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apwic/
nerui-pt-pl10-3

Name: nerui-pt-pl10-3
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl10-3

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: 0.0600
Location Precision: 0.8646
Location Recall: 0.9651
Location F1: 0.9121
Location Number: 86
Organization Precision: 0.9480
Organization Recall: 0.9213
Organization F1: 0.9345
Organization Number: 178
Person Precision: 0.9766
Person Recall: 0.9766
Person F1: 0.9766
Person Number: 128
Overall Precision: 0.9370
Overall Recall: 0.9490
Overall F1: 0.9430
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
0.8464	1.0	96	0.3799	0.0	0.0	0.0	86	0.2409	0.2978	0.2663	178	0.3154	0.3672	0.3394	128	0.2667	0.2551	0.2608	0.8748
0.3634	2.0	192	0.2157	0.3509	0.4651	0.4	86	0.6235	0.5674	0.5941	178	0.5843	0.7578	0.6599	128	0.5385	0.6071	0.5707	0.9331
0.1975	3.0	288	0.0942	0.7831	0.7558	0.7692	86	0.7304	0.8371	0.7801	178	0.9470	0.9766	0.9615	128	0.8091	0.8648	0.8360	0.9692
0.1277	4.0	384	0.0909	0.7396	0.8256	0.7802	86	0.8110	0.7472	0.7778	178	0.9466	0.9688	0.9575	128	0.8389	0.8367	0.8378	0.9703
0.1039	5.0	480	0.0632	0.7551	0.8605	0.8043	86	0.8876	0.8876	0.8876	178	0.9688	0.9688	0.9688	128	0.8812	0.9082	0.8945	0.9784
0.0916	6.0	576	0.0719	0.6724	0.9070	0.7723	86	0.9032	0.7865	0.8408	178	0.9615	0.9766	0.9690	128	0.8554	0.875	0.8651	0.9744
0.0815	7.0	672	0.0528	0.8409	0.8605	0.8506	86	0.8743	0.9382	0.9051	178	0.9843	0.9766	0.9804	128	0.9015	0.9337	0.9173	0.9841
0.0776	8.0	768	0.0514	0.8824	0.8721	0.8772	86	0.8967	0.9270	0.9116	178	0.9764	0.9688	0.9725	128	0.9192	0.9286	0.9239	0.9830
0.0697	9.0	864	0.0519	0.7961	0.9535	0.8677	86	0.9070	0.8764	0.8914	178	0.9843	0.9766	0.9804	128	0.9030	0.9260	0.9144	0.9825
0.0641	10.0	960	0.0573	0.8119	0.9535	0.8770	86	0.9075	0.8820	0.8946	178	0.9766	0.9766	0.9766	128	0.9055	0.9286	0.9169	0.9803
0.0613	11.0	1056	0.0494	0.8646	0.9651	0.9121	86	0.9527	0.9045	0.9280	178	0.9766	0.9766	0.9766	128	0.9389	0.9413	0.9401	0.9854
0.0578	12.0	1152	0.0461	0.8438	0.9419	0.8901	86	0.8956	0.9157	0.9056	178	0.9690	0.9766	0.9728	128	0.9066	0.9413	0.9237	0.9857
0.0527	13.0	1248	0.0581	0.8367	0.9535	0.8913	86	0.9294	0.8876	0.9080	178	0.9538	0.9688	0.9612	128	0.9146	0.9286	0.9215	0.9827
0.0485	14.0	1344	0.0523	0.8298	0.9070	0.8667	86	0.9306	0.9045	0.9174	178	0.9615	0.9766	0.9690	128	0.9169	0.9286	0.9227	0.9830
0.0467	15.0	1440	0.0490	0.8778	0.9186	0.8977	86	0.9266	0.9213	0.9239	178	0.9615	0.9766	0.9690	128	0.9270	0.9388	0.9328	0.9849
0.0459	16.0	1536	0.0469	0.8421	0.9302	0.8840	86	0.9261	0.9157	0.9209	178	0.9612	0.9688	0.9650	128	0.9175	0.9362	0.9268	0.9865
0.0449	17.0	1632	0.0481	0.8632	0.9535	0.9061	86	0.9153	0.9101	0.9127	178	0.9615	0.9766	0.9690	128	0.9179	0.9413	0.9295	0.9854
0.0419	18.0	1728	0.0519	0.8511	0.9302	0.8889	86	0.9405	0.8876	0.9133	178	0.9766	0.9766	0.9766	128	0.9308	0.9260	0.9284	0.9843
0.0408	19.0	1824	0.0408	0.9080	0.9186	0.9133	86	0.9341	0.9551	0.9444	178	0.9766	0.9766	0.9766	128	0.9421	0.9541	0.9480	0.9879
0.0378	20.0	1920	0.0570	0.8081	0.9302	0.8649	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9175	0.9362	0.9268	0.9852
0.0373	21.0	2016	0.0538	0.8632	0.9535	0.9061	86	0.9521	0.8933	0.9217	178	0.9690	0.9766	0.9728	128	0.9361	0.9337	0.9349	0.9846
0.0369	22.0	2112	0.0506	0.8351	0.9419	0.8852	86	0.9364	0.9101	0.9231	178	0.9690	0.9766	0.9728	128	0.9223	0.9388	0.9305	0.9854
0.0357	23.0	2208	0.0469	0.8646	0.9651	0.9121	86	0.9480	0.9213	0.9345	178	0.9690	0.9766	0.9728	128	0.9347	0.9490	0.9418	0.9879
0.0347	24.0	2304	0.0499	0.8817	0.9535	0.9162	86	0.9322	0.9270	0.9296	178	0.9766	0.9766	0.9766	128	0.9347	0.9490	0.9418	0.9860
0.034	25.0	2400	0.0472	0.8696	0.9302	0.8989	86	0.9368	0.9157	0.9261	178	0.9690	0.9766	0.9728	128	0.9316	0.9388	0.9352	0.9868
0.031	26.0	2496	0.0428	0.8737	0.9651	0.9171	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9879
0.0302	27.0	2592	0.0519	0.8317	0.9767	0.8984	86	0.9253	0.9045	0.9148	178	0.9766	0.9766	0.9766	128	0.9181	0.9439	0.9308	0.9868
0.0288	28.0	2688	0.0437	0.8901	0.9419	0.9153	86	0.9385	0.9438	0.9412	178	0.9843	0.9766	0.9804	128	0.9421	0.9541	0.9480	0.9879
0.0282	29.0	2784	0.0406	0.9011	0.9535	0.9266	86	0.9389	0.9494	0.9441	178	0.9843	0.9766	0.9804	128	0.9447	0.9592	0.9519	0.9903
0.0279	30.0	2880	0.0520	0.8469	0.9651	0.9022	86	0.9651	0.9326	0.9486	178	0.9766	0.9766	0.9766	128	0.9397	0.9541	0.9468	0.9862
0.0261	31.0	2976	0.0489	0.8317	0.9767	0.8984	86	0.9306	0.9045	0.9174	178	0.9766	0.9766	0.9766	128	0.9204	0.9439	0.9320	0.9868
0.0239	32.0	3072	0.0426	0.8542	0.9535	0.9011	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9273	0.9439	0.9355	0.9889
0.0262	33.0	3168	0.0508	0.8617	0.9419	0.9000	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9296	0.9439	0.9367	0.9865
0.0233	34.0	3264	0.0522	0.8710	0.9419	0.9050	86	0.9371	0.9213	0.9292	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9868
0.0234	35.0	3360	0.0454	0.8557	0.9651	0.9071	86	0.9593	0.9270	0.9429	178	0.9843	0.9766	0.9804	128	0.9419	0.9515	0.9467	0.9889
0.0242	36.0	3456	0.0452	0.8632	0.9535	0.9061	86	0.9657	0.9494	0.9575	178	0.9843	0.9766	0.9804	128	0.9471	0.9592	0.9531	0.9892
0.0223	37.0	3552	0.0422	0.8723	0.9535	0.9111	86	0.9432	0.9326	0.9379	178	0.9766	0.9766	0.9766	128	0.9372	0.9515	0.9443	0.9892
0.0225	38.0	3648	0.0490	0.82	0.9535	0.8817	86	0.9357	0.8989	0.9169	178	0.9766	0.9766	0.9766	128	0.9198	0.9362	0.9279	0.9860
0.0233	39.0	3744	0.0521	0.8485	0.9767	0.9081	86	0.9326	0.9326	0.9326	178	0.9766	0.9766	0.9766	128	0.9259	0.9566	0.9410	0.9870
0.0197	40.0	3840	0.0488	0.8710	0.9419	0.9050	86	0.9235	0.9494	0.9363	178	0.9843	0.9766	0.9804	128	0.9305	0.9566	0.9434	0.9884
0.0205	41.0	3936	0.0494	0.8817	0.9535	0.9162	86	0.9379	0.9326	0.9352	178	0.9766	0.9766	0.9766	128	0.9372	0.9515	0.9443	0.9884
0.0206	42.0	4032	0.0436	0.8989	0.9302	0.9143	86	0.9379	0.9326	0.9352	178	0.9766	0.9766	0.9766	128	0.9416	0.9464	0.9440	0.9887
0.0194	43.0	4128	0.0476	0.8723	0.9535	0.9111	86	0.9486	0.9326	0.9405	178	0.9766	0.9766	0.9766	128	0.9395	0.9515	0.9455	0.9889
0.019	44.0	4224	0.0517	0.8454	0.9535	0.8962	86	0.9529	0.9101	0.9310	178	0.9766	0.9766	0.9766	128	0.9342	0.9413	0.9377	0.9876
0.0199	45.0	4320	0.0490	0.8632	0.9535	0.9061	86	0.9540	0.9326	0.9432	178	0.9766	0.9766	0.9766	128	0.9395	0.9515	0.9455	0.9881
0.0165	46.0	4416	0.0579	0.8660	0.9767	0.9180	86	0.9415	0.9045	0.9226	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9862
0.0178	47.0	4512	0.0522	0.8542	0.9535	0.9011	86	0.9422	0.9157	0.9288	178	0.9843	0.9766	0.9804	128	0.9343	0.9439	0.9391	0.9876
0.0173	48.0	4608	0.0547	0.8632	0.9535	0.9061	86	0.9379	0.9326	0.9352	178	0.9843	0.9766	0.9804	128	0.9348	0.9515	0.9431	0.9879
0.0176	49.0	4704	0.0536	0.8632	0.9535	0.9061	86	0.9593	0.9270	0.9429	178	0.9766	0.9766	0.9766	128	0.9418	0.9490	0.9454	0.9873
0.0173	50.0	4800	0.0541	0.8681	0.9186	0.8927	86	0.9486	0.9326	0.9405	178	0.9843	0.9766	0.9804	128	0.9415	0.9439	0.9427	0.9870
0.016	51.0	4896	0.0534	0.8602	0.9302	0.8939	86	0.9408	0.8933	0.9164	178	0.9766	0.9766	0.9766	128	0.9333	0.9286	0.9309	0.9852
0.0144	52.0	4992	0.0561	0.8710	0.9419	0.9050	86	0.9368	0.9157	0.9261	178	0.9843	0.9766	0.9804	128	0.9365	0.9413	0.9389	0.9879
0.0163	53.0	5088	0.0604	0.8737	0.9651	0.9171	86	0.9322	0.9270	0.9296	178	0.9766	0.9766	0.9766	128	0.9325	0.9515	0.9419	0.9876
0.0157	54.0	5184	0.0543	0.8737	0.9651	0.9171	86	0.9368	0.9157	0.9261	178	0.9766	0.9766	0.9766	128	0.9345	0.9464	0.9404	0.9868
0.015	55.0	5280	0.0539	0.875	0.9767	0.9231	86	0.9368	0.9157	0.9261	178	0.9766	0.9766	0.9766	128	0.9347	0.9490	0.9418	0.9876
0.0124	56.0	5376	0.0579	0.8646	0.9651	0.9121	86	0.9360	0.9045	0.9200	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9868
0.0143	57.0	5472	0.0590	0.84	0.9767	0.9032	86	0.9148	0.9045	0.9096	178	0.9766	0.9766	0.9766	128	0.9158	0.9439	0.9296	0.9860
0.014	58.0	5568	0.0608	0.8660	0.9767	0.9180	86	0.9415	0.9045	0.9226	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9868
0.0139	59.0	5664	0.0554	0.8723	0.9535	0.9111	86	0.9435	0.9382	0.9408	178	0.9766	0.9766	0.9766	128	0.9373	0.9541	0.9456	0.9873
0.0147	60.0	5760	0.0585	0.8557	0.9651	0.9071	86	0.9480	0.9213	0.9345	178	0.9843	0.9766	0.9804	128	0.9370	0.9490	0.9430	0.9870
0.0136	61.0	5856	0.0562	0.8723	0.9535	0.9111	86	0.9435	0.9382	0.9408	178	0.9766	0.9766	0.9766	128	0.9373	0.9541	0.9456	0.9870
0.0134	62.0	5952	0.0526	0.8830	0.9651	0.9222	86	0.9282	0.9438	0.9359	178	0.9766	0.9766	0.9766	128	0.9330	0.9592	0.9459	0.9884
0.014	63.0	6048	0.0560	0.8925	0.9651	0.9274	86	0.9278	0.9382	0.9330	178	0.9766	0.9766	0.9766	128	0.9352	0.9566	0.9458	0.9876
0.0136	64.0	6144	0.0489	0.8913	0.9535	0.9213	86	0.9282	0.9438	0.9359	178	0.9766	0.9766	0.9766	128	0.9352	0.9566	0.9458	0.9892
0.0124	65.0	6240	0.0550	0.8925	0.9651	0.9274	86	0.9659	0.9551	0.9605	178	0.9843	0.9766	0.9804	128	0.9545	0.9643	0.9594	0.9895
0.0117	66.0	6336	0.0521	0.8632	0.9535	0.9061	86	0.9540	0.9326	0.9432	178	0.9766	0.9766	0.9766	128	0.9395	0.9515	0.9455	0.9887
0.0127	67.0	6432	0.0509	0.8632	0.9535	0.9061	86	0.9655	0.9438	0.9545	178	0.9843	0.9766	0.9804	128	0.9470	0.9566	0.9518	0.9892
0.012	68.0	6528	0.0563	0.9	0.9419	0.9205	86	0.9282	0.9438	0.9359	178	0.9766	0.9766	0.9766	128	0.9373	0.9541	0.9456	0.9879
0.0118	69.0	6624	0.0548	0.8660	0.9767	0.9180	86	0.9483	0.9270	0.9375	178	0.9766	0.9766	0.9766	128	0.9373	0.9541	0.9456	0.9887
0.0119	70.0	6720	0.0591	0.8557	0.9651	0.9071	86	0.9375	0.9270	0.9322	178	0.9766	0.9766	0.9766	128	0.9302	0.9515	0.9407	0.9873
0.0119	71.0	6816	0.0608	0.8913	0.9535	0.9213	86	0.9286	0.9494	0.9389	178	0.9766	0.9766	0.9766	128	0.9353	0.9592	0.9471	0.9870
0.0102	72.0	6912	0.0570	0.8817	0.9535	0.9162	86	0.9382	0.9382	0.9382	178	0.9766	0.9766	0.9766	128	0.9373	0.9541	0.9456	0.9879
0.0108	73.0	7008	0.0602	0.8571	0.9767	0.9130	86	0.9422	0.9157	0.9288	178	0.9766	0.9766	0.9766	128	0.9323	0.9490	0.9406	0.9876
0.0112	74.0	7104	0.0605	0.8542	0.9535	0.9011	86	0.9477	0.9157	0.9314	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9870
0.011	75.0	7200	0.0666	0.84	0.9767	0.9032	86	0.9133	0.8876	0.9003	178	0.9766	0.9766	0.9766	128	0.9152	0.9362	0.9256	0.9857
0.0098	76.0	7296	0.0570	0.8723	0.9535	0.9111	86	0.9326	0.9326	0.9326	178	0.9766	0.9766	0.9766	128	0.9325	0.9515	0.9419	0.9879
0.0098	77.0	7392	0.0579	0.8646	0.9651	0.9121	86	0.9322	0.9270	0.9296	178	0.9766	0.9766	0.9766	128	0.9302	0.9515	0.9407	0.9876
0.0099	78.0	7488	0.0626	0.8571	0.9767	0.9130	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9277	0.9490	0.9382	0.9865
0.0107	79.0	7584	0.0580	0.8830	0.9651	0.9222	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9345	0.9464	0.9404	0.9873
0.0103	80.0	7680	0.0593	0.8660	0.9767	0.9180	86	0.9326	0.9326	0.9326	178	0.9688	0.9688	0.9688	128	0.9280	0.9541	0.9409	0.9881
0.0091	81.0	7776	0.0626	0.8660	0.9767	0.9180	86	0.9543	0.9382	0.9462	178	0.9766	0.9766	0.9766	128	0.94	0.9592	0.9495	0.9887
0.0105	82.0	7872	0.0579	0.8646	0.9651	0.9121	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9298	0.9464	0.9381	0.9868
0.0103	83.0	7968	0.0657	0.8660	0.9767	0.9180	86	0.9527	0.9045	0.9280	178	0.9766	0.9766	0.9766	128	0.9391	0.9439	0.9415	0.9865
0.0118	84.0	8064	0.0636	0.8660	0.9767	0.9180	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9322	0.9464	0.9392	0.9865
0.0095	85.0	8160	0.0632	0.8632	0.9535	0.9061	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9295	0.9413	0.9354	0.9868
0.0089	86.0	8256	0.0606	0.8660	0.9767	0.9180	86	0.9415	0.9045	0.9226	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9862
0.0086	87.0	8352	0.0599	0.8557	0.9651	0.9071	86	0.9480	0.9213	0.9345	178	0.9766	0.9766	0.9766	128	0.9347	0.9490	0.9418	0.9879
0.0104	88.0	8448	0.0596	0.8646	0.9651	0.9121	86	0.9483	0.9270	0.9375	178	0.9766	0.9766	0.9766	128	0.9372	0.9515	0.9443	0.9892
0.0081	89.0	8544	0.0583	0.8646	0.9651	0.9121	86	0.9483	0.9270	0.9375	178	0.9766	0.9766	0.9766	128	0.9372	0.9515	0.9443	0.9895
0.0076	90.0	8640	0.0589	0.8646	0.9651	0.9121	86	0.9429	0.9270	0.9348	178	0.9766	0.9766	0.9766	128	0.9348	0.9515	0.9431	0.9887
0.0092	91.0	8736	0.0612	0.8646	0.9651	0.9121	86	0.9425	0.9213	0.9318	178	0.9766	0.9766	0.9766	128	0.9347	0.9490	0.9418	0.9881
0.008	92.0	8832	0.0596	0.8646	0.9651	0.9121	86	0.9480	0.9213	0.9345	178	0.9766	0.9766	0.9766	128	0.9370	0.9490	0.9430	0.9879
0.0086	93.0	8928	0.0602	0.8646	0.9651	0.9121	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9298	0.9464	0.9381	0.9865
0.0063	94.0	9024	0.0605	0.8646	0.9651	0.9121	86	0.9593	0.9270	0.9429	178	0.9766	0.9766	0.9766	128	0.9419	0.9515	0.9467	0.9876
0.0078	95.0	9120	0.0594	0.8737	0.9651	0.9171	86	0.9543	0.9382	0.9462	178	0.9766	0.9766	0.9766	128	0.9422	0.9566	0.9494	0.9887
0.0079	96.0	9216	0.0588	0.8737	0.9651	0.9171	86	0.9483	0.9270	0.9375	178	0.9766	0.9766	0.9766	128	0.9395	0.9515	0.9455	0.9881
0.0083	97.0	9312	0.0615	0.8646	0.9651	0.9121	86	0.9593	0.9270	0.9429	178	0.9766	0.9766	0.9766	128	0.9419	0.9515	0.9467	0.9881
0.0075	98.0	9408	0.0611	0.8646	0.9651	0.9121	86	0.9535	0.9213	0.9371	178	0.9766	0.9766	0.9766	128	0.9394	0.9490	0.9442	0.9873
0.0083	99.0	9504	0.0600	0.8646	0.9651	0.9121	86	0.9535	0.9213	0.9371	178	0.9766	0.9766	0.9766	128	0.9394	0.9490	0.9442	0.9876
0.0074	100.0	9600	0.0600	0.8646	0.9651	0.9121	86	0.9480	0.9213	0.9345	178	0.9766	0.9766	0.9766	128	0.9370	0.9490	0.9430	0.9879

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl10-3

nerui-pt-pl10-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl10-3