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apwic/
nerui-pt-pl5-4

Name: nerui-pt-pl5-4
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl5-4

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: 0.0497
Location Precision: 0.9423
Location Recall: 0.9515
Location F1: 0.9469
Location Number: 103
Organization Precision: 0.9401
Organization Recall: 0.9181
Organization F1: 0.9290
Organization Number: 171
Person Precision: 0.9695
Person Recall: 0.9695
Person F1: 0.9695
Person Number: 131
Overall Precision: 0.9502
Overall Recall: 0.9432
Overall F1: 0.9467
Overall Accuracy: 0.9898

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
0.8871	1.0	96	0.3854	1.0	0.0097	0.0192	103	0.2486	0.2632	0.2557	171	0.3333	0.3282	0.3308	131	0.2862	0.2198	0.2486	0.8672
0.3506	2.0	192	0.1984	0.4098	0.4854	0.4444	103	0.6020	0.6901	0.6431	171	0.7368	0.8550	0.7915	131	0.5957	0.6914	0.64	0.9420
0.1958	3.0	288	0.1058	0.7647	0.7573	0.7610	103	0.7513	0.8304	0.7889	171	0.9420	0.9924	0.9665	131	0.8159	0.8642	0.8393	0.9682
0.1391	4.0	384	0.0841	0.8333	0.8252	0.8293	103	0.7772	0.8772	0.8242	171	0.9552	0.9771	0.9660	131	0.8462	0.8963	0.8705	0.9735
0.112	5.0	480	0.0736	0.8673	0.8252	0.8458	103	0.8258	0.8596	0.8424	171	0.9621	0.9695	0.9658	131	0.8799	0.8864	0.8831	0.9768
0.0955	6.0	576	0.0585	0.7686	0.9029	0.8304	103	0.8571	0.8070	0.8313	171	0.9621	0.9695	0.9658	131	0.8647	0.8840	0.8742	0.9790
0.083	7.0	672	0.0581	0.9	0.8738	0.8867	103	0.8482	0.9474	0.8950	171	0.9621	0.9695	0.9658	131	0.8960	0.9358	0.9155	0.9832
0.0751	8.0	768	0.0458	0.8990	0.8641	0.8812	103	0.8413	0.9298	0.8833	171	0.9624	0.9771	0.9697	131	0.8931	0.9284	0.9104	0.9848
0.0713	9.0	864	0.0503	0.825	0.9612	0.8879	103	0.9	0.8421	0.8701	171	0.9697	0.9771	0.9734	131	0.9005	0.9160	0.9082	0.9840
0.0655	10.0	960	0.0430	0.9074	0.9515	0.9289	103	0.9040	0.9357	0.9195	171	0.9627	0.9847	0.9736	131	0.9236	0.9556	0.9393	0.9873
0.063	11.0	1056	0.0381	0.94	0.9126	0.9261	103	0.8798	0.9415	0.9096	171	0.9846	0.9771	0.9808	131	0.9274	0.9457	0.9364	0.9867
0.0577	12.0	1152	0.0397	0.9412	0.9320	0.9366	103	0.8791	0.9357	0.9065	171	0.9624	0.9771	0.9697	131	0.9209	0.9481	0.9343	0.9865
0.0562	13.0	1248	0.0430	0.8496	0.9320	0.8889	103	0.8876	0.8772	0.8824	171	0.9697	0.9771	0.9734	131	0.9034	0.9235	0.9133	0.9843
0.0518	14.0	1344	0.0473	0.8981	0.9417	0.9194	103	0.9102	0.8889	0.8994	171	0.9697	0.9771	0.9734	131	0.9263	0.9309	0.9286	0.9851
0.0517	15.0	1440	0.0441	0.9314	0.9223	0.9268	103	0.8579	0.9181	0.8870	171	0.9699	0.9847	0.9773	131	0.9115	0.9407	0.9259	0.9854
0.0505	16.0	1536	0.0406	0.8972	0.9320	0.9143	103	0.8844	0.8947	0.8895	171	0.9771	0.9771	0.9771	131	0.9173	0.9309	0.9240	0.9851
0.0447	17.0	1632	0.0398	0.8899	0.9417	0.9151	103	0.9012	0.9064	0.9038	171	0.9697	0.9771	0.9734	131	0.9201	0.9383	0.9291	0.9870
0.0411	18.0	1728	0.0407	0.8829	0.9515	0.9159	103	0.8953	0.9006	0.8980	171	0.9771	0.9771	0.9771	131	0.9179	0.9383	0.9280	0.9854
0.0413	19.0	1824	0.0379	0.9083	0.9612	0.9340	103	0.9176	0.9123	0.9150	171	0.9771	0.9771	0.9771	131	0.9341	0.9457	0.9399	0.9876
0.0392	20.0	1920	0.0412	0.9009	0.9709	0.9346	103	0.9281	0.9064	0.9172	171	0.9697	0.9771	0.9734	131	0.9341	0.9457	0.9399	0.9870
0.0383	21.0	2016	0.0393	0.9159	0.9515	0.9333	103	0.9186	0.9240	0.9213	171	0.9695	0.9695	0.9695	131	0.9341	0.9457	0.9399	0.9873
0.0381	22.0	2112	0.0379	0.9	0.9612	0.9296	103	0.9244	0.9298	0.9271	171	0.9624	0.9771	0.9697	131	0.9301	0.9531	0.9415	0.9881
0.0368	23.0	2208	0.0417	0.9074	0.9515	0.9289	103	0.8844	0.8947	0.8895	171	0.9695	0.9695	0.9695	131	0.9175	0.9333	0.9253	0.9865
0.0354	24.0	2304	0.0424	0.9159	0.9515	0.9333	103	0.9107	0.8947	0.9027	171	0.9695	0.9695	0.9695	131	0.9310	0.9333	0.9322	0.9865
0.0345	25.0	2400	0.0387	0.9009	0.9709	0.9346	103	0.9102	0.8889	0.8994	171	0.9695	0.9695	0.9695	131	0.9267	0.9358	0.9312	0.9865
0.0332	26.0	2496	0.0399	0.9490	0.9029	0.9254	103	0.8729	0.9240	0.8977	171	0.9697	0.9771	0.9734	131	0.9221	0.9358	0.9289	0.9867
0.0333	27.0	2592	0.0369	0.9143	0.9320	0.9231	103	0.9017	0.9123	0.9070	171	0.9771	0.9771	0.9771	131	0.9291	0.9383	0.9337	0.9876
0.0324	28.0	2688	0.0376	0.96	0.9320	0.9458	103	0.9096	0.9415	0.9253	171	0.9695	0.9695	0.9695	131	0.9412	0.9481	0.9446	0.9884
0.0299	29.0	2784	0.0383	0.8919	0.9612	0.9252	103	0.9281	0.9064	0.9172	171	0.9695	0.9695	0.9695	131	0.9315	0.9407	0.9361	0.9876
0.0299	30.0	2880	0.0425	0.9412	0.9320	0.9366	103	0.8870	0.9181	0.9023	171	0.9621	0.9695	0.9658	131	0.9246	0.9383	0.9314	0.9867
0.0276	31.0	2976	0.0386	0.9074	0.9515	0.9289	103	0.9162	0.8947	0.9053	171	0.9695	0.9695	0.9695	131	0.9310	0.9333	0.9322	0.9881
0.0281	32.0	3072	0.0393	0.8850	0.9709	0.9259	103	0.9375	0.8772	0.9063	171	0.9695	0.9695	0.9695	131	0.9332	0.9309	0.9320	0.9884
0.0244	33.0	3168	0.0404	0.9099	0.9806	0.9439	103	0.9560	0.8889	0.9212	171	0.9695	0.9695	0.9695	131	0.9476	0.9383	0.9429	0.9895
0.0266	34.0	3264	0.0357	0.9327	0.9417	0.9372	103	0.8983	0.9298	0.9138	171	0.9621	0.9695	0.9658	131	0.9274	0.9457	0.9364	0.9890
0.0255	35.0	3360	0.0411	0.9167	0.9612	0.9384	103	0.9277	0.9006	0.9139	171	0.9621	0.9695	0.9658	131	0.9360	0.9383	0.9371	0.9881
0.0243	36.0	3456	0.0409	0.9167	0.9612	0.9384	103	0.8988	0.8830	0.8909	171	0.9618	0.9618	0.9618	131	0.9238	0.9284	0.9261	0.9873
0.0227	37.0	3552	0.0458	0.9327	0.9417	0.9372	103	0.8944	0.9415	0.9174	171	0.9695	0.9695	0.9695	131	0.9277	0.9506	0.9390	0.9887
0.0244	38.0	3648	0.0444	0.8991	0.9515	0.9245	103	0.9024	0.8655	0.8836	171	0.9621	0.9695	0.9658	131	0.9210	0.9210	0.9210	0.9862
0.024	39.0	3744	0.0389	0.9320	0.9320	0.9320	103	0.8895	0.8947	0.8921	171	0.9621	0.9695	0.9658	131	0.9238	0.9284	0.9261	0.9873
0.0228	40.0	3840	0.0473	0.9167	0.9612	0.9384	103	0.9030	0.8713	0.8869	171	0.9621	0.9695	0.9658	131	0.9259	0.9259	0.9259	0.9865
0.0226	41.0	3936	0.0484	0.9583	0.8932	0.9246	103	0.8715	0.9123	0.8914	171	0.9621	0.9695	0.9658	131	0.9214	0.9259	0.9236	0.9856
0.0221	42.0	4032	0.0408	0.9320	0.9320	0.9320	103	0.9118	0.9064	0.9091	171	0.9621	0.9695	0.9658	131	0.9333	0.9333	0.9333	0.9876
0.0195	43.0	4128	0.0387	0.9091	0.9709	0.9390	103	0.9042	0.8830	0.8935	171	0.9621	0.9695	0.9658	131	0.9242	0.9333	0.9287	0.9876
0.0204	44.0	4224	0.0469	0.9245	0.9515	0.9378	103	0.9281	0.9064	0.9172	171	0.9618	0.9618	0.9618	131	0.9381	0.9358	0.9370	0.9884
0.0216	45.0	4320	0.0481	0.9107	0.9903	0.9488	103	0.9329	0.8947	0.9134	171	0.9697	0.9771	0.9734	131	0.9387	0.9457	0.9422	0.9881
0.0199	46.0	4416	0.0417	0.9174	0.9709	0.9434	103	0.9162	0.8947	0.9053	171	0.9771	0.9771	0.9771	131	0.9361	0.9407	0.9384	0.9873
0.021	47.0	4512	0.0408	0.9159	0.9515	0.9333	103	0.9167	0.9006	0.9086	171	0.9771	0.9771	0.9771	131	0.9360	0.9383	0.9371	0.9890
0.0201	48.0	4608	0.0493	0.8919	0.9612	0.9252	103	0.9024	0.8655	0.8836	171	0.9771	0.9771	0.9771	131	0.9236	0.9259	0.9248	0.9876
0.0203	49.0	4704	0.0423	0.9074	0.9515	0.9289	103	0.9273	0.8947	0.9107	171	0.9771	0.9771	0.9771	131	0.9381	0.9358	0.9370	0.9878
0.0197	50.0	4800	0.0435	0.9333	0.9515	0.9423	103	0.9128	0.9181	0.9155	171	0.9697	0.9771	0.9734	131	0.9364	0.9457	0.9410	0.9878
0.0184	51.0	4896	0.0412	0.9091	0.9709	0.9390	103	0.9273	0.8947	0.9107	171	0.9697	0.9771	0.9734	131	0.9361	0.9407	0.9384	0.9890
0.0197	52.0	4992	0.0405	0.9245	0.9515	0.9378	103	0.9162	0.8947	0.9053	171	0.9697	0.9771	0.9734	131	0.9358	0.9358	0.9358	0.9887
0.0187	53.0	5088	0.0417	0.9245	0.9515	0.9378	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9291	0.9383	0.9337	0.9873
0.0175	54.0	5184	0.0433	0.9327	0.9417	0.9372	103	0.9006	0.9006	0.9006	171	0.9697	0.9771	0.9734	131	0.9312	0.9358	0.9335	0.9865
0.0195	55.0	5280	0.0476	0.9018	0.9806	0.9395	103	0.9387	0.8947	0.9162	171	0.9771	0.9771	0.9771	131	0.9409	0.9432	0.9420	0.9895
0.0176	56.0	5376	0.0457	0.9238	0.9417	0.9327	103	0.9167	0.9006	0.9086	171	0.9771	0.9771	0.9771	131	0.9381	0.9358	0.9370	0.9890
0.0175	57.0	5472	0.0422	0.9327	0.9417	0.9372	103	0.9231	0.9123	0.9176	171	0.9552	0.9771	0.9660	131	0.9361	0.9407	0.9384	0.9878
0.0171	58.0	5568	0.0467	0.9159	0.9515	0.9333	103	0.9387	0.8947	0.9162	171	0.9697	0.9771	0.9734	131	0.9428	0.9358	0.9393	0.9881
0.0165	59.0	5664	0.0456	0.9174	0.9709	0.9434	103	0.9390	0.9006	0.9194	171	0.9624	0.9771	0.9697	131	0.9409	0.9432	0.9420	0.9881
0.0173	60.0	5760	0.0486	0.9091	0.9709	0.9390	103	0.9387	0.8947	0.9162	171	0.9771	0.9771	0.9771	131	0.9431	0.9407	0.9419	0.9887
0.0155	61.0	5856	0.0437	0.9327	0.9417	0.9372	103	0.9172	0.9064	0.9118	171	0.9771	0.9771	0.9771	131	0.9406	0.9383	0.9394	0.9881
0.0151	62.0	5952	0.0527	0.9083	0.9612	0.9340	103	0.9277	0.9006	0.9139	171	0.9624	0.9771	0.9697	131	0.9338	0.9407	0.9373	0.9878
0.0171	63.0	6048	0.0484	0.9252	0.9612	0.9429	103	0.9390	0.9006	0.9194	171	0.9771	0.9771	0.9771	131	0.9478	0.9407	0.9442	0.9895
0.0151	64.0	6144	0.0465	0.9174	0.9709	0.9434	103	0.9444	0.8947	0.9189	171	0.9769	0.9695	0.9732	131	0.9476	0.9383	0.9429	0.9887
0.0146	65.0	6240	0.0482	0.9340	0.9612	0.9474	103	0.9398	0.9123	0.9258	171	0.9549	0.9695	0.9621	131	0.9432	0.9432	0.9432	0.9890
0.0141	66.0	6336	0.0474	0.9099	0.9806	0.9439	103	0.9448	0.9006	0.9222	171	0.9695	0.9695	0.9695	131	0.9432	0.9432	0.9432	0.9887
0.015	67.0	6432	0.0483	0.9266	0.9806	0.9528	103	0.9455	0.9123	0.9286	171	0.9621	0.9695	0.9658	131	0.9458	0.9481	0.9470	0.9892
0.0134	68.0	6528	0.0510	0.9252	0.9612	0.9429	103	0.9448	0.9006	0.9222	171	0.9549	0.9695	0.9621	131	0.9429	0.9383	0.9406	0.9887
0.0151	69.0	6624	0.0473	0.9327	0.9417	0.9372	103	0.9345	0.9181	0.9263	171	0.9695	0.9695	0.9695	131	0.9454	0.9407	0.9431	0.9895
0.0143	70.0	6720	0.0553	0.9327	0.9417	0.9372	103	0.9398	0.9123	0.9258	171	0.9695	0.9695	0.9695	131	0.9476	0.9383	0.9429	0.9892
0.0136	71.0	6816	0.0459	0.9231	0.9320	0.9275	103	0.9181	0.9181	0.9181	171	0.9771	0.9771	0.9771	131	0.9384	0.9407	0.9396	0.9884
0.0132	72.0	6912	0.0523	0.9417	0.9417	0.9417	103	0.9341	0.9123	0.9231	171	0.9695	0.9695	0.9695	131	0.9476	0.9383	0.9429	0.9890
0.0141	73.0	7008	0.0443	0.9340	0.9612	0.9474	103	0.9290	0.9181	0.9235	171	0.9771	0.9771	0.9771	131	0.9458	0.9481	0.9470	0.9884
0.0141	74.0	7104	0.0474	0.9333	0.9515	0.9423	103	0.9401	0.9181	0.9290	171	0.9621	0.9695	0.9658	131	0.9455	0.9432	0.9444	0.9892
0.0127	75.0	7200	0.0530	0.9340	0.9612	0.9474	103	0.9455	0.9123	0.9286	171	0.9621	0.9695	0.9658	131	0.9479	0.9432	0.9455	0.9895
0.0133	76.0	7296	0.0487	0.9417	0.9417	0.9417	103	0.9345	0.9181	0.9263	171	0.9695	0.9695	0.9695	131	0.9478	0.9407	0.9442	0.9895
0.0128	77.0	7392	0.0526	0.9333	0.9515	0.9423	103	0.9390	0.9006	0.9194	171	0.9549	0.9695	0.9621	131	0.9428	0.9358	0.9393	0.9884
0.012	78.0	7488	0.0506	0.9252	0.9612	0.9429	103	0.9448	0.9006	0.9222	171	0.9621	0.9695	0.9658	131	0.9453	0.9383	0.9418	0.9887
0.0117	79.0	7584	0.0514	0.9327	0.9417	0.9372	103	0.9394	0.9064	0.9226	171	0.9695	0.9695	0.9695	131	0.9475	0.9358	0.9416	0.9884
0.0107	80.0	7680	0.0516	0.9327	0.9417	0.9372	103	0.9390	0.9006	0.9194	171	0.9549	0.9695	0.9621	131	0.9426	0.9333	0.9380	0.9881
0.0118	81.0	7776	0.0517	0.9429	0.9612	0.9519	103	0.9455	0.9123	0.9286	171	0.9549	0.9695	0.9621	131	0.9479	0.9432	0.9455	0.9892
0.0111	82.0	7872	0.0513	0.9429	0.9612	0.9519	103	0.9512	0.9123	0.9313	171	0.9695	0.9695	0.9695	131	0.955	0.9432	0.9491	0.9895
0.0104	83.0	7968	0.0516	0.9429	0.9612	0.9519	103	0.9512	0.9123	0.9313	171	0.9621	0.9695	0.9658	131	0.9526	0.9432	0.9479	0.9892
0.0131	84.0	8064	0.0515	0.9346	0.9709	0.9524	103	0.9568	0.9064	0.9309	171	0.9549	0.9695	0.9621	131	0.9502	0.9432	0.9467	0.9890
0.0128	85.0	8160	0.0491	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9549	0.9695	0.9621	131	0.9479	0.9432	0.9455	0.9890
0.0114	86.0	8256	0.0492	0.9333	0.9515	0.9423	103	0.9455	0.9123	0.9286	171	0.9695	0.9695	0.9695	131	0.9501	0.9407	0.9454	0.9895
0.0104	87.0	8352	0.0484	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9895
0.0117	88.0	8448	0.0503	0.9327	0.9417	0.9372	103	0.9455	0.9123	0.9286	171	0.9695	0.9695	0.9695	131	0.95	0.9383	0.9441	0.9892
0.0115	89.0	8544	0.0506	0.9417	0.9417	0.9417	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9525	0.9407	0.9466	0.9895
0.0102	90.0	8640	0.0493	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9898
0.0108	91.0	8736	0.0493	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9898
0.0106	92.0	8832	0.0501	0.9333	0.9515	0.9423	103	0.9455	0.9123	0.9286	171	0.9695	0.9695	0.9695	131	0.9501	0.9407	0.9454	0.9895
0.0117	93.0	8928	0.0492	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9898
0.011	94.0	9024	0.0489	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9898
0.0098	95.0	9120	0.0506	0.9423	0.9515	0.9469	103	0.9458	0.9181	0.9318	171	0.9695	0.9695	0.9695	131	0.9526	0.9432	0.9479	0.9898
0.0114	96.0	9216	0.0495	0.9423	0.9515	0.9469	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9502	0.9432	0.9467	0.9898
0.0095	97.0	9312	0.0496	0.9423	0.9515	0.9469	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9502	0.9432	0.9467	0.9898
0.0105	98.0	9408	0.0492	0.9423	0.9515	0.9469	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9502	0.9432	0.9467	0.9898
0.0107	99.0	9504	0.0496	0.9423	0.9515	0.9469	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9502	0.9432	0.9467	0.9898
0.0108	100.0	9600	0.0497	0.9423	0.9515	0.9469	103	0.9401	0.9181	0.9290	171	0.9695	0.9695	0.9695	131	0.9502	0.9432	0.9467	0.9898

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-pt-pl5-4

nerui-pt-pl5-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl5-4