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apwic/
nerui-pt-pl50-2

Name: nerui-pt-pl50-2
Availability: InStock

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl50-2

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: 0.0795
Location Precision: 0.8911
Location Recall: 0.9677
Location F1: 0.9278
Location Number: 93
Organization Precision: 0.9202
Organization Recall: 0.9036
Organization F1: 0.9119
Organization Number: 166
Person Precision: 0.9583
Person Recall: 0.9718
Person F1: 0.9650
Person Number: 142
Overall Precision: 0.9265
Overall Recall: 0.9426
Overall F1: 0.9345
Overall Accuracy: 0.9857

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
0.8529	1.0	96	0.4086	0.0	0.0	0.0	93	0.2301	0.1566	0.1864	166	0.3086	0.1761	0.2242	142	0.2589	0.1272	0.1706	0.8527
0.3531	2.0	192	0.1972	0.3952	0.5269	0.4516	93	0.5864	0.5723	0.5793	166	0.6742	0.8451	0.7500	142	0.5690	0.6584	0.6104	0.9432
0.1852	3.0	288	0.0960	0.8261	0.8172	0.8216	93	0.7474	0.8735	0.8056	166	0.9592	0.9930	0.9758	142	0.8360	0.9027	0.8681	0.9737
0.126	4.0	384	0.0836	0.7736	0.8817	0.8241	93	0.7824	0.9096	0.8412	166	0.9586	0.9789	0.9686	142	0.8378	0.9277	0.8805	0.9750
0.1027	5.0	480	0.0584	0.8763	0.9140	0.8947	93	0.8795	0.8795	0.8795	166	0.9583	0.9718	0.9650	142	0.9066	0.9202	0.9134	0.9830
0.0925	6.0	576	0.0524	0.7876	0.9570	0.8641	93	0.9221	0.8554	0.8875	166	0.9583	0.9718	0.9650	142	0.8978	0.9202	0.9089	0.9824
0.0803	7.0	672	0.0523	0.8542	0.8817	0.8677	93	0.8495	0.9518	0.8977	166	0.9586	0.9789	0.9686	142	0.8876	0.9451	0.9155	0.9833
0.0733	8.0	768	0.0525	0.8333	0.9677	0.8955	93	0.9241	0.8795	0.9012	166	0.9650	0.9718	0.9684	142	0.9144	0.9327	0.9235	0.9824
0.0684	9.0	864	0.0438	0.8812	0.9570	0.9175	93	0.9030	0.8976	0.9003	166	0.9583	0.9718	0.9650	142	0.9171	0.9377	0.9273	0.9852
0.0627	10.0	960	0.0445	0.8654	0.9677	0.9137	93	0.9136	0.8916	0.9024	166	0.9586	0.9789	0.9686	142	0.9173	0.9401	0.9286	0.9849
0.059	11.0	1056	0.0453	0.8641	0.9570	0.9082	93	0.9	0.9217	0.9107	166	0.9716	0.9648	0.9682	142	0.9155	0.9451	0.9301	0.9855
0.0535	12.0	1152	0.0456	0.88	0.9462	0.9119	93	0.9091	0.9036	0.9063	166	0.9858	0.9789	0.9823	142	0.9286	0.9401	0.9343	0.9852
0.05	13.0	1248	0.0437	0.8889	0.9462	0.9167	93	0.8889	0.9157	0.9021	166	0.9714	0.9577	0.9645	142	0.9171	0.9377	0.9273	0.9860
0.0481	14.0	1344	0.0442	0.9020	0.9892	0.9436	93	0.9136	0.8916	0.9024	166	0.9720	0.9789	0.9754	142	0.9312	0.9451	0.9381	0.9866
0.0456	15.0	1440	0.0437	0.8812	0.9570	0.9175	93	0.9212	0.9157	0.9184	166	0.9583	0.9718	0.9650	142	0.9244	0.9451	0.9346	0.9866
0.0435	16.0	1536	0.0495	0.8824	0.9677	0.9231	93	0.9187	0.8855	0.9018	166	0.9583	0.9718	0.9650	142	0.9236	0.9352	0.9294	0.9855
0.0412	17.0	1632	0.0444	0.9167	0.9462	0.9312	93	0.9048	0.9157	0.9102	166	0.9650	0.9718	0.9684	142	0.9287	0.9426	0.9356	0.9868
0.0369	18.0	1728	0.0475	0.8667	0.9785	0.9192	93	0.9427	0.8916	0.9164	166	0.9720	0.9789	0.9754	142	0.9333	0.9426	0.9380	0.9863
0.04	19.0	1824	0.0397	0.9175	0.9570	0.9368	93	0.8988	0.9096	0.9042	166	0.9718	0.9718	0.9718	142	0.9287	0.9426	0.9356	0.9874
0.0355	20.0	1920	0.0478	0.9091	0.9677	0.9375	93	0.8947	0.9217	0.9080	166	0.9716	0.9648	0.9682	142	0.9246	0.9476	0.9360	0.9868
0.0369	21.0	2016	0.0561	0.8738	0.9677	0.9184	93	0.9355	0.8735	0.9034	166	0.9857	0.9718	0.9787	142	0.9372	0.9302	0.9337	0.9855
0.0338	22.0	2112	0.0521	0.8713	0.9462	0.9072	93	0.8922	0.8976	0.8949	166	0.9379	0.9577	0.9477	142	0.9031	0.9302	0.9165	0.9841
0.033	23.0	2208	0.0519	0.8990	0.9570	0.9271	93	0.8922	0.8976	0.8949	166	0.9514	0.9648	0.9580	142	0.9146	0.9352	0.9248	0.9852
0.032	24.0	2304	0.0583	0.8505	0.9785	0.91	93	0.8957	0.8795	0.8875	166	0.9650	0.9718	0.9684	142	0.9080	0.9352	0.9214	0.9835
0.0303	25.0	2400	0.0572	0.8911	0.9677	0.9278	93	0.9036	0.9036	0.9036	166	0.9650	0.9718	0.9684	142	0.9220	0.9426	0.9322	0.9855
0.0286	26.0	2496	0.0588	0.8824	0.9677	0.9231	93	0.9136	0.8916	0.9024	166	0.9580	0.9648	0.9614	142	0.9214	0.9352	0.9282	0.9846
0.0288	27.0	2592	0.0550	0.9	0.9677	0.9326	93	0.9136	0.8916	0.9024	166	0.9580	0.9648	0.9614	142	0.9259	0.9352	0.9305	0.9849
0.0267	28.0	2688	0.0563	0.8824	0.9677	0.9231	93	0.9096	0.9096	0.9096	166	0.9653	0.9789	0.9720	142	0.9223	0.9476	0.9348	0.9855
0.0257	29.0	2784	0.0529	0.9	0.9677	0.9326	93	0.9321	0.9096	0.9207	166	0.9514	0.9648	0.9580	142	0.9310	0.9426	0.9368	0.9860
0.0252	30.0	2880	0.0557	0.9175	0.9570	0.9368	93	0.9118	0.9337	0.9226	166	0.9718	0.9718	0.9718	142	0.9340	0.9526	0.9432	0.9863
0.0258	31.0	2976	0.0524	0.8824	0.9677	0.9231	93	0.9136	0.8916	0.9024	166	0.9653	0.9789	0.9720	142	0.9240	0.9401	0.9320	0.9868
0.0221	32.0	3072	0.0587	0.8725	0.9570	0.9128	93	0.9198	0.8976	0.9085	166	0.9580	0.9648	0.9614	142	0.9214	0.9352	0.9282	0.9857
0.023	33.0	3168	0.0571	0.8738	0.9677	0.9184	93	0.9146	0.9036	0.9091	166	0.9580	0.9648	0.9614	142	0.9195	0.9401	0.9297	0.9852
0.0236	34.0	3264	0.0558	0.9278	0.9677	0.9474	93	0.9222	0.9277	0.9249	166	0.9786	0.9648	0.9716	142	0.9431	0.9501	0.9466	0.9871
0.0195	35.0	3360	0.0618	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9580	0.9648	0.9614	142	0.9263	0.9401	0.9332	0.9857
0.0238	36.0	3456	0.0569	0.8835	0.9785	0.9286	93	0.9304	0.8855	0.9074	166	0.9510	0.9577	0.9544	142	0.9257	0.9327	0.9292	0.9863
0.0219	37.0	3552	0.0601	0.8824	0.9677	0.9231	93	0.9096	0.9096	0.9096	166	0.9650	0.9718	0.9684	142	0.9221	0.9451	0.9335	0.9860
0.0188	38.0	3648	0.0608	0.9091	0.9677	0.9375	93	0.9212	0.9157	0.9184	166	0.9580	0.9648	0.9614	142	0.9312	0.9451	0.9381	0.9866
0.0193	39.0	3744	0.0613	0.8824	0.9677	0.9231	93	0.9308	0.8916	0.9108	166	0.9650	0.9718	0.9684	142	0.9307	0.9377	0.9342	0.9852
0.0192	40.0	3840	0.0613	0.89	0.9570	0.9223	93	0.9202	0.9036	0.9119	166	0.9650	0.9718	0.9684	142	0.9286	0.9401	0.9343	0.9863
0.0166	41.0	3936	0.0598	0.9091	0.9677	0.9375	93	0.9053	0.9217	0.9134	166	0.9580	0.9648	0.9614	142	0.9246	0.9476	0.9360	0.9863
0.0162	42.0	4032	0.0590	0.8824	0.9677	0.9231	93	0.9207	0.9096	0.9152	166	0.9653	0.9789	0.9720	142	0.9268	0.9476	0.9371	0.9868
0.0163	43.0	4128	0.0629	0.8911	0.9677	0.9278	93	0.9268	0.9157	0.9212	166	0.9580	0.9648	0.9614	142	0.9289	0.9451	0.9370	0.9852
0.0168	44.0	4224	0.0631	0.9082	0.9570	0.9319	93	0.9102	0.9157	0.9129	166	0.9653	0.9789	0.9720	142	0.9291	0.9476	0.9383	0.9857
0.0152	45.0	4320	0.0669	0.8824	0.9677	0.9231	93	0.9255	0.8976	0.9113	166	0.9650	0.9718	0.9684	142	0.9286	0.9401	0.9343	0.9860
0.0138	46.0	4416	0.0695	0.8738	0.9677	0.9184	93	0.9182	0.8795	0.8985	166	0.9583	0.9718	0.9650	142	0.9212	0.9327	0.9269	0.9844
0.0142	47.0	4512	0.0683	0.9175	0.9570	0.9368	93	0.8876	0.9036	0.8955	166	0.9583	0.9718	0.9650	142	0.9195	0.9401	0.9297	0.9852
0.0156	48.0	4608	0.0628	0.8738	0.9677	0.9184	93	0.9255	0.8976	0.9113	166	0.9718	0.9718	0.9718	142	0.9286	0.9401	0.9343	0.9866
0.0139	49.0	4704	0.0686	0.9263	0.9462	0.9362	93	0.8947	0.9217	0.9080	166	0.9580	0.9648	0.9614	142	0.9242	0.9426	0.9333	0.9855
0.016	50.0	4800	0.0620	0.9167	0.9462	0.9312	93	0.9042	0.9096	0.9069	166	0.9718	0.9718	0.9718	142	0.9309	0.9401	0.9355	0.9863
0.0145	51.0	4896	0.0674	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9650	0.9718	0.9684	142	0.9287	0.9426	0.9356	0.9863
0.0126	52.0	4992	0.0710	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9580	0.9648	0.9614	142	0.9263	0.9401	0.9332	0.9852
0.0132	53.0	5088	0.0719	0.9082	0.9570	0.9319	93	0.9217	0.9217	0.9217	166	0.9583	0.9718	0.9650	142	0.9314	0.9476	0.9394	0.9841
0.014	54.0	5184	0.0706	0.8911	0.9677	0.9278	93	0.9024	0.8916	0.8970	166	0.9648	0.9648	0.9648	142	0.9214	0.9352	0.9282	0.9852
0.014	55.0	5280	0.0654	0.8713	0.9462	0.9072	93	0.9313	0.8976	0.9141	166	0.9718	0.9718	0.9718	142	0.9305	0.9352	0.9328	0.9866
0.0119	56.0	5376	0.0683	0.8812	0.9570	0.9175	93	0.9212	0.9157	0.9184	166	0.9650	0.9718	0.9684	142	0.9267	0.9451	0.9358	0.9863
0.0117	57.0	5472	0.0686	0.8812	0.9570	0.9175	93	0.9136	0.8916	0.9024	166	0.9650	0.9718	0.9684	142	0.9236	0.9352	0.9294	0.9860
0.0113	58.0	5568	0.0691	0.8654	0.9677	0.9137	93	0.9182	0.8795	0.8985	166	0.9452	0.9718	0.9583	142	0.9144	0.9327	0.9235	0.9849
0.0126	59.0	5664	0.0714	0.8812	0.9570	0.9175	93	0.9264	0.9096	0.9179	166	0.9517	0.9718	0.9617	142	0.9242	0.9426	0.9333	0.9863
0.0106	60.0	5760	0.0744	0.8824	0.9677	0.9231	93	0.9136	0.8916	0.9024	166	0.9716	0.9648	0.9682	142	0.9259	0.9352	0.9305	0.9849
0.0116	61.0	5856	0.0741	0.8812	0.9570	0.9175	93	0.9141	0.8976	0.9058	166	0.9648	0.9648	0.9648	142	0.9236	0.9352	0.9294	0.9849
0.0122	62.0	5952	0.0684	0.8725	0.9570	0.9128	93	0.9207	0.9096	0.9152	166	0.9648	0.9648	0.9648	142	0.9240	0.9401	0.9320	0.9860
0.0103	63.0	6048	0.0751	0.8824	0.9677	0.9231	93	0.9207	0.9096	0.9152	166	0.9514	0.9648	0.9580	142	0.9220	0.9426	0.9322	0.9846
0.0099	64.0	6144	0.0667	0.9184	0.9677	0.9424	93	0.9268	0.9157	0.9212	166	0.9580	0.9648	0.9614	142	0.9358	0.9451	0.9404	0.9871
0.0089	65.0	6240	0.0764	0.8812	0.9570	0.9175	93	0.9130	0.8855	0.8991	166	0.9452	0.9718	0.9583	142	0.9167	0.9327	0.9246	0.9841
0.0098	66.0	6336	0.0752	0.8824	0.9677	0.9231	93	0.9202	0.9036	0.9119	166	0.9650	0.9718	0.9684	142	0.9265	0.9426	0.9345	0.9857
0.0097	67.0	6432	0.0784	0.8738	0.9677	0.9184	93	0.9198	0.8976	0.9085	166	0.9650	0.9718	0.9684	142	0.9240	0.9401	0.9320	0.9846
0.0097	68.0	6528	0.0771	0.9	0.9677	0.9326	93	0.9217	0.9217	0.9217	166	0.9650	0.9718	0.9684	142	0.9315	0.9501	0.9407	0.9852
0.0094	69.0	6624	0.0737	0.8990	0.9570	0.9271	93	0.9157	0.9157	0.9157	166	0.9650	0.9718	0.9684	142	0.9289	0.9451	0.9370	0.9857
0.0083	70.0	6720	0.0739	0.8824	0.9677	0.9231	93	0.9317	0.9036	0.9174	166	0.9650	0.9718	0.9684	142	0.9310	0.9426	0.9368	0.9852
0.0087	71.0	6816	0.0715	0.9082	0.9570	0.9319	93	0.9042	0.9096	0.9069	166	0.9650	0.9718	0.9684	142	0.9265	0.9426	0.9345	0.9866
0.0084	72.0	6912	0.0727	0.8911	0.9677	0.9278	93	0.9317	0.9036	0.9174	166	0.9650	0.9718	0.9684	142	0.9333	0.9426	0.9380	0.9871
0.0076	73.0	7008	0.0726	0.8824	0.9677	0.9231	93	0.9317	0.9036	0.9174	166	0.9718	0.9718	0.9718	142	0.9333	0.9426	0.9380	0.9868
0.0087	74.0	7104	0.0800	0.8824	0.9677	0.9231	93	0.9259	0.9036	0.9146	166	0.9650	0.9718	0.9684	142	0.9287	0.9426	0.9356	0.9841
0.0079	75.0	7200	0.0772	0.9	0.9677	0.9326	93	0.9321	0.9096	0.9207	166	0.9650	0.9718	0.9684	142	0.9358	0.9451	0.9404	0.9857
0.007	76.0	7296	0.0782	0.9	0.9677	0.9326	93	0.9207	0.9096	0.9152	166	0.9650	0.9718	0.9684	142	0.9312	0.9451	0.9381	0.9863
0.0089	77.0	7392	0.0773	0.8911	0.9677	0.9278	93	0.9207	0.9096	0.9152	166	0.9718	0.9718	0.9718	142	0.9312	0.9451	0.9381	0.9855
0.008	78.0	7488	0.0786	0.8824	0.9677	0.9231	93	0.9080	0.8916	0.8997	166	0.9718	0.9718	0.9718	142	0.9238	0.9377	0.9307	0.9838
0.0082	79.0	7584	0.0727	0.8911	0.9677	0.9278	93	0.9198	0.8976	0.9085	166	0.9517	0.9718	0.9617	142	0.9240	0.9401	0.9320	0.9849
0.0086	80.0	7680	0.0743	0.8911	0.9677	0.9278	93	0.9085	0.8976	0.9030	166	0.9650	0.9718	0.9684	142	0.9240	0.9401	0.9320	0.9846
0.0085	81.0	7776	0.0710	0.8911	0.9677	0.9278	93	0.9091	0.9036	0.9063	166	0.9718	0.9718	0.9718	142	0.9265	0.9426	0.9345	0.9860
0.0072	82.0	7872	0.0770	0.9	0.9677	0.9326	93	0.9141	0.8976	0.9058	166	0.9583	0.9718	0.9650	142	0.9263	0.9401	0.9332	0.9849
0.0067	83.0	7968	0.0810	0.8824	0.9677	0.9231	93	0.9136	0.8916	0.9024	166	0.9583	0.9718	0.9650	142	0.9216	0.9377	0.9295	0.9841
0.0067	84.0	8064	0.0766	0.8990	0.9570	0.9271	93	0.9096	0.9096	0.9096	166	0.9583	0.9718	0.9650	142	0.9242	0.9426	0.9333	0.9855
0.0058	85.0	8160	0.0795	0.8911	0.9677	0.9278	93	0.9264	0.9096	0.9179	166	0.9718	0.9718	0.9718	142	0.9335	0.9451	0.9393	0.9863
0.0079	86.0	8256	0.0777	0.8824	0.9677	0.9231	93	0.9030	0.8976	0.9003	166	0.9650	0.9718	0.9684	142	0.9195	0.9401	0.9297	0.9846
0.0058	87.0	8352	0.0786	0.8824	0.9677	0.9231	93	0.9141	0.8976	0.9058	166	0.9517	0.9718	0.9617	142	0.9195	0.9401	0.9297	0.9846
0.0071	88.0	8448	0.0757	0.9	0.9677	0.9326	93	0.9091	0.9036	0.9063	166	0.9583	0.9718	0.9650	142	0.9242	0.9426	0.9333	0.9855
0.0068	89.0	8544	0.0806	0.8911	0.9677	0.9278	93	0.9091	0.9036	0.9063	166	0.9650	0.9718	0.9684	142	0.9242	0.9426	0.9333	0.9846
0.0061	90.0	8640	0.0750	0.8911	0.9677	0.9278	93	0.9198	0.8976	0.9085	166	0.9718	0.9718	0.9718	142	0.9309	0.9401	0.9355	0.9855
0.0056	91.0	8736	0.0774	0.8911	0.9677	0.9278	93	0.9141	0.8976	0.9058	166	0.9583	0.9718	0.9650	142	0.9240	0.9401	0.9320	0.9855
0.0054	92.0	8832	0.0808	0.8824	0.9677	0.9231	93	0.9141	0.8976	0.9058	166	0.9517	0.9718	0.9617	142	0.9195	0.9401	0.9297	0.9849
0.0058	93.0	8928	0.0783	0.8911	0.9677	0.9278	93	0.9141	0.8976	0.9058	166	0.9517	0.9718	0.9617	142	0.9218	0.9401	0.9309	0.9852
0.0064	94.0	9024	0.0802	0.8911	0.9677	0.9278	93	0.9080	0.8916	0.8997	166	0.9583	0.9718	0.9650	142	0.9216	0.9377	0.9295	0.9852
0.0069	95.0	9120	0.0824	0.8911	0.9677	0.9278	93	0.9141	0.8976	0.9058	166	0.9650	0.9718	0.9684	142	0.9263	0.9401	0.9332	0.9855
0.0059	96.0	9216	0.0814	0.8911	0.9677	0.9278	93	0.9080	0.8916	0.8997	166	0.9517	0.9718	0.9617	142	0.9193	0.9377	0.9284	0.9852
0.0058	97.0	9312	0.0790	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9583	0.9718	0.9650	142	0.9265	0.9426	0.9345	0.9860
0.0069	98.0	9408	0.0797	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9583	0.9718	0.9650	142	0.9265	0.9426	0.9345	0.9857
0.0061	99.0	9504	0.0795	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9583	0.9718	0.9650	142	0.9265	0.9426	0.9345	0.9857
0.0067	100.0	9600	0.0795	0.8911	0.9677	0.9278	93	0.9202	0.9036	0.9119	166	0.9583	0.9718	0.9650	142	0.9265	0.9426	0.9345	0.9857

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

apwic/nerui-pt-pl50-2

nerui-pt-pl50-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl50-2