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apwic/
nerui-pt-pl30-3

Name: nerui-pt-pl30-3
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl30-3

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: 0.0655
Location Precision: 0.8830
Location Recall: 0.9651
Location F1: 0.9222
Location Number: 86
Organization Precision: 0.9368
Organization Recall: 0.9157
Organization F1: 0.9261
Organization Number: 178
Person Precision: 0.9688
Person Recall: 0.9688
Person F1: 0.9688
Person Number: 128
Overall Precision: 0.9343
Overall Recall: 0.9439
Overall F1: 0.9391
Overall Accuracy: 0.9870

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
0.8467	1.0	96	0.3800	0.0	0.0	0.0	86	0.2437	0.2191	0.2308	178	0.3116	0.3359	0.3233	128	0.2733	0.2092	0.2370	0.8699
0.3716	2.0	192	0.2423	0.3929	0.3837	0.3882	86	0.5917	0.5618	0.5764	178	0.6456	0.7969	0.7133	128	0.5718	0.5995	0.5853	0.9309
0.2184	3.0	288	0.1218	0.7412	0.7326	0.7368	86	0.7225	0.7753	0.7480	178	0.9130	0.9844	0.9474	128	0.7899	0.8342	0.8114	0.9649
0.1455	4.0	384	0.0960	0.6822	0.8488	0.7565	86	0.7910	0.7865	0.7887	178	0.9259	0.9766	0.9506	128	0.8067	0.8622	0.8335	0.9714
0.1147	5.0	480	0.0768	0.8	0.8837	0.8398	86	0.8641	0.8933	0.8785	178	0.9398	0.9766	0.9579	128	0.8738	0.9184	0.8955	0.9762
0.1009	6.0	576	0.0727	0.6696	0.8721	0.7576	86	0.9	0.8090	0.8521	178	0.9615	0.9766	0.9690	128	0.8557	0.8776	0.8665	0.9776
0.0893	7.0	672	0.0597	0.8041	0.9070	0.8525	86	0.8703	0.9045	0.8871	178	0.9688	0.9688	0.9688	128	0.8854	0.9260	0.9052	0.9814
0.082	8.0	768	0.0496	0.8556	0.8953	0.875	86	0.8703	0.9045	0.8871	178	0.9609	0.9609	0.9609	128	0.8958	0.9209	0.9082	0.9835
0.0775	9.0	864	0.0508	0.7822	0.9186	0.8449	86	0.9059	0.8652	0.8851	178	0.9766	0.9766	0.9766	128	0.8972	0.9133	0.9052	0.9811
0.0696	10.0	960	0.0515	0.7714	0.9419	0.8482	86	0.9128	0.8820	0.8971	178	0.9843	0.9766	0.9804	128	0.8985	0.9260	0.9121	0.9819
0.0635	11.0	1056	0.0434	0.8495	0.9186	0.8827	86	0.9282	0.9438	0.9359	178	0.9688	0.9688	0.9688	128	0.9229	0.9464	0.9345	0.9854
0.0604	12.0	1152	0.0430	0.8901	0.9419	0.9153	86	0.8978	0.9382	0.9176	178	0.9843	0.9766	0.9804	128	0.9233	0.9515	0.9372	0.9870
0.0557	13.0	1248	0.0416	0.8977	0.9186	0.9080	86	0.8978	0.9382	0.9176	178	0.9688	0.9688	0.9688	128	0.9204	0.9439	0.9320	0.9862
0.051	14.0	1344	0.0443	0.9080	0.9186	0.9133	86	0.9157	0.9157	0.9157	178	0.9843	0.9766	0.9804	128	0.9362	0.9362	0.9362	0.9862
0.0492	15.0	1440	0.0424	0.8778	0.9186	0.8977	86	0.9153	0.9101	0.9127	178	0.9764	0.9688	0.9725	128	0.9264	0.9311	0.9288	0.9868
0.0463	16.0	1536	0.0439	0.9011	0.9535	0.9266	86	0.9180	0.9438	0.9307	178	0.9843	0.9766	0.9804	128	0.9352	0.9566	0.9458	0.9870
0.0417	17.0	1632	0.0444	0.9	0.9419	0.9205	86	0.9235	0.9494	0.9363	178	0.9690	0.9766	0.9728	128	0.9328	0.9566	0.9446	0.9879
0.0407	18.0	1728	0.0415	0.9294	0.9186	0.9240	86	0.9333	0.9438	0.9385	178	0.9843	0.9766	0.9804	128	0.9490	0.9490	0.9490	0.9884
0.0413	19.0	1824	0.0429	0.9080	0.9186	0.9133	86	0.9444	0.9551	0.9497	178	0.9843	0.9766	0.9804	128	0.9492	0.9541	0.9517	0.9879
0.0368	20.0	1920	0.0558	0.8632	0.9535	0.9061	86	0.9162	0.9213	0.9188	178	0.9690	0.9766	0.9728	128	0.9206	0.9464	0.9333	0.9838
0.0361	21.0	2016	0.0473	0.9	0.9419	0.9205	86	0.9270	0.9270	0.9270	178	0.9764	0.9688	0.9725	128	0.9367	0.9439	0.9403	0.9868
0.0355	22.0	2112	0.0557	0.8247	0.9302	0.8743	86	0.9022	0.9326	0.9171	178	0.9609	0.9609	0.9609	128	0.9022	0.9413	0.9213	0.9835
0.0314	23.0	2208	0.0475	0.8901	0.9419	0.9153	86	0.9385	0.9438	0.9412	178	0.9615	0.9766	0.9690	128	0.935	0.9541	0.9444	0.9868
0.0308	24.0	2304	0.0556	0.8384	0.9651	0.8973	86	0.9412	0.8989	0.9195	178	0.9612	0.9688	0.9650	128	0.9221	0.9362	0.9291	0.9852
0.0303	25.0	2400	0.0505	0.9213	0.9535	0.9371	86	0.9371	0.9213	0.9292	178	0.9688	0.9688	0.9688	128	0.9439	0.9439	0.9439	0.9876
0.0288	26.0	2496	0.0486	0.9111	0.9535	0.9318	86	0.9157	0.9157	0.9157	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9860
0.0276	27.0	2592	0.0479	0.8817	0.9535	0.9162	86	0.9176	0.9382	0.9278	178	0.9615	0.9766	0.9690	128	0.9235	0.9541	0.9385	0.9879
0.0264	28.0	2688	0.0468	0.9524	0.9302	0.9412	86	0.8984	0.9438	0.9205	178	0.9535	0.9609	0.9572	128	0.9275	0.9464	0.9369	0.9873
0.0267	29.0	2784	0.0553	0.8632	0.9535	0.9061	86	0.9209	0.9157	0.9183	178	0.9766	0.9766	0.9766	128	0.925	0.9439	0.9343	0.9849
0.0258	30.0	2880	0.0486	0.8989	0.9302	0.9143	86	0.9140	0.9551	0.9341	178	0.9766	0.9766	0.9766	128	0.9305	0.9566	0.9434	0.9868
0.0252	31.0	2976	0.0507	0.9	0.9419	0.9205	86	0.9368	0.9157	0.9261	178	0.9615	0.9766	0.9690	128	0.9365	0.9413	0.9389	0.9860
0.0248	32.0	3072	0.0498	0.9111	0.9535	0.9318	86	0.9101	0.9101	0.9101	178	0.9612	0.9688	0.9650	128	0.9270	0.9388	0.9328	0.9865
0.0233	33.0	3168	0.0516	0.9318	0.9535	0.9425	86	0.9326	0.9326	0.9326	178	0.9766	0.9766	0.9766	128	0.9467	0.9515	0.9491	0.9881
0.0243	34.0	3264	0.0541	0.8632	0.9535	0.9061	86	0.9382	0.9382	0.9382	178	0.9843	0.9766	0.9804	128	0.935	0.9541	0.9444	0.9870
0.0199	35.0	3360	0.0515	0.9111	0.9535	0.9318	86	0.9218	0.9270	0.9244	178	0.9766	0.9766	0.9766	128	0.9370	0.9490	0.9430	0.9870
0.0217	36.0	3456	0.0696	0.8454	0.9535	0.8962	86	0.9235	0.8820	0.9023	178	0.9685	0.9609	0.9647	128	0.9188	0.9235	0.9211	0.9830
0.0215	37.0	3552	0.0597	0.8889	0.9302	0.9091	86	0.9056	0.9157	0.9106	178	0.9766	0.9766	0.9766	128	0.9246	0.9388	0.9316	0.9857
0.0194	38.0	3648	0.0528	0.8723	0.9535	0.9111	86	0.9209	0.9157	0.9183	178	0.9764	0.9688	0.9725	128	0.9271	0.9413	0.9342	0.9857
0.0182	39.0	3744	0.0516	0.8913	0.9535	0.9213	86	0.9261	0.9157	0.9209	178	0.9690	0.9766	0.9728	128	0.9320	0.9439	0.9379	0.9868
0.0187	40.0	3840	0.0568	0.8925	0.9651	0.9274	86	0.9368	0.9157	0.9261	178	0.9766	0.9766	0.9766	128	0.9392	0.9464	0.9428	0.9865
0.0184	41.0	3936	0.0632	0.8710	0.9419	0.9050	86	0.9298	0.8933	0.9112	178	0.9766	0.9766	0.9766	128	0.9311	0.9311	0.9311	0.9843
0.0175	42.0	4032	0.0560	0.8817	0.9535	0.9162	86	0.9153	0.9101	0.9127	178	0.9615	0.9766	0.9690	128	0.9225	0.9413	0.9318	0.9852
0.0177	43.0	4128	0.0562	0.9	0.9419	0.9205	86	0.9157	0.9157	0.9157	178	0.9690	0.9766	0.9728	128	0.9295	0.9413	0.9354	0.9860
0.0181	44.0	4224	0.0505	0.9111	0.9535	0.9318	86	0.9157	0.9157	0.9157	178	0.9688	0.9688	0.9688	128	0.9318	0.9413	0.9365	0.9860
0.0172	45.0	4320	0.0636	0.8723	0.9535	0.9111	86	0.9191	0.8933	0.9060	178	0.9535	0.9609	0.9572	128	0.9192	0.9286	0.9239	0.9854
0.0166	46.0	4416	0.0647	0.8737	0.9651	0.9171	86	0.9186	0.8876	0.9029	178	0.9766	0.9766	0.9766	128	0.9266	0.9337	0.9301	0.9860
0.017	47.0	4512	0.0551	0.8901	0.9419	0.9153	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9296	0.9439	0.9367	0.9868
0.0147	48.0	4608	0.0572	0.8723	0.9535	0.9111	86	0.9148	0.9045	0.9096	178	0.9766	0.9766	0.9766	128	0.9246	0.9388	0.9316	0.9868
0.0138	49.0	4704	0.0527	0.8817	0.9535	0.9162	86	0.9360	0.9045	0.9200	178	0.9766	0.9766	0.9766	128	0.9364	0.9388	0.9376	0.9873
0.0154	50.0	4800	0.0613	0.9419	0.9419	0.9419	86	0.9180	0.9438	0.9307	178	0.9766	0.9766	0.9766	128	0.9421	0.9541	0.9480	0.9865
0.0151	51.0	4896	0.0615	0.8817	0.9535	0.9162	86	0.9422	0.9157	0.9288	178	0.9766	0.9766	0.9766	128	0.9391	0.9439	0.9415	0.9870
0.0144	52.0	4992	0.0548	0.8913	0.9535	0.9213	86	0.9371	0.9213	0.9292	178	0.9688	0.9688	0.9688	128	0.9367	0.9439	0.9403	0.9873
0.0145	53.0	5088	0.0669	0.9080	0.9186	0.9133	86	0.9270	0.9270	0.9270	178	0.9766	0.9766	0.9766	128	0.9389	0.9413	0.9401	0.9860
0.0132	54.0	5184	0.0701	0.9091	0.9302	0.9195	86	0.9106	0.9157	0.9132	178	0.9688	0.9688	0.9688	128	0.9291	0.9362	0.9327	0.9852
0.0133	55.0	5280	0.0593	0.9	0.9419	0.9205	86	0.9419	0.9101	0.9257	178	0.9766	0.9766	0.9766	128	0.9436	0.9388	0.9412	0.9865
0.0126	56.0	5376	0.0588	0.8817	0.9535	0.9162	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9345	0.9464	0.9404	0.9870
0.013	57.0	5472	0.0589	0.8737	0.9651	0.9171	86	0.9371	0.9213	0.9292	178	0.9766	0.9766	0.9766	128	0.9347	0.9490	0.9418	0.9865
0.0126	58.0	5568	0.0615	0.8913	0.9535	0.9213	86	0.9425	0.9213	0.9318	178	0.9766	0.9766	0.9766	128	0.9416	0.9464	0.9440	0.9870
0.0113	59.0	5664	0.0596	0.8925	0.9651	0.9274	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9367	0.9439	0.9403	0.9870
0.0125	60.0	5760	0.0596	0.8925	0.9651	0.9274	86	0.9483	0.9270	0.9375	178	0.9688	0.9688	0.9688	128	0.9418	0.9490	0.9454	0.9876
0.0117	61.0	5856	0.0644	0.9101	0.9419	0.9257	86	0.9375	0.9270	0.9322	178	0.9688	0.9688	0.9688	128	0.9415	0.9439	0.9427	0.9870
0.0113	62.0	5952	0.0621	0.8817	0.9535	0.9162	86	0.9205	0.9101	0.9153	178	0.9688	0.9688	0.9688	128	0.9270	0.9388	0.9328	0.9860
0.0115	63.0	6048	0.0634	0.8817	0.9535	0.9162	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9342	0.9413	0.9377	0.9865
0.0105	64.0	6144	0.0688	0.8817	0.9535	0.9162	86	0.9257	0.9101	0.9178	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9862
0.0115	65.0	6240	0.0591	0.8913	0.9535	0.9213	86	0.9218	0.9270	0.9244	178	0.9766	0.9766	0.9766	128	0.9323	0.9490	0.9406	0.9873
0.0081	66.0	6336	0.0631	0.8913	0.9535	0.9213	86	0.9425	0.9213	0.9318	178	0.9688	0.9688	0.9688	128	0.9391	0.9439	0.9415	0.9873
0.0091	67.0	6432	0.0679	0.8817	0.9535	0.9162	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9342	0.9413	0.9377	0.9862
0.0096	68.0	6528	0.0684	0.9011	0.9535	0.9266	86	0.9261	0.9157	0.9209	178	0.9688	0.9688	0.9688	128	0.9342	0.9413	0.9377	0.9860
0.0098	69.0	6624	0.0686	0.8913	0.9535	0.9213	86	0.9314	0.9157	0.9235	178	0.9766	0.9766	0.9766	128	0.9367	0.9439	0.9403	0.9862
0.0081	70.0	6720	0.0659	0.8817	0.9535	0.9162	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9342	0.9413	0.9377	0.9868
0.0091	71.0	6816	0.0677	0.8913	0.9535	0.9213	86	0.9486	0.9326	0.9405	178	0.9766	0.9766	0.9766	128	0.9443	0.9515	0.9479	0.9873
0.0087	72.0	6912	0.0659	0.8913	0.9535	0.9213	86	0.9474	0.9101	0.9284	178	0.9766	0.9766	0.9766	128	0.9437	0.9413	0.9425	0.9870
0.009	73.0	7008	0.0659	0.8913	0.9535	0.9213	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9389	0.9413	0.9401	0.9862
0.0097	74.0	7104	0.0631	0.8913	0.9535	0.9213	86	0.9419	0.9101	0.9257	178	0.9766	0.9766	0.9766	128	0.9413	0.9413	0.9413	0.9868
0.0096	75.0	7200	0.0627	0.8901	0.9419	0.9153	86	0.9257	0.9101	0.9178	178	0.9766	0.9766	0.9766	128	0.9340	0.9388	0.9364	0.9862
0.0085	76.0	7296	0.0648	0.8913	0.9535	0.9213	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9365	0.9413	0.9389	0.9868
0.0081	77.0	7392	0.0659	0.8913	0.9535	0.9213	86	0.9360	0.9045	0.9200	178	0.9766	0.9766	0.9766	128	0.9388	0.9388	0.9388	0.9862
0.0089	78.0	7488	0.0651	0.8913	0.9535	0.9213	86	0.9583	0.9045	0.9306	178	0.9766	0.9766	0.9766	128	0.9485	0.9388	0.9436	0.9879
0.0075	79.0	7584	0.0643	0.8913	0.9535	0.9213	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9365	0.9413	0.9389	0.9865
0.009	80.0	7680	0.0645	0.9011	0.9535	0.9266	86	0.9375	0.9270	0.9322	178	0.9766	0.9766	0.9766	128	0.9418	0.9490	0.9454	0.9876
0.0103	81.0	7776	0.0637	0.8817	0.9535	0.9162	86	0.9360	0.9045	0.9200	178	0.9766	0.9766	0.9766	128	0.9364	0.9388	0.9376	0.9862
0.0085	82.0	7872	0.0638	0.8817	0.9535	0.9162	86	0.9474	0.9101	0.9284	178	0.9766	0.9766	0.9766	128	0.9413	0.9413	0.9413	0.9868
0.0081	83.0	7968	0.0637	0.8817	0.9535	0.9162	86	0.9419	0.9101	0.9257	178	0.9766	0.9766	0.9766	128	0.9389	0.9413	0.9401	0.9865
0.0073	84.0	8064	0.0631	0.8830	0.9651	0.9222	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9343	0.9439	0.9391	0.9865
0.0067	85.0	8160	0.0622	0.9032	0.9767	0.9385	86	0.9371	0.9213	0.9292	178	0.9688	0.9688	0.9688	128	0.9394	0.9490	0.9442	0.9870
0.0072	86.0	8256	0.0688	0.8830	0.9651	0.9222	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9367	0.9439	0.9403	0.9865
0.0071	87.0	8352	0.0658	0.8925	0.9651	0.9274	86	0.9310	0.9101	0.9205	178	0.9688	0.9688	0.9688	128	0.9342	0.9413	0.9377	0.9865
0.0083	88.0	8448	0.0687	0.8913	0.9535	0.9213	86	0.9209	0.9157	0.9183	178	0.9688	0.9688	0.9688	128	0.9295	0.9413	0.9354	0.9865
0.008	89.0	8544	0.0678	0.8936	0.9767	0.9333	86	0.9529	0.9101	0.9310	178	0.9688	0.9688	0.9688	128	0.9439	0.9439	0.9439	0.9873
0.0058	90.0	8640	0.0681	0.8817	0.9535	0.9162	86	0.92	0.9045	0.9122	178	0.9688	0.9688	0.9688	128	0.9268	0.9362	0.9315	0.9860
0.0076	91.0	8736	0.0648	0.9022	0.9651	0.9326	86	0.9266	0.9213	0.9239	178	0.9688	0.9688	0.9688	128	0.9345	0.9464	0.9404	0.9870
0.0068	92.0	8832	0.0657	0.8913	0.9535	0.9213	86	0.9209	0.9157	0.9183	178	0.9688	0.9688	0.9688	128	0.9295	0.9413	0.9354	0.9865
0.0067	93.0	8928	0.0659	0.8913	0.9535	0.9213	86	0.9209	0.9157	0.9183	178	0.9688	0.9688	0.9688	128	0.9295	0.9413	0.9354	0.9865
0.0084	94.0	9024	0.0652	0.8936	0.9767	0.9333	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9369	0.9464	0.9416	0.9873
0.0061	95.0	9120	0.0647	0.8925	0.9651	0.9274	86	0.9261	0.9157	0.9209	178	0.9688	0.9688	0.9688	128	0.9320	0.9439	0.9379	0.9868
0.0063	96.0	9216	0.0642	0.8913	0.9535	0.9213	86	0.9157	0.9157	0.9157	178	0.9688	0.9688	0.9688	128	0.9271	0.9413	0.9342	0.9868
0.0067	97.0	9312	0.0645	0.8925	0.9651	0.9274	86	0.9371	0.9213	0.9292	178	0.9688	0.9688	0.9688	128	0.9369	0.9464	0.9416	0.9873
0.0072	98.0	9408	0.0660	0.8830	0.9651	0.9222	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9343	0.9439	0.9391	0.9870
0.0069	99.0	9504	0.0655	0.8830	0.9651	0.9222	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9343	0.9439	0.9391	0.9870
0.0068	100.0	9600	0.0655	0.8830	0.9651	0.9222	86	0.9368	0.9157	0.9261	178	0.9688	0.9688	0.9688	128	0.9343	0.9439	0.9391	0.9870

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl30-3

nerui-pt-pl30-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl30-3