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apwic/
nerui-pt-pl5-0

Name: nerui-pt-pl5-0
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl5-0

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: 0.0573
Location Precision: 0.8911
Location Recall: 0.9574
Location F1: 0.9231
Location Number: 94
Organization Precision: 0.9187
Organization Recall: 0.8802
Organization F1: 0.8991
Organization Number: 167
Person Precision: 0.9853
Person Recall: 0.9781
Person F1: 0.9817
Person Number: 137
Overall Precision: 0.9345
Overall Recall: 0.9322
Overall F1: 0.9333
Overall Accuracy: 0.9859

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
0.839	1.0	96	0.3844	0.25	0.0106	0.0204	94	0.2767	0.4192	0.3333	167	0.2259	0.4453	0.2998	137	0.2505	0.3317	0.2854	0.8696
0.3364	2.0	192	0.1918	0.5714	0.4255	0.4878	94	0.5545	0.7305	0.6305	167	0.8591	0.9343	0.8951	137	0.6606	0.7286	0.6930	0.9439
0.1872	3.0	288	0.1045	0.7473	0.7234	0.7351	94	0.7487	0.8383	0.7910	167	0.9441	0.9854	0.9643	137	0.8147	0.8618	0.8376	0.9655
0.1357	4.0	384	0.0794	0.7822	0.8404	0.8103	94	0.7765	0.8323	0.8035	167	0.9714	0.9927	0.9819	137	0.8429	0.8894	0.8655	0.9715
0.1109	5.0	480	0.0615	0.8095	0.9043	0.8543	94	0.8253	0.8204	0.8228	167	0.9926	0.9854	0.9890	137	0.8771	0.8970	0.8870	0.9785
0.0945	6.0	576	0.0598	0.7679	0.9149	0.8350	94	0.8383	0.8383	0.8383	167	0.9926	0.9854	0.9890	137	0.8699	0.9070	0.8881	0.9793
0.0834	7.0	672	0.0513	0.83	0.8830	0.8557	94	0.8588	0.8743	0.8665	167	0.9853	0.9781	0.9817	137	0.8941	0.9121	0.9030	0.9820
0.0779	8.0	768	0.0559	0.7586	0.9362	0.8381	94	0.9073	0.8204	0.8616	167	0.9854	0.9854	0.9854	137	0.8911	0.9045	0.8978	0.9801
0.0707	9.0	864	0.0503	0.7672	0.9468	0.8476	94	0.8831	0.8144	0.8474	167	0.9783	0.9854	0.9818	137	0.8824	0.9045	0.8933	0.9812
0.0677	10.0	960	0.0476	0.8148	0.9362	0.8713	94	0.8834	0.8623	0.8727	167	0.9926	0.9854	0.9890	137	0.9017	0.9221	0.9118	0.9834
0.0634	11.0	1056	0.0480	0.8396	0.9468	0.89	94	0.8994	0.8563	0.8773	167	1.0	0.9854	0.9926	137	0.9175	0.9221	0.9198	0.9843
0.0569	12.0	1152	0.0517	0.85	0.9043	0.8763	94	0.8690	0.8743	0.8716	167	1.0	0.9854	0.9926	137	0.9082	0.9196	0.9139	0.9823
0.0583	13.0	1248	0.0423	0.8958	0.9149	0.9053	94	0.8605	0.8862	0.8732	167	0.9926	0.9854	0.9890	137	0.9134	0.9271	0.9202	0.9848
0.0538	14.0	1344	0.0422	0.8738	0.9574	0.9137	94	0.8855	0.8802	0.8829	167	1.0	0.9854	0.9926	137	0.9208	0.9347	0.9277	0.9859
0.0497	15.0	1440	0.0519	0.8713	0.9362	0.9026	94	0.8690	0.8743	0.8716	167	0.9926	0.9854	0.9890	137	0.9111	0.9271	0.9191	0.9845
0.0472	16.0	1536	0.0422	0.8426	0.9681	0.9010	94	0.9051	0.8563	0.88	167	0.9926	0.9854	0.9890	137	0.9179	0.9271	0.9225	0.9862
0.0437	17.0	1632	0.0422	0.8598	0.9787	0.9154	94	0.9136	0.8862	0.8997	167	1.0	0.9854	0.9926	137	0.9282	0.9422	0.9352	0.9870
0.0433	18.0	1728	0.0486	0.8679	0.9787	0.9200	94	0.9145	0.8323	0.8715	167	0.9926	0.9854	0.9890	137	0.9289	0.9196	0.9242	0.9854
0.0397	19.0	1824	0.0541	0.7965	0.9574	0.8696	94	0.9375	0.8084	0.8682	167	0.9854	0.9854	0.9854	137	0.9137	0.9045	0.9091	0.9829
0.0408	20.0	1920	0.0469	0.8519	0.9787	0.9109	94	0.9281	0.8503	0.8875	167	0.9854	0.9854	0.9854	137	0.9271	0.9271	0.9271	0.9859
0.0388	21.0	2016	0.0433	0.91	0.9681	0.9381	94	0.9036	0.8982	0.9009	167	0.9854	0.9854	0.9854	137	0.9330	0.9447	0.9388	0.9876
0.0373	22.0	2112	0.0441	0.8679	0.9787	0.9200	94	0.8976	0.8922	0.8949	167	0.9926	0.9854	0.9890	137	0.9216	0.9447	0.9330	0.9867
0.0348	23.0	2208	0.0519	0.8182	0.9574	0.8824	94	0.9156	0.8443	0.8785	167	0.9926	0.9854	0.9890	137	0.915	0.9196	0.9173	0.9840
0.0343	24.0	2304	0.0432	0.8812	0.9468	0.9128	94	0.9042	0.9042	0.9042	167	1.0	0.9854	0.9926	137	0.9305	0.9422	0.9363	0.9870
0.0371	25.0	2400	0.0434	0.8667	0.9681	0.9146	94	0.8896	0.8683	0.8788	167	0.9926	0.9781	0.9853	137	0.9181	0.9296	0.9238	0.9851
0.033	26.0	2496	0.0433	0.9072	0.9362	0.9215	94	0.9080	0.8862	0.8970	167	0.9926	0.9781	0.9853	137	0.9367	0.9296	0.9332	0.9867
0.0315	27.0	2592	0.0424	0.9149	0.9149	0.9149	94	0.8935	0.9042	0.8988	167	0.9926	0.9854	0.9890	137	0.9323	0.9347	0.9335	0.9862
0.0287	28.0	2688	0.0464	0.8922	0.9681	0.9286	94	0.9097	0.8443	0.8758	167	0.9854	0.9854	0.9854	137	0.9315	0.9221	0.9268	0.9867
0.0285	29.0	2784	0.0545	0.8182	0.9574	0.8824	94	0.9315	0.8144	0.8690	167	1.0	0.9854	0.9926	137	0.9233	0.9070	0.9151	0.9848
0.0287	30.0	2880	0.0494	0.8333	0.9574	0.8911	94	0.8970	0.8862	0.8916	167	0.9927	0.9927	0.9927	137	0.9122	0.9397	0.9257	0.9854
0.0276	31.0	2976	0.0416	0.8922	0.9681	0.9286	94	0.9182	0.8743	0.8957	167	1.0	0.9854	0.9926	137	0.9394	0.9347	0.9370	0.9878
0.027	32.0	3072	0.0478	0.8426	0.9681	0.9010	94	0.9167	0.8563	0.8854	167	1.0	0.9854	0.9926	137	0.9248	0.9271	0.9260	0.9873
0.0269	33.0	3168	0.0442	0.9	0.9574	0.9278	94	0.9245	0.8802	0.9018	167	0.9926	0.9854	0.9890	137	0.9418	0.9347	0.9382	0.9876
0.0231	34.0	3264	0.0465	0.875	0.9681	0.9192	94	0.9299	0.8743	0.9012	167	0.9926	0.9781	0.9853	137	0.9369	0.9322	0.9345	0.9878
0.0249	35.0	3360	0.0489	0.8273	0.9681	0.8922	94	0.9172	0.8623	0.8889	167	1.0	0.9854	0.9926	137	0.9204	0.9296	0.925	0.9870
0.0245	36.0	3456	0.0452	0.875	0.9681	0.9192	94	0.9255	0.8922	0.9085	167	0.9926	0.9854	0.9890	137	0.9352	0.9422	0.9387	0.9881
0.0236	37.0	3552	0.0536	0.8411	0.9574	0.8955	94	0.9068	0.8743	0.8902	167	1.0	0.9854	0.9926	137	0.9206	0.9322	0.9263	0.9851
0.0225	38.0	3648	0.0538	0.8411	0.9574	0.8955	94	0.9	0.8623	0.8807	167	1.0	0.9854	0.9926	137	0.9179	0.9271	0.9225	0.9851
0.0246	39.0	3744	0.0508	0.88	0.9362	0.9072	94	0.9036	0.8982	0.9009	167	0.9926	0.9854	0.9890	137	0.9279	0.9372	0.9325	0.9859
0.0233	40.0	3840	0.0528	0.8411	0.9574	0.8955	94	0.9193	0.8862	0.9024	167	1.0	0.9854	0.9926	137	0.9256	0.9372	0.9313	0.9856
0.0222	41.0	3936	0.0449	0.8878	0.9255	0.9062	94	0.9042	0.9042	0.9042	167	1.0	0.9854	0.9926	137	0.9325	0.9372	0.9348	0.9865
0.0208	42.0	4032	0.0472	0.9010	0.9681	0.9333	94	0.9096	0.9042	0.9069	167	0.9926	0.9854	0.9890	137	0.9355	0.9472	0.9413	0.9876
0.0199	43.0	4128	0.0570	0.8824	0.9574	0.9184	94	0.8916	0.8862	0.8889	167	0.9926	0.9854	0.9890	137	0.9233	0.9372	0.9302	0.9851
0.0206	44.0	4224	0.0619	0.8491	0.9574	0.9	94	0.9026	0.8323	0.8660	167	0.9779	0.9708	0.9744	137	0.9141	0.9095	0.9118	0.9840
0.0219	45.0	4320	0.0458	0.9175	0.9468	0.9319	94	0.8817	0.8922	0.8869	167	0.9852	0.9708	0.9779	137	0.9252	0.9322	0.9287	0.9867
0.0188	46.0	4416	0.0546	0.9082	0.9468	0.9271	94	0.8896	0.8683	0.8788	167	1.0	0.9781	0.9889	137	0.9316	0.9246	0.9281	0.9845
0.0195	47.0	4512	0.0473	0.8932	0.9787	0.9340	94	0.9141	0.8922	0.9030	167	1.0	0.9854	0.9926	137	0.9377	0.9447	0.9412	0.9878
0.0174	48.0	4608	0.0576	0.8654	0.9574	0.9091	94	0.8994	0.8563	0.8773	167	0.9926	0.9854	0.9890	137	0.9223	0.9246	0.9235	0.9845
0.0188	49.0	4704	0.0533	0.8396	0.9468	0.89	94	0.9006	0.8683	0.8841	167	1.0	0.9781	0.9889	137	0.9177	0.9246	0.9212	0.9845
0.0201	50.0	4800	0.0522	0.8641	0.9468	0.9036	94	0.9068	0.8743	0.8902	167	0.9853	0.9781	0.9817	137	0.9225	0.9271	0.9248	0.9865
0.0184	51.0	4896	0.0543	0.8529	0.9255	0.8878	94	0.9024	0.8862	0.8943	167	1.0	0.9781	0.9889	137	0.9225	0.9271	0.9248	0.9865
0.0167	52.0	4992	0.0494	0.9082	0.9468	0.9271	94	0.9030	0.8922	0.8976	167	0.9851	0.9635	0.9742	137	0.9320	0.9296	0.9308	0.9873
0.0149	53.0	5088	0.0619	0.8544	0.9362	0.8934	94	0.9241	0.8743	0.8985	167	0.9925	0.9708	0.9815	137	0.9291	0.9221	0.9256	0.9851
0.0175	54.0	5184	0.0522	0.9167	0.9362	0.9263	94	0.8988	0.9042	0.9015	167	0.9852	0.9708	0.9779	137	0.9323	0.9347	0.9335	0.9870
0.0145	55.0	5280	0.0509	0.89	0.9468	0.9175	94	0.9146	0.8982	0.9063	167	1.0	0.9854	0.9926	137	0.9373	0.9397	0.9385	0.9884
0.0146	56.0	5376	0.0553	0.8969	0.9255	0.9110	94	0.9152	0.9042	0.9096	167	1.0	0.9854	0.9926	137	0.9395	0.9372	0.9384	0.9873
0.0155	57.0	5472	0.0610	0.8505	0.9681	0.9055	94	0.9108	0.8563	0.8827	167	0.9926	0.9854	0.9890	137	0.9225	0.9271	0.9248	0.9854
0.0145	58.0	5568	0.0565	0.9167	0.9362	0.9263	94	0.8916	0.8862	0.8889	167	0.9853	0.9781	0.9817	137	0.9296	0.9296	0.9296	0.9851
0.0174	59.0	5664	0.0595	0.8302	0.9362	0.88	94	0.8720	0.8563	0.8640	167	0.9926	0.9854	0.9890	137	0.9015	0.9196	0.9104	0.9834
0.0168	60.0	5760	0.0559	0.8654	0.9574	0.9091	94	0.9119	0.8683	0.8896	167	0.9926	0.9854	0.9890	137	0.9273	0.9296	0.9285	0.9859
0.0172	61.0	5856	0.0563	0.9082	0.9468	0.9271	94	0.8817	0.8922	0.8869	167	0.9926	0.9854	0.9890	137	0.9256	0.9372	0.9313	0.9859
0.0142	62.0	5952	0.0599	0.875	0.9681	0.9192	94	0.9119	0.8683	0.8896	167	0.9853	0.9781	0.9817	137	0.9273	0.9296	0.9285	0.9854
0.013	63.0	6048	0.0518	0.8922	0.9681	0.9286	94	0.8855	0.8802	0.8829	167	0.9926	0.9781	0.9853	137	0.9231	0.9347	0.9288	0.9867
0.0137	64.0	6144	0.0639	0.8476	0.9468	0.8945	94	0.9103	0.8503	0.8793	167	0.9926	0.9854	0.9890	137	0.9219	0.9196	0.9208	0.9854
0.0125	65.0	6240	0.0558	0.8922	0.9681	0.9286	94	0.9299	0.8743	0.9012	167	0.9852	0.9708	0.9779	137	0.9391	0.9296	0.9343	0.9870
0.0129	66.0	6336	0.0599	0.8725	0.9468	0.9082	94	0.9119	0.8683	0.8896	167	0.9778	0.9635	0.9706	137	0.9242	0.9196	0.9219	0.9856
0.0124	67.0	6432	0.0655	0.8333	0.9574	0.8911	94	0.9032	0.8383	0.8696	167	0.9853	0.9781	0.9817	137	0.9123	0.9146	0.9134	0.9840
0.0146	68.0	6528	0.0561	0.8911	0.9574	0.9231	94	0.9062	0.8683	0.8869	167	0.9926	0.9854	0.9890	137	0.9320	0.9296	0.9308	0.9862
0.0125	69.0	6624	0.0558	0.8641	0.9468	0.9036	94	0.8810	0.8862	0.8836	167	0.9926	0.9854	0.9890	137	0.9140	0.9347	0.9242	0.9862
0.0113	70.0	6720	0.0547	0.8911	0.9574	0.9231	94	0.9074	0.8802	0.8936	167	0.9853	0.9781	0.9817	137	0.9298	0.9322	0.9310	0.9867
0.0121	71.0	6816	0.0568	0.8725	0.9468	0.9082	94	0.9182	0.8743	0.8957	167	0.9853	0.9781	0.9817	137	0.9295	0.9271	0.9283	0.9862
0.0116	72.0	6912	0.0549	0.8641	0.9468	0.9036	94	0.9012	0.8743	0.8875	167	0.9926	0.9854	0.9890	137	0.9227	0.9296	0.9262	0.9862
0.0139	73.0	7008	0.0564	0.8641	0.9468	0.9036	94	0.9074	0.8802	0.8936	167	0.9853	0.9781	0.9817	137	0.9227	0.9296	0.9262	0.9856
0.012	74.0	7104	0.0532	0.8824	0.9574	0.9184	94	0.9308	0.8862	0.9080	167	0.9779	0.9708	0.9744	137	0.9345	0.9322	0.9333	0.9867
0.0122	75.0	7200	0.0576	0.9	0.9574	0.9278	94	0.9074	0.8802	0.8936	167	0.9853	0.9781	0.9817	137	0.9322	0.9322	0.9322	0.9865
0.0112	76.0	7296	0.0608	0.8725	0.9468	0.9082	94	0.9012	0.8743	0.8875	167	0.9779	0.9708	0.9744	137	0.92	0.9246	0.9223	0.9859
0.0108	77.0	7392	0.0643	0.8558	0.9468	0.8990	94	0.9221	0.8503	0.8847	167	0.9853	0.9781	0.9817	137	0.9264	0.9171	0.9217	0.9851
0.0098	78.0	7488	0.0656	0.8824	0.9574	0.9184	94	0.9006	0.8683	0.8841	167	0.9926	0.9854	0.9890	137	0.9273	0.9296	0.9285	0.9851
0.0107	79.0	7584	0.0604	0.8824	0.9574	0.9184	94	0.9024	0.8862	0.8943	167	0.9926	0.9854	0.9890	137	0.9279	0.9372	0.9325	0.9859
0.0102	80.0	7680	0.0597	0.8911	0.9574	0.9231	94	0.9085	0.8922	0.9003	167	0.9779	0.9708	0.9744	137	0.9277	0.9347	0.9312	0.9867
0.0106	81.0	7776	0.0614	0.8641	0.9468	0.9036	94	0.9241	0.8743	0.8985	167	0.9853	0.9781	0.9817	137	0.9295	0.9271	0.9283	0.9862
0.011	82.0	7872	0.0560	0.9	0.9574	0.9278	94	0.9255	0.8922	0.9085	167	0.9852	0.9708	0.9779	137	0.9394	0.9347	0.9370	0.9870
0.01	83.0	7968	0.0602	0.8725	0.9468	0.9082	94	0.9182	0.8743	0.8957	167	0.9852	0.9708	0.9779	137	0.9293	0.9246	0.9270	0.9865
0.0098	84.0	8064	0.0569	0.8738	0.9574	0.9137	94	0.9068	0.8743	0.8902	167	0.9926	0.9781	0.9853	137	0.9273	0.9296	0.9285	0.9859
0.0105	85.0	8160	0.0580	0.8558	0.9468	0.8990	94	0.9172	0.8623	0.8889	167	0.9852	0.9708	0.9779	137	0.9242	0.9196	0.9219	0.9854
0.0108	86.0	8256	0.0577	0.8824	0.9574	0.9184	94	0.9187	0.8802	0.8991	167	0.9853	0.9781	0.9817	137	0.9322	0.9322	0.9322	0.9865
0.0092	87.0	8352	0.0594	0.8911	0.9574	0.9231	94	0.8802	0.8802	0.8802	167	0.9926	0.9854	0.9890	137	0.9208	0.9347	0.9277	0.9859
0.01	88.0	8448	0.0583	0.8667	0.9681	0.9146	94	0.9	0.8623	0.8807	167	0.9926	0.9854	0.9890	137	0.9227	0.9296	0.9262	0.9859
0.0098	89.0	8544	0.0587	0.8824	0.9574	0.9184	94	0.9018	0.8802	0.8909	167	0.9926	0.9854	0.9890	137	0.9277	0.9347	0.9312	0.9867
0.0093	90.0	8640	0.0555	0.8824	0.9574	0.9184	94	0.9130	0.8802	0.8963	167	0.9926	0.9854	0.9890	137	0.9323	0.9347	0.9335	0.9870
0.0085	91.0	8736	0.0550	0.8911	0.9574	0.9231	94	0.9074	0.8802	0.8936	167	0.9853	0.9781	0.9817	137	0.9298	0.9322	0.9310	0.9865
0.0081	92.0	8832	0.0573	0.8911	0.9574	0.9231	94	0.9074	0.8802	0.8936	167	0.9853	0.9781	0.9817	137	0.9298	0.9322	0.9310	0.9865
0.0101	93.0	8928	0.0610	0.8911	0.9574	0.9231	94	0.9187	0.8802	0.8991	167	0.9926	0.9854	0.9890	137	0.9370	0.9347	0.9358	0.9859
0.0095	94.0	9024	0.0596	0.8922	0.9681	0.9286	94	0.9304	0.8802	0.9046	167	0.9926	0.9854	0.9890	137	0.9419	0.9372	0.9395	0.9865
0.0092	95.0	9120	0.0559	0.8911	0.9574	0.9231	94	0.9080	0.8862	0.8970	167	1.0	0.9854	0.9926	137	0.9348	0.9372	0.9360	0.9873
0.0089	96.0	9216	0.0566	0.8911	0.9574	0.9231	94	0.8963	0.8802	0.8882	167	0.9853	0.9781	0.9817	137	0.9252	0.9322	0.9287	0.9865
0.0085	97.0	9312	0.0570	0.8911	0.9574	0.9231	94	0.9245	0.8802	0.9018	167	0.9853	0.9781	0.9817	137	0.9369	0.9322	0.9345	0.9862
0.0084	98.0	9408	0.0571	0.8911	0.9574	0.9231	94	0.9245	0.8802	0.9018	167	0.9853	0.9781	0.9817	137	0.9369	0.9322	0.9345	0.9862
0.0089	99.0	9504	0.0571	0.8911	0.9574	0.9231	94	0.9187	0.8802	0.8991	167	0.9853	0.9781	0.9817	137	0.9345	0.9322	0.9333	0.9859
0.0093	100.0	9600	0.0573	0.8911	0.9574	0.9231	94	0.9187	0.8802	0.8991	167	0.9853	0.9781	0.9817	137	0.9345	0.9322	0.9333	0.9859

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-pt-pl5-0

nerui-pt-pl5-0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl5-0