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apwic/
nerui-pt-pl20-3

Name: nerui-pt-pl20-3
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl20-3

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: 0.0737
Location Precision: 0.8696
Location Recall: 0.9302
Location F1: 0.8989
Location Number: 86
Organization Precision: 0.9205
Organization Recall: 0.9101
Organization F1: 0.9153
Organization Number: 178
Person Precision: 0.9690
Person Recall: 0.9766
Person F1: 0.9728
Person Number: 128
Overall Precision: 0.9244
Overall Recall: 0.9362
Overall F1: 0.9303
Overall Accuracy: 0.9854

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
0.8524	1.0	96	0.3784	0.0	0.0	0.0	86	0.2622	0.2416	0.2515	178	0.3011	0.4141	0.3487	128	0.2783	0.2449	0.2605	0.8726
0.3653	2.0	192	0.2446	0.3478	0.3721	0.3596	86	0.5629	0.4775	0.5167	178	0.5515	0.7109	0.6212	128	0.5098	0.5306	0.5200	0.9266
0.2075	3.0	288	0.1080	0.8243	0.7093	0.7625	86	0.7059	0.8090	0.7539	178	0.9130	0.9844	0.9474	128	0.7957	0.8444	0.8193	0.9652
0.1374	4.0	384	0.0841	0.6990	0.8372	0.7619	86	0.8372	0.8090	0.8229	178	0.9618	0.9844	0.9730	128	0.8424	0.8724	0.8571	0.9735
0.1114	5.0	480	0.0720	0.8427	0.8721	0.8571	86	0.8404	0.8876	0.8634	178	0.9618	0.9844	0.9730	128	0.8799	0.9158	0.8975	0.9781
0.0921	6.0	576	0.0616	0.8172	0.8837	0.8492	86	0.8715	0.8764	0.8739	178	0.9692	0.9844	0.9767	128	0.8905	0.9133	0.9018	0.9806
0.0864	7.0	672	0.0567	0.8298	0.9070	0.8667	86	0.8639	0.9270	0.8943	178	0.9615	0.9766	0.9690	128	0.8867	0.9388	0.9120	0.9811
0.0791	8.0	768	0.0483	0.8681	0.9186	0.8927	86	0.8684	0.9270	0.8967	178	0.9609	0.9609	0.9609	128	0.8973	0.9362	0.9164	0.9843
0.0712	9.0	864	0.0451	0.8495	0.9186	0.8827	86	0.9066	0.9270	0.9167	178	0.9766	0.9766	0.9766	128	0.9156	0.9413	0.9283	0.9852
0.0653	10.0	960	0.0492	0.8526	0.9419	0.8950	86	0.8967	0.9270	0.9116	178	0.9843	0.9766	0.9804	128	0.9138	0.9464	0.9298	0.9841
0.0605	11.0	1056	0.0434	0.8842	0.9767	0.9282	86	0.8939	0.8989	0.8964	178	0.9843	0.9766	0.9804	128	0.9202	0.9413	0.9306	0.9846
0.0592	12.0	1152	0.0370	0.8791	0.9302	0.9040	86	0.8942	0.9494	0.9210	178	0.9843	0.9766	0.9804	128	0.9189	0.9541	0.9362	0.9876
0.0532	13.0	1248	0.0414	0.9231	0.9767	0.9492	86	0.9066	0.9270	0.9167	178	0.9764	0.9688	0.9725	128	0.9325	0.9515	0.9419	0.9879
0.05	14.0	1344	0.0382	0.9425	0.9535	0.9480	86	0.9056	0.9157	0.9106	178	0.9766	0.9766	0.9766	128	0.9367	0.9439	0.9403	0.9870
0.0467	15.0	1440	0.0398	0.9310	0.9419	0.9364	86	0.9253	0.9045	0.9148	178	0.9841	0.9688	0.9764	128	0.9457	0.9337	0.9397	0.9873
0.0461	16.0	1536	0.0421	0.875	0.9767	0.9231	86	0.9274	0.9326	0.9300	178	0.9843	0.9766	0.9804	128	0.9328	0.9566	0.9446	0.9884
0.0407	17.0	1632	0.0353	0.9205	0.9419	0.9310	86	0.9286	0.9494	0.9389	178	0.9843	0.9766	0.9804	128	0.9446	0.9566	0.9506	0.9895
0.0393	18.0	1728	0.0387	0.9231	0.9767	0.9492	86	0.9066	0.9270	0.9167	178	0.9766	0.9766	0.9766	128	0.9327	0.9541	0.9433	0.9876
0.037	19.0	1824	0.0422	0.9222	0.9651	0.9432	86	0.9318	0.9213	0.9266	178	0.9843	0.9766	0.9804	128	0.9466	0.9490	0.9478	0.9881
0.0367	20.0	1920	0.0515	0.8737	0.9651	0.9171	86	0.9037	0.9494	0.9260	178	0.9843	0.9766	0.9804	128	0.9218	0.9617	0.9413	0.9852
0.0333	21.0	2016	0.0392	0.8673	0.9884	0.9239	86	0.9213	0.9213	0.9213	178	0.9843	0.9766	0.9804	128	0.9280	0.9541	0.9409	0.9876
0.0336	22.0	2112	0.0432	0.8925	0.9651	0.9274	86	0.9270	0.9270	0.9270	178	0.9843	0.9766	0.9804	128	0.9372	0.9515	0.9443	0.9870
0.0308	23.0	2208	0.0484	0.9	0.9419	0.9205	86	0.9306	0.9045	0.9174	178	0.9766	0.9766	0.9766	128	0.9386	0.9362	0.9374	0.9862
0.0327	24.0	2304	0.0477	0.8925	0.9651	0.9274	86	0.9257	0.9101	0.9178	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9854
0.0319	25.0	2400	0.0503	0.9	0.9419	0.9205	86	0.9368	0.9157	0.9261	178	0.9766	0.9766	0.9766	128	0.9413	0.9413	0.9413	0.9868
0.0285	26.0	2496	0.0487	0.9121	0.9651	0.9379	86	0.9266	0.9213	0.9239	178	0.9766	0.9766	0.9766	128	0.9394	0.9490	0.9442	0.9865
0.0268	27.0	2592	0.0480	0.9121	0.9651	0.9379	86	0.9218	0.9270	0.9244	178	0.9766	0.9766	0.9766	128	0.9372	0.9515	0.9443	0.9868
0.0288	28.0	2688	0.0457	0.8989	0.9302	0.9143	86	0.9121	0.9326	0.9222	178	0.9766	0.9766	0.9766	128	0.9298	0.9464	0.9381	0.9865
0.0227	29.0	2784	0.0574	0.8817	0.9535	0.9162	86	0.9218	0.9270	0.9244	178	0.9612	0.9688	0.9650	128	0.9252	0.9464	0.9357	0.9852
0.0263	30.0	2880	0.0627	0.8925	0.9651	0.9274	86	0.9240	0.8876	0.9054	178	0.9690	0.9766	0.9728	128	0.9313	0.9337	0.9325	0.9841
0.0258	31.0	2976	0.0492	0.8646	0.9651	0.9121	86	0.9191	0.8933	0.9060	178	0.9766	0.9766	0.9766	128	0.9244	0.9362	0.9303	0.9854
0.0245	32.0	3072	0.0481	0.8804	0.9419	0.9101	86	0.9086	0.8933	0.9008	178	0.9766	0.9766	0.9766	128	0.9241	0.9311	0.9276	0.9860
0.0236	33.0	3168	0.0483	0.8817	0.9535	0.9162	86	0.9266	0.9213	0.9239	178	0.9766	0.9766	0.9766	128	0.9322	0.9464	0.9392	0.9854
0.0209	34.0	3264	0.0546	0.875	0.9767	0.9231	86	0.9148	0.9045	0.9096	178	0.9690	0.9766	0.9728	128	0.9227	0.9439	0.9332	0.9843
0.0211	35.0	3360	0.0549	0.8913	0.9535	0.9213	86	0.9056	0.9157	0.9106	178	0.9690	0.9766	0.9728	128	0.9227	0.9439	0.9332	0.9849
0.0228	36.0	3456	0.0544	0.84	0.9767	0.9032	86	0.9070	0.8764	0.8914	178	0.9766	0.9766	0.9766	128	0.9125	0.9311	0.9217	0.9843
0.0191	37.0	3552	0.0571	0.8876	0.9186	0.9029	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9293	0.9388	0.9340	0.9849
0.0208	38.0	3648	0.0672	0.8723	0.9535	0.9111	86	0.9181	0.8820	0.8997	178	0.9766	0.9766	0.9766	128	0.9262	0.9286	0.9274	0.9838
0.0189	39.0	3744	0.0589	0.8889	0.9302	0.9091	86	0.8994	0.9045	0.9020	178	0.9766	0.9766	0.9766	128	0.9219	0.9337	0.9278	0.9841
0.019	40.0	3840	0.0527	0.8989	0.9302	0.9143	86	0.9	0.9101	0.9050	178	0.9690	0.9766	0.9728	128	0.9221	0.9362	0.9291	0.9862
0.0187	41.0	3936	0.0511	0.8977	0.9186	0.9080	86	0.8983	0.8933	0.8958	178	0.9766	0.9766	0.9766	128	0.9237	0.9260	0.9248	0.9852
0.0175	42.0	4032	0.0567	0.8989	0.9302	0.9143	86	0.9162	0.9213	0.9188	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9862
0.0186	43.0	4128	0.0569	0.8901	0.9419	0.9153	86	0.9310	0.9101	0.9205	178	0.9766	0.9766	0.9766	128	0.9364	0.9388	0.9376	0.9854
0.0166	44.0	4224	0.0696	0.8571	0.9767	0.9130	86	0.9364	0.9101	0.9231	178	0.9766	0.9766	0.9766	128	0.9298	0.9464	0.9381	0.9838
0.018	45.0	4320	0.0561	0.8901	0.9419	0.9153	86	0.9302	0.8989	0.9143	178	0.9766	0.9766	0.9766	128	0.9361	0.9337	0.9349	0.9862
0.0159	46.0	4416	0.0736	0.8384	0.9651	0.8973	86	0.9133	0.8876	0.9003	178	0.9766	0.9766	0.9766	128	0.915	0.9337	0.9242	0.9825
0.0158	47.0	4512	0.0583	0.8710	0.9419	0.9050	86	0.9096	0.9045	0.9070	178	0.9690	0.9766	0.9728	128	0.9198	0.9362	0.9279	0.9835
0.0141	48.0	4608	0.0593	0.8602	0.9302	0.8939	86	0.9205	0.9101	0.9153	178	0.9690	0.9766	0.9728	128	0.9221	0.9362	0.9291	0.9849
0.0151	49.0	4704	0.0573	0.8889	0.9302	0.9091	86	0.9266	0.9213	0.9239	178	0.9690	0.9766	0.9728	128	0.9318	0.9413	0.9365	0.9857
0.0149	50.0	4800	0.0580	0.8804	0.9419	0.9101	86	0.9483	0.9270	0.9375	178	0.9766	0.9766	0.9766	128	0.9416	0.9464	0.9440	0.9870
0.0135	51.0	4896	0.0561	0.8681	0.9186	0.8927	86	0.9379	0.9326	0.9352	178	0.9766	0.9766	0.9766	128	0.9343	0.9439	0.9391	0.9870
0.0142	52.0	4992	0.0679	0.8602	0.9302	0.8939	86	0.8950	0.9101	0.9025	178	0.9690	0.9766	0.9728	128	0.9107	0.9362	0.9233	0.9835
0.0135	53.0	5088	0.0597	0.8791	0.9302	0.9040	86	0.9385	0.9438	0.9412	178	0.9843	0.9766	0.9804	128	0.9395	0.9515	0.9455	0.9857
0.0138	54.0	5184	0.0608	0.8571	0.9070	0.8814	86	0.9116	0.9270	0.9192	178	0.9766	0.9766	0.9766	128	0.92	0.9388	0.9293	0.9841
0.013	55.0	5280	0.0648	0.8696	0.9302	0.8989	86	0.9273	0.8596	0.8921	178	0.9766	0.9766	0.9766	128	0.9299	0.9133	0.9215	0.9830
0.0126	56.0	5376	0.0643	0.8681	0.9186	0.8927	86	0.9195	0.8989	0.9091	178	0.9766	0.9766	0.9766	128	0.9262	0.9286	0.9274	0.9841
0.012	57.0	5472	0.0689	0.8791	0.9302	0.9040	86	0.9218	0.9270	0.9244	178	0.9690	0.9766	0.9728	128	0.9273	0.9439	0.9355	0.9841
0.0115	58.0	5568	0.0722	0.8632	0.9535	0.9061	86	0.9290	0.8820	0.9049	178	0.9690	0.9766	0.9728	128	0.9262	0.9286	0.9274	0.9838
0.0126	59.0	5664	0.0676	0.8495	0.9186	0.8827	86	0.9408	0.8933	0.9164	178	0.9766	0.9766	0.9766	128	0.9308	0.9260	0.9284	0.9846
0.0128	60.0	5760	0.0627	0.8511	0.9302	0.8889	86	0.9148	0.9045	0.9096	178	0.9766	0.9766	0.9766	128	0.9196	0.9337	0.9266	0.9843
0.0122	61.0	5856	0.0623	0.8791	0.9302	0.9040	86	0.9266	0.9213	0.9239	178	0.9766	0.9766	0.9766	128	0.9318	0.9413	0.9365	0.9846
0.0118	62.0	5952	0.0625	0.8602	0.9302	0.8939	86	0.9153	0.9101	0.9127	178	0.9690	0.9766	0.9728	128	0.9198	0.9362	0.9279	0.9849
0.0092	63.0	6048	0.0661	0.8778	0.9186	0.8977	86	0.9274	0.9326	0.9300	178	0.9690	0.9766	0.9728	128	0.9296	0.9439	0.9367	0.9841
0.0091	64.0	6144	0.0651	0.8587	0.9186	0.8876	86	0.9205	0.9101	0.9153	178	0.9766	0.9766	0.9766	128	0.9242	0.9337	0.9289	0.9849
0.0107	65.0	6240	0.0635	0.8681	0.9186	0.8927	86	0.9326	0.9326	0.9326	178	0.9690	0.9766	0.9728	128	0.9296	0.9439	0.9367	0.9854
0.009	66.0	6336	0.0662	0.8602	0.9302	0.8939	86	0.9266	0.9213	0.9239	178	0.9690	0.9766	0.9728	128	0.9248	0.9413	0.9330	0.9849
0.0109	67.0	6432	0.0711	0.8421	0.9302	0.8840	86	0.9191	0.8933	0.9060	178	0.9690	0.9766	0.9728	128	0.9169	0.9286	0.9227	0.9843
0.0093	68.0	6528	0.0670	0.8681	0.9186	0.8927	86	0.9368	0.9157	0.9261	178	0.9690	0.9766	0.9728	128	0.9315	0.9362	0.9338	0.9852
0.0104	69.0	6624	0.0662	0.8696	0.9302	0.8989	86	0.9153	0.9101	0.9127	178	0.9690	0.9766	0.9728	128	0.9221	0.9362	0.9291	0.9852
0.0098	70.0	6720	0.0689	0.8710	0.9419	0.9050	86	0.92	0.9045	0.9122	178	0.9766	0.9766	0.9766	128	0.9268	0.9362	0.9315	0.9854
0.0086	71.0	6816	0.0655	0.8681	0.9186	0.8927	86	0.9278	0.9382	0.9330	178	0.9690	0.9766	0.9728	128	0.9275	0.9464	0.9369	0.9857
0.0101	72.0	6912	0.0689	0.8681	0.9186	0.8927	86	0.9172	0.8708	0.8934	178	0.9690	0.9766	0.9728	128	0.9229	0.9158	0.9193	0.9827
0.011	73.0	7008	0.0632	0.8587	0.9186	0.8876	86	0.92	0.9045	0.9122	178	0.9690	0.9766	0.9728	128	0.9217	0.9311	0.9264	0.9854
0.0089	74.0	7104	0.0701	0.8710	0.9419	0.9050	86	0.9191	0.8933	0.9060	178	0.9690	0.9766	0.9728	128	0.9241	0.9311	0.9276	0.9841
0.0084	75.0	7200	0.0708	0.8511	0.9302	0.8889	86	0.9310	0.9101	0.9205	178	0.9690	0.9766	0.9728	128	0.9244	0.9362	0.9303	0.9841
0.0092	76.0	7296	0.0645	0.8587	0.9186	0.8876	86	0.9318	0.9213	0.9266	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9854
0.0079	77.0	7392	0.0684	0.8696	0.9302	0.8989	86	0.9422	0.9157	0.9288	178	0.9766	0.9766	0.9766	128	0.9364	0.9388	0.9376	0.9857
0.0085	78.0	7488	0.0658	0.8696	0.9302	0.8989	86	0.9318	0.9213	0.9266	178	0.9690	0.9766	0.9728	128	0.9295	0.9413	0.9354	0.9849
0.0083	79.0	7584	0.0674	0.8602	0.9302	0.8939	86	0.9318	0.9213	0.9266	178	0.9766	0.9766	0.9766	128	0.9295	0.9413	0.9354	0.9854
0.0079	80.0	7680	0.0735	0.8696	0.9302	0.8989	86	0.9209	0.9157	0.9183	178	0.9690	0.9766	0.9728	128	0.9246	0.9388	0.9316	0.9852
0.0077	81.0	7776	0.0693	0.8696	0.9302	0.8989	86	0.9322	0.9270	0.9296	178	0.9690	0.9766	0.9728	128	0.9296	0.9439	0.9367	0.9860
0.009	82.0	7872	0.0703	0.8696	0.9302	0.8989	86	0.9318	0.9213	0.9266	178	0.9690	0.9766	0.9728	128	0.9295	0.9413	0.9354	0.9854
0.0093	83.0	7968	0.0659	0.8778	0.9186	0.8977	86	0.9148	0.9045	0.9096	178	0.9690	0.9766	0.9728	128	0.9241	0.9311	0.9276	0.9857
0.0069	84.0	8064	0.0694	0.8696	0.9302	0.8989	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9268	0.9362	0.9315	0.9857
0.0082	85.0	8160	0.0692	0.8696	0.9302	0.8989	86	0.9209	0.9157	0.9183	178	0.9690	0.9766	0.9728	128	0.9246	0.9388	0.9316	0.9857
0.0074	86.0	8256	0.0710	0.8602	0.9302	0.8939	86	0.9314	0.9157	0.9235	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0075	87.0	8352	0.0727	0.8791	0.9302	0.9040	86	0.9371	0.9213	0.9292	178	0.9690	0.9766	0.9728	128	0.9342	0.9413	0.9377	0.9862
0.0072	88.0	8448	0.0723	0.8602	0.9302	0.8939	86	0.9261	0.9157	0.9209	178	0.9690	0.9766	0.9728	128	0.9246	0.9388	0.9316	0.9854
0.0059	89.0	8544	0.0735	0.8602	0.9302	0.8939	86	0.9261	0.9157	0.9209	178	0.9690	0.9766	0.9728	128	0.9246	0.9388	0.9316	0.9854
0.007	90.0	8640	0.0725	0.8889	0.9302	0.9091	86	0.9266	0.9213	0.9239	178	0.9690	0.9766	0.9728	128	0.9318	0.9413	0.9365	0.9862
0.0074	91.0	8736	0.0722	0.8710	0.9419	0.9050	86	0.9425	0.9213	0.9318	178	0.9690	0.9766	0.9728	128	0.9343	0.9439	0.9391	0.9862
0.0073	92.0	8832	0.0745	0.8617	0.9419	0.9000	86	0.92	0.9045	0.9122	178	0.9690	0.9766	0.9728	128	0.9221	0.9362	0.9291	0.9852
0.0071	93.0	8928	0.0752	0.8617	0.9419	0.9000	86	0.92	0.9045	0.9122	178	0.9690	0.9766	0.9728	128	0.9221	0.9362	0.9291	0.9854
0.0076	94.0	9024	0.0738	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9854
0.0072	95.0	9120	0.0728	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0072	96.0	9216	0.0732	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0072	97.0	9312	0.0744	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0079	98.0	9408	0.0741	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0059	99.0	9504	0.0738	0.8710	0.9419	0.9050	86	0.9257	0.9101	0.9178	178	0.9690	0.9766	0.9728	128	0.9270	0.9388	0.9328	0.9857
0.0068	100.0	9600	0.0737	0.8696	0.9302	0.8989	86	0.9205	0.9101	0.9153	178	0.9690	0.9766	0.9728	128	0.9244	0.9362	0.9303	0.9854

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl20-3

nerui-pt-pl20-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl20-3