0.9890
0.012	61.0	5856	0.0424	0.93	0.9029	0.9163	103	0.9357	0.9357	0.9357	171	0.9398	0.9542	0.9470	131	0.9356	0.9333	0.9345	0.9884
0.0116	62.0	5952	0.0487	0.9340	0.9612	0.9474	103	0.9461	0.9240	0.9349	171	0.9621	0.9695	0.9658	131	0.9481	0.9481	0.9481	0.9887
0.0116	63.0	6048	0.0477	0.9327	0.9417	0.9372	103	0.9298	0.9298	0.9298	171	0.9545	0.9618	0.9582	131	0.9386	0.9432	0.9409	0.9887
0.0099	64.0	6144	0.0493	0.9238	0.9417	0.9327	103	0.9337	0.9064	0.9199	171	0.9545	0.9618	0.9582	131	0.9380	0.9333	0.9356	0.9878
0.0127	65.0	6240	0.0442	0.9505	0.9320	0.9412	103	0.9364	0.9474	0.9419	171	0.9695	0.9695	0.9695	131	0.9506	0.9506	0.9506	0.9901
0.0097	66.0	6336	0.0476	0.9340	0.9612	0.9474	103	0.9461	0.9240	0.9349	171	0.9621	0.9695	0.9658	131	0.9481	0.9481	0.9481	0.9890
0.0111	67.0	6432	0.0427	0.9346	0.9709	0.9524	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9511	0.9605	0.9558	0.9906
0.0092	68.0	6528	0.0484	0.9340	0.9612	0.9474	103	0.9235	0.9181	0.9208	171	0.9771	0.9771	0.9771	131	0.9435	0.9481	0.9458	0.9903
0.0091	69.0	6624	0.0479	0.9346	0.9709	0.9524	103	0.9521	0.9298	0.9408	171	0.9771	0.9771	0.9771	131	0.9556	0.9556	0.9556	0.9898
0.0102	70.0	6720	0.0474	0.9259	0.9709	0.9479	103	0.9573	0.9181	0.9373	171	0.9771	0.9771	0.9771	131	0.9553	0.9506	0.9530	0.9895
0.0099	71.0	6816	0.0448	0.9320	0.9320	0.9320	103	0.9148	0.9415	0.9280	171	0.9771	0.9771	0.9771	131	0.9390	0.9506	0.9448	0.9892
0.0102	72.0	6912	0.0486	0.9333	0.9515	0.9423	103	0.9451	0.9064	0.9254	171	0.9695	0.9695	0.9695	131	0.95	0.9383	0.9441	0.9887
0.0097	73.0	7008	0.0505	0.9245	0.9515	0.9378	103	0.9527	0.9415	0.9471	171	0.9695	0.9695	0.9695	131	0.9507	0.9531	0.9519	0.9895
0.0094	74.0	7104	0.0494	0.9412	0.9320	0.9366	103	0.9353	0.9298	0.9326	171	0.9695	0.9695	0.9695	131	0.9479	0.9432	0.9455	0.9892
0.0083	75.0	7200	0.0489	0.9340	0.9612	0.9474	103	0.9302	0.9357	0.9329	171	0.9695	0.9695	0.9695	131	0.9438	0.9531	0.9484	0.9903
0.0084	76.0	7296	0.0458	0.9245	0.9515	0.9378	103	0.9467	0.9357	0.9412	171	0.9695	0.9695	0.9695	131	0.9483	0.9506	0.9494	0.9898
0.0088	77.0	7392	0.0488	0.9245	0.9515	0.9378	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9455	0.9432	0.9444	0.9892
0.0091	78.0	7488	0.0486	0.9167	0.9612	0.9384	103	0.9524	0.9357	0.9440	171	0.9771	0.9771	0.9771	131	0.9509	0.9556	0.9532	0.9898
0.0078	79.0	7584	0.0478	0.9167	0.9612	0.9384	103	0.9521	0.9298	0.9408	171	0.9695	0.9695	0.9695	131	0.9483	0.9506	0.9494	0.9892
0.0071	80.0	7680	0.0472	0.9231	0.9320	0.9275	103	0.9306	0.9415	0.9360	171	0.9695	0.9695	0.9695	131	0.9412	0.9481	0.9446	0.9901
0.0095	81.0	7776	0.0462	0.9417	0.9417	0.9417	103	0.9205	0.9474	0.9337	171	0.9771	0.9771	0.9771	131	0.9439	0.9556	0.9497	0.9901
0.008	82.0	7872	0.0491	0.9245	0.9515	0.9378	103	0.9240	0.9240	0.9240	171	0.9771	0.9771	0.9771	131	0.9412	0.9481	0.9446	0.9898
0.0075	83.0	7968	0.0492	0.9412	0.9320	0.9366	103	0.9364	0.9474	0.9419	171	0.9771	0.9771	0.9771	131	0.9507	0.9531	0.9519	0.9901
0.0071	84.0	8064	0.0497	0.9423	0.9515	0.9469	103	0.9306	0.9415	0.9360	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9906
0.0074	85.0	8160	0.0506	0.9231	0.9320	0.9275	103	0.9294	0.9240	0.9267	171	0.9771	0.9771	0.9771	131	0.9432	0.9432	0.9432	0.9895
0.0074	86.0	8256	0.0508	0.9252	0.9612	0.9429	103	0.9412	0.9357	0.9384	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9903
0.0071	87.0	8352	0.0505	0.9223	0.9223	0.9223	103	0.9253	0.9415	0.9333	171	0.9771	0.9771	0.9771	131	0.9412	0.9481	0.9446	0.9901
0.006	88.0	8448	0.0518	0.9143	0.9320	0.9231	103	0.9357	0.9357	0.9357	171	0.9771	0.9771	0.9771	131	0.9435	0.9481	0.9458	0.9903
0.0085	89.0	8544	0.0490	0.9238	0.9417	0.9327	103	0.9310	0.9474	0.9391	171	0.9771	0.9771	0.9771	131	0.9439	0.9556	0.9497	0.9903
0.0067	90.0	8640	0.0488	0.9245	0.9515	0.9378	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9909
0.0075	91.0	8736	0.0488	0.9245	0.9515	0.9378	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9901
0.0064	92.0	8832	0.0488	0.9333	0.9515	0.9423	103	0.9474	0.9474	0.9474	171	0.9771	0.9771	0.9771	131	0.9533	0.9580	0.9557	0.9906
0.007	93.0	8928	0.0497	0.9245	0.9515	0.9378	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9906
0.0072	94.0	9024	0.0493	0.9333	0.9515	0.9423	103	0.9419	0.9474	0.9446	171	0.9771	0.9771	0.9771	131	0.9510	0.9580	0.9545	0.9912
0.0072	95.0	9120	0.0495	0.9245	0.9515	0.9378	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9909
0.0063	96.0	9216	0.0481	0.9245	0.9515	0.9378	103	0.9415	0.9415	0.9415	171	0.9771	0.9771	0.9771	131	0.9485	0.9556	0.9520	0.9909
0.0056	97.0	9312	0.0488	0.9151	0.9417	0.9282	103	0.9357	0.9357	0.9357	171	0.9771	0.9771	0.9771	131	0.9436	0.9506	0.9471	0.9903
0.0069	98.0	9408	0.0481	0.9333	0.9515	0.9423	103	0.9419	0.9474	0.9446	171	0.9771	0.9771	0.9771	131	0.9510	0.9580	0.9545	0.9912
0.006	99.0	9504	0.0481	0.9245	0.9515	0.9378	103	0.9357	0.9357	0.9357	171	0.9771	0.9771	0.9771	131	0.9461	0.9531	0.9496	0.9906
0.0063	100.0	9600	0.0479	0.9333	0.9515	0.9423	103	0.9419	0.9474	0.9446	171	0.9771	0.9771	0.9771	131	0.9510	0.9580	0.9545	0.9912

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-pt-pl50-4

nerui-pt-pl50-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl50-4