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apwic/
nerui-pt-pl30-4

Name: nerui-pt-pl30-4
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl30-4

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: 0.0617
Location Precision: 0.9159
Location Recall: 0.9515
Location F1: 0.9333
Location Number: 103
Organization Precision: 0.9512
Organization Recall: 0.9123
Organization F1: 0.9313
Organization Number: 171
Person Precision: 0.9621
Person Recall: 0.9695
Person F1: 0.9658
Person Number: 131
Overall Precision: 0.9454
Overall Recall: 0.9407
Overall F1: 0.9431
Overall Accuracy: 0.9878

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
0.8501	1.0	96	0.3899	0.0	0.0	0.0	103	0.2283	0.2456	0.2366	171	0.2828	0.3130	0.2971	131	0.2492	0.2049	0.2249	0.8652
0.3538	2.0	192	0.2020	0.3805	0.4175	0.3981	103	0.5340	0.6433	0.5836	171	0.7063	0.8626	0.7766	131	0.5553	0.6568	0.6018	0.9348
0.1818	3.0	288	0.1081	0.8352	0.7379	0.7835	103	0.7514	0.7953	0.7727	171	0.9203	0.9695	0.9442	131	0.8268	0.8370	0.8319	0.9660
0.1278	4.0	384	0.0764	0.8165	0.8641	0.8396	103	0.7989	0.8363	0.8171	171	0.9412	0.9771	0.9588	131	0.8491	0.8889	0.8685	0.9751
0.1043	5.0	480	0.0636	0.8378	0.9029	0.8692	103	0.8529	0.8480	0.8504	171	0.9549	0.9695	0.9621	131	0.8816	0.9012	0.8913	0.9796
0.0902	6.0	576	0.0551	0.8	0.9320	0.8610	103	0.8683	0.8480	0.8580	171	0.9769	0.9695	0.9732	131	0.8825	0.9086	0.8954	0.9818
0.085	7.0	672	0.0493	0.9381	0.8835	0.9100	103	0.8579	0.9181	0.8870	171	0.9769	0.9695	0.9732	131	0.9146	0.9259	0.9202	0.9851
0.0767	8.0	768	0.0471	0.8962	0.9223	0.9091	103	0.8895	0.8947	0.8921	171	0.9769	0.9695	0.9732	131	0.9191	0.9259	0.9225	0.9856
0.0726	9.0	864	0.0472	0.8899	0.9417	0.9151	103	0.8922	0.8713	0.8817	171	0.9846	0.9771	0.9808	131	0.9212	0.9235	0.9223	0.9845
0.0632	10.0	960	0.0418	0.8909	0.9515	0.9202	103	0.8947	0.8947	0.8947	171	0.9621	0.9695	0.9658	131	0.9153	0.9333	0.9242	0.9862
0.0615	11.0	1056	0.0432	0.9048	0.9223	0.9135	103	0.9029	0.9240	0.9133	171	0.9695	0.9695	0.9695	131	0.9246	0.9383	0.9314	0.9878
0.0576	12.0	1152	0.0386	0.9038	0.9126	0.9082	103	0.8876	0.9240	0.9054	171	0.9769	0.9695	0.9732	131	0.9199	0.9358	0.9278	0.9873
0.0559	13.0	1248	0.0401	0.9604	0.9417	0.9510	103	0.9017	0.9123	0.9070	171	0.9697	0.9771	0.9734	131	0.9384	0.9407	0.9396	0.9878
0.0525	14.0	1344	0.0485	0.875	0.9515	0.9116	103	0.9236	0.8480	0.8841	171	0.9697	0.9771	0.9734	131	0.9252	0.9160	0.9206	0.9848
0.0472	15.0	1440	0.0359	0.9340	0.9612	0.9474	103	0.9294	0.9240	0.9267	171	0.9771	0.9771	0.9771	131	0.9459	0.9506	0.9483	0.9903
0.0455	16.0	1536	0.0400	0.9065	0.9417	0.9238	103	0.9444	0.8947	0.9189	171	0.9771	0.9771	0.9771	131	0.945	0.9333	0.9391	0.9881
0.0418	17.0	1632	0.0433	0.9314	0.9223	0.9268	103	0.9195	0.9357	0.9275	171	0.9549	0.9695	0.9621	131	0.9340	0.9432	0.9386	0.9865
0.0395	18.0	1728	0.0411	0.9074	0.9515	0.9289	103	0.9118	0.9064	0.9091	171	0.9549	0.9695	0.9621	131	0.9246	0.9383	0.9314	0.9873
0.0383	19.0	1824	0.0411	0.9238	0.9417	0.9327	103	0.9405	0.9240	0.9322	171	0.9621	0.9695	0.9658	131	0.9432	0.9432	0.9432	0.9878
0.0347	20.0	1920	0.0474	0.8684	0.9612	0.9124	103	0.9379	0.8830	0.9096	171	0.9771	0.9771	0.9771	131	0.9310	0.9333	0.9322	0.9865
0.0368	21.0	2016	0.0400	0.9091	0.9709	0.9390	103	0.9186	0.9240	0.9213	171	0.9697	0.9771	0.9734	131	0.9324	0.9531	0.9426	0.9892
0.0359	22.0	2112	0.0388	0.8991	0.9515	0.9245	103	0.9086	0.9298	0.9191	171	0.9621	0.9695	0.9658	131	0.9231	0.9481	0.9354	0.9870
0.0325	23.0	2208	0.0443	0.8534	0.9612	0.9041	103	0.9130	0.8596	0.8855	171	0.9771	0.9771	0.9771	131	0.9167	0.9235	0.9200	0.9859
0.0309	24.0	2304	0.0464	0.8609	0.9612	0.9083	103	0.9273	0.8947	0.9107	171	0.9624	0.9771	0.9697	131	0.9201	0.9383	0.9291	0.9851
0.0307	25.0	2400	0.0469	0.9	0.9612	0.9296	103	0.9329	0.8947	0.9134	171	0.9621	0.9695	0.9658	131	0.9335	0.9358	0.9346	0.9876
0.0311	26.0	2496	0.0425	0.9604	0.9417	0.9510	103	0.9364	0.9474	0.9419	171	0.9624	0.9771	0.9697	131	0.9509	0.9556	0.9532	0.9892
0.0283	27.0	2592	0.0381	0.9245	0.9515	0.9378	103	0.9244	0.9298	0.9271	171	0.9621	0.9695	0.9658	131	0.9366	0.9481	0.9423	0.9892
0.0294	28.0	2688	0.0444	0.9346	0.9709	0.9524	103	0.9240	0.9240	0.9240	171	0.9697	0.9771	0.9734	131	0.9415	0.9531	0.9472	0.9878
0.0269	29.0	2784	0.0457	0.9167	0.9612	0.9384	103	0.9345	0.9181	0.9263	171	0.9697	0.9771	0.9734	131	0.9412	0.9481	0.9446	0.9887
0.0261	30.0	2880	0.0444	0.9327	0.9417	0.9372	103	0.9086	0.9298	0.9191	171	0.9621	0.9695	0.9658	131	0.9319	0.9457	0.9387	0.9881
0.0272	31.0	2976	0.0400	0.9327	0.9417	0.9372	103	0.9138	0.9298	0.9217	171	0.9697	0.9771	0.9734	131	0.9366	0.9481	0.9423	0.9892
0.0219	32.0	3072	0.0442	0.9135	0.9223	0.9179	103	0.8960	0.9064	0.9012	171	0.9697	0.9771	0.9734	131	0.9242	0.9333	0.9287	0.9878
0.0249	33.0	3168	0.0400	0.8972	0.9320	0.9143	103	0.9172	0.9064	0.9118	171	0.9624	0.9771	0.9697	131	0.9267	0.9358	0.9312	0.9876
0.0244	34.0	3264	0.0420	0.9151	0.9417	0.9282	103	0.9235	0.9181	0.9208	171	0.9621	0.9695	0.9658	131	0.9338	0.9407	0.9373	0.9887
0.0234	35.0	3360	0.0519	0.8919	0.9612	0.9252	103	0.9030	0.8713	0.8869	171	0.9478	0.9695	0.9585	131	0.9146	0.9259	0.9202	0.9859
0.0215	36.0	3456	0.0504	0.8991	0.9515	0.9245	103	0.9202	0.8772	0.8982	171	0.9621	0.9695	0.9658	131	0.9282	0.9259	0.9271	0.9873
0.0219	37.0	3552	0.0520	0.9231	0.9320	0.9275	103	0.9172	0.9064	0.9118	171	0.9621	0.9695	0.9658	131	0.9333	0.9333	0.9333	0.9876
0.0234	38.0	3648	0.0489	0.8684	0.9612	0.9124	103	0.9494	0.8772	0.9119	171	0.9695	0.9695	0.9695	131	0.9330	0.9284	0.9307	0.9876
0.0215	39.0	3744	0.0456	0.9159	0.9515	0.9333	103	0.9455	0.9123	0.9286	171	0.9771	0.9771	0.9771	131	0.9479	0.9432	0.9455	0.9895
0.02	40.0	3840	0.0540	0.9238	0.9417	0.9327	103	0.9329	0.8947	0.9134	171	0.9621	0.9695	0.9658	131	0.9401	0.9309	0.9355	0.9881
0.0192	41.0	3936	0.0514	0.9151	0.9417	0.9282	103	0.9390	0.9006	0.9194	171	0.9695	0.9695	0.9695	131	0.9426	0.9333	0.9380	0.9890
0.0189	42.0	4032	0.0502	0.9151	0.9417	0.9282	103	0.9329	0.8947	0.9134	171	0.9695	0.9695	0.9695	131	0.9401	0.9309	0.9355	0.9873
0.0177	43.0	4128	0.0429	0.9238	0.9417	0.9327	103	0.9176	0.9123	0.9150	171	0.9695	0.9695	0.9695	131	0.9360	0.9383	0.9371	0.9884
0.0161	44.0	4224	0.0555	0.8839	0.9612	0.9209	103	0.9441	0.8889	0.9157	171	0.9695	0.9695	0.9695	131	0.9356	0.9333	0.9345	0.9873
0.0159	45.0	4320	0.0532	0.8772	0.9709	0.9217	103	0.9620	0.8889	0.9240	171	0.9474	0.9618	0.9545	131	0.9333	0.9333	0.9333	0.9878
0.0152	46.0	4416	0.0521	0.8991	0.9515	0.9245	103	0.9333	0.9006	0.9167	171	0.9697	0.9771	0.9734	131	0.9360	0.9383	0.9371	0.9878
0.0171	47.0	4512	0.0536	0.9151	0.9417	0.9282	103	0.9458	0.9181	0.9318	171	0.9621	0.9695	0.9658	131	0.9431	0.9407	0.9419	0.9870
0.0147	48.0	4608	0.0537	0.8870	0.9903	0.9358	103	0.9684	0.8947	0.9301	171	0.9695	0.9695	0.9695	131	0.9455	0.9432	0.9444	0.9884
0.0156	49.0	4704	0.0554	0.9252	0.9612	0.9429	103	0.9281	0.9064	0.9172	171	0.9695	0.9695	0.9695	131	0.9407	0.9407	0.9407	0.9878
0.0149	50.0	4800	0.0611	0.9	0.9612	0.9296	103	0.95	0.8889	0.9184	171	0.9695	0.9695	0.9695	131	0.9426	0.9333	0.9380	0.9884
0.0136	51.0	4896	0.0622	0.8899	0.9417	0.9151	103	0.9620	0.8889	0.9240	171	0.9621	0.9695	0.9658	131	0.9424	0.9284	0.9353	0.9867
0.0142	52.0	4992	0.0523	0.9159	0.9515	0.9333	103	0.9401	0.9181	0.9290	171	0.9621	0.9695	0.9658	131	0.9409	0.9432	0.9420	0.9878
0.0135	53.0	5088	0.0513	0.9167	0.9612	0.9384	103	0.9294	0.9240	0.9267	171	0.9697	0.9771	0.9734	131	0.9390	0.9506	0.9448	0.9878
0.0136	54.0	5184	0.0532	0.9151	0.9417	0.9282	103	0.9012	0.9064	0.9038	171	0.9621	0.9695	0.9658	131	0.9244	0.9358	0.9301	0.9867
0.0121	55.0	5280	0.0602	0.9159	0.9515	0.9333	103	0.9444	0.8947	0.9189	171	0.9695	0.9695	0.9695	131	0.945	0.9333	0.9391	0.9884
0.013	56.0	5376	0.0534	0.9320	0.9320	0.9320	103	0.9240	0.9240	0.9240	171	0.9621	0.9695	0.9658	131	0.9384	0.9407	0.9396	0.9887
0.0133	57.0	5472	0.0589	0.9159	0.9515	0.9333	103	0.9333	0.9006	0.9167	171	0.9695	0.9695	0.9695	131	0.9404	0.9358	0.9381	0.9873
0.013	58.0	5568	0.0602	0.9065	0.9417	0.9238	103	0.9451	0.9064	0.9254	171	0.9695	0.9695	0.9695	131	0.9428	0.9358	0.9393	0.9878
0.0147	59.0	5664	0.0539	0.8981	0.9417	0.9194	103	0.9286	0.9123	0.9204	171	0.9771	0.9771	0.9771	131	0.9361	0.9407	0.9384	0.9887
0.013	60.0	5760	0.0661	0.8991	0.9515	0.9245	103	0.9455	0.9123	0.9286	171	0.9695	0.9695	0.9695	131	0.9407	0.9407	0.9407	0.9873
0.0113	61.0	5856	0.0606	0.9327	0.9417	0.9372	103	0.9398	0.9123	0.9258	171	0.9621	0.9695	0.9658	131	0.9453	0.9383	0.9418	0.9887
0.012	62.0	5952	0.0615	0.9057	0.9320	0.9187	103	0.9212	0.8889	0.9048	171	0.9621	0.9695	0.9658	131	0.9305	0.9259	0.9282	0.9862
0.0129	63.0	6048	0.0613	0.9083	0.9612	0.9340	103	0.9747	0.9006	0.9362	171	0.9621	0.9695	0.9658	131	0.9524	0.9383	0.9453	0.9884
0.0123	64.0	6144	0.0532	0.9327	0.9417	0.9372	103	0.9464	0.9298	0.9381	171	0.9695	0.9695	0.9695	131	0.9504	0.9457	0.9480	0.9895
0.012	65.0	6240	0.0529	0.9143	0.9320	0.9231	103	0.9341	0.9123	0.9231	171	0.9695	0.9695	0.9695	131	0.9404	0.9358	0.9381	0.9887
0.0118	66.0	6336	0.0503	0.9143	0.9320	0.9231	103	0.9231	0.9123	0.9176	171	0.9618	0.9618	0.9618	131	0.9333	0.9333	0.9333	0.9887
0.0121	67.0	6432	0.0564	0.9057	0.9320	0.9187	103	0.9290	0.9181	0.9235	171	0.9695	0.9695	0.9695	131	0.9360	0.9383	0.9371	0.9878
0.0094	68.0	6528	0.0560	0.9083	0.9612	0.9340	103	0.9286	0.9123	0.9204	171	0.9545	0.9618	0.9582	131	0.9315	0.9407	0.9361	0.9878
0.0111	69.0	6624	0.0634	0.9159	0.9515	0.9333	103	0.9394	0.9064	0.9226	171	0.9695	0.9695	0.9695	131	0.9429	0.9383	0.9406	0.9867
0.0107	70.0	6720	0.0596	0.9151	0.9417	0.9282	103	0.9451	0.9064	0.9254	171	0.9695	0.9695	0.9695	131	0.9451	0.9358	0.9404	0.9881
0.0095	71.0	6816	0.0612	0.9151	0.9417	0.9282	103	0.9167	0.9006	0.9086	171	0.9695	0.9695	0.9695	131	0.9333	0.9333	0.9333	0.9876
0.0092	72.0	6912	0.0639	0.9074	0.9515	0.9289	103	0.9506	0.9006	0.9249	171	0.9771	0.9771	0.9771	131	0.9476	0.9383	0.9429	0.9881
0.0095	73.0	7008	0.0673	0.9074	0.9515	0.9289	103	0.9390	0.9006	0.9194	171	0.9695	0.9695	0.9695	131	0.9404	0.9358	0.9381	0.9870
0.0092	74.0	7104	0.0616	0.9151	0.9417	0.9282	103	0.9281	0.9064	0.9172	171	0.9695	0.9695	0.9695	131	0.9381	0.9358	0.9370	0.9873
0.0094	75.0	7200	0.0666	0.8981	0.9417	0.9194	103	0.9563	0.8947	0.9245	171	0.9695	0.9695	0.9695	131	0.9449	0.9309	0.9378	0.9878
0.0093	76.0	7296	0.0617	0.9	0.9612	0.9296	103	0.9568	0.9064	0.9309	171	0.9771	0.9771	0.9771	131	0.9479	0.9432	0.9455	0.9881
0.01	77.0	7392	0.0603	0.9083	0.9612	0.9340	103	0.9394	0.9064	0.9226	171	0.9695	0.9695	0.9695	131	0.9407	0.9407	0.9407	0.9887
0.0087	78.0	7488	0.0609	0.9174	0.9709	0.9434	103	0.9455	0.9123	0.9286	171	0.9771	0.9771	0.9771	131	0.9481	0.9481	0.9481	0.9895
0.0099	79.0	7584	0.0609	0.9151	0.9417	0.9282	103	0.9118	0.9064	0.9091	171	0.9771	0.9771	0.9771	131	0.9337	0.9383	0.9360	0.9876
0.0096	80.0	7680	0.0657	0.9167	0.9612	0.9384	103	0.9455	0.9123	0.9286	171	0.9695	0.9695	0.9695	131	0.9455	0.9432	0.9444	0.9873
0.0095	81.0	7776	0.0611	0.9167	0.9612	0.9384	103	0.9341	0.9123	0.9231	171	0.9621	0.9695	0.9658	131	0.9386	0.9432	0.9409	0.9870
0.0086	82.0	7872	0.0618	0.8839	0.9612	0.9209	103	0.9444	0.8947	0.9189	171	0.9621	0.9695	0.9658	131	0.9335	0.9358	0.9346	0.9865
0.0089	83.0	7968	0.0613	0.9143	0.9320	0.9231	103	0.9394	0.9064	0.9226	171	0.9621	0.9695	0.9658	131	0.9403	0.9333	0.9368	0.9876
0.0082	84.0	8064	0.0685	0.8909	0.9515	0.9202	103	0.9625	0.9006	0.9305	171	0.9621	0.9695	0.9658	131	0.9428	0.9358	0.9393	0.9876
0.008	85.0	8160	0.0658	0.9167	0.9612	0.9384	103	0.9571	0.9123	0.9341	171	0.9621	0.9695	0.9658	131	0.9479	0.9432	0.9455	0.9881
0.0079	86.0	8256	0.0656	0.9143	0.9320	0.9231	103	0.9394	0.9064	0.9226	171	0.9621	0.9695	0.9658	131	0.9403	0.9333	0.9368	0.9876
0.0072	87.0	8352	0.0637	0.9143	0.9320	0.9231	103	0.9451	0.9064	0.9254	171	0.9621	0.9695	0.9658	131	0.9426	0.9333	0.9380	0.9878
0.0074	88.0	8448	0.0641	0.9143	0.9320	0.9231	103	0.9451	0.9064	0.9254	171	0.9621	0.9695	0.9658	131	0.9426	0.9333	0.9380	0.9878
0.0068	89.0	8544	0.0618	0.9151	0.9417	0.9282	103	0.9337	0.9064	0.9199	171	0.9621	0.9695	0.9658	131	0.9381	0.9358	0.9370	0.9873
0.0067	90.0	8640	0.0616	0.9143	0.9320	0.9231	103	0.9451	0.9064	0.9254	171	0.9621	0.9695	0.9658	131	0.9426	0.9333	0.9380	0.9878
0.0078	91.0	8736	0.0586	0.9159	0.9515	0.9333	103	0.9341	0.9123	0.9231	171	0.9771	0.9771	0.9771	131	0.9432	0.9432	0.9432	0.9876
0.0069	92.0	8832	0.0627	0.9159	0.9515	0.9333	103	0.9455	0.9123	0.9286	171	0.9621	0.9695	0.9658	131	0.9431	0.9407	0.9419	0.9876
0.0069	93.0	8928	0.0634	0.9159	0.9515	0.9333	103	0.9455	0.9123	0.9286	171	0.9621	0.9695	0.9658	131	0.9431	0.9407	0.9419	0.9876
0.0084	94.0	9024	0.0633	0.9159	0.9515	0.9333	103	0.9455	0.9123	0.9286	171	0.9621	0.9695	0.9658	131	0.9431	0.9407	0.9419	0.9876
0.0072	95.0	9120	0.0591	0.9151	0.9417	0.9282	103	0.9337	0.9064	0.9199	171	0.9697	0.9771	0.9734	131	0.9406	0.9383	0.9394	0.9876
0.0066	96.0	9216	0.0610	0.9167	0.9612	0.9384	103	0.9512	0.9123	0.9313	171	0.9697	0.9771	0.9734	131	0.9480	0.9457	0.9468	0.9881
0.0074	97.0	9312	0.0623	0.9167	0.9612	0.9384	103	0.9571	0.9123	0.9341	171	0.9621	0.9695	0.9658	131	0.9479	0.9432	0.9455	0.9881
0.0061	98.0	9408	0.0620	0.9167	0.9612	0.9384	103	0.9571	0.9123	0.9341	171	0.9621	0.9695	0.9658	131	0.9479	0.9432	0.9455	0.9881
0.0074	99.0	9504	0.0614	0.9159	0.9515	0.9333	103	0.9512	0.9123	0.9313	171	0.9621	0.9695	0.9658	131	0.9454	0.9407	0.9431	0.9878
0.0067	100.0	9600	0.0617	0.9159	0.9515	0.9333	103	0.9512	0.9123	0.9313	171	0.9621	0.9695	0.9658	131	0.9454	0.9407	0.9431	0.9878

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl30-4

nerui-pt-pl30-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl30-4