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apwic/
nerui-pt-pl10-0

Name: nerui-pt-pl10-0
Availability: InStock

tensorboard

generated_from_trainer

indolem/indobert-base-uncased

finetune

mit

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nerui-pt-pl10-0

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: 0.0714
Location Precision: 0.91
Location Recall: 0.9681
Location F1: 0.9381
Location Number: 94
Organization Precision: 0.9255
Organization Recall: 0.8922
Organization F1: 0.9085
Organization Number: 167
Person Precision: 0.9781
Person Recall: 0.9781
Person F1: 0.9781
Person Number: 137
Overall Precision: 0.9397
Overall Recall: 0.9397
Overall F1: 0.9397
Overall Accuracy: 0.9876

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
0.847	1.0	96	0.4058	0.1667	0.0106	0.02	94	0.2152	0.3054	0.2525	167	0.2315	0.3650	0.2833	137	0.2222	0.2563	0.2380	0.8622
0.3603	2.0	192	0.2120	0.4038	0.4468	0.4242	94	0.5450	0.6886	0.6085	167	0.8038	0.9270	0.8610	137	0.6004	0.7136	0.6521	0.9398
0.1884	3.0	288	0.0928	0.7938	0.8191	0.8063	94	0.7765	0.8323	0.8035	167	0.9571	0.9781	0.9675	137	0.8413	0.8794	0.8600	0.9707
0.1293	4.0	384	0.0661	0.8367	0.8723	0.8542	94	0.8448	0.8802	0.8622	167	0.9853	0.9781	0.9817	137	0.8897	0.9121	0.9007	0.9796
0.1088	5.0	480	0.0556	0.8660	0.8936	0.8796	94	0.8538	0.8743	0.8639	167	0.9926	0.9854	0.9890	137	0.9035	0.9171	0.9102	0.9820
0.0895	6.0	576	0.0537	0.7965	0.9574	0.8696	94	0.8772	0.8982	0.8876	167	1.0	0.9854	0.9926	137	0.8950	0.9422	0.9180	0.9831
0.0849	7.0	672	0.0548	0.8037	0.9149	0.8557	94	0.8882	0.9042	0.8961	167	0.9925	0.9708	0.9815	137	0.9002	0.9296	0.9147	0.9812
0.0802	8.0	768	0.0499	0.8165	0.9468	0.8768	94	0.8563	0.8563	0.8563	167	1.0	0.9854	0.9926	137	0.8929	0.9221	0.9073	0.9829
0.0711	9.0	864	0.0462	0.8396	0.9468	0.89	94	0.8982	0.8982	0.8982	167	1.0	0.9854	0.9926	137	0.9167	0.9397	0.9280	0.9848
0.0656	10.0	960	0.0529	0.8182	0.9574	0.8824	94	0.8728	0.9042	0.8882	167	0.9781	0.9781	0.9781	137	0.8929	0.9422	0.9169	0.9826
0.0637	11.0	1056	0.0466	0.8257	0.9574	0.8867	94	0.8957	0.8743	0.8848	167	0.9926	0.9781	0.9853	137	0.9091	0.9296	0.9193	0.9843
0.0544	12.0	1152	0.0485	0.8241	0.9468	0.8812	94	0.9193	0.8862	0.9024	167	0.9853	0.9781	0.9817	137	0.9160	0.9322	0.9240	0.9845
0.0521	13.0	1248	0.0439	0.8476	0.9468	0.8945	94	0.9325	0.9102	0.9212	167	0.9926	0.9781	0.9853	137	0.9305	0.9422	0.9363	0.9870
0.0535	14.0	1344	0.0429	0.8529	0.9255	0.8878	94	0.8902	0.9222	0.9059	167	0.9926	0.9854	0.9890	137	0.9148	0.9447	0.9295	0.9856
0.0502	15.0	1440	0.0447	0.8889	0.9362	0.9119	94	0.9264	0.9042	0.9152	167	0.9853	0.9781	0.9817	137	0.9372	0.9372	0.9372	0.9876
0.0473	16.0	1536	0.0394	0.8980	0.9362	0.9167	94	0.9157	0.9102	0.9129	167	1.0	0.9854	0.9926	137	0.9398	0.9422	0.9410	0.9881
0.0459	17.0	1632	0.0450	0.8411	0.9574	0.8955	94	0.9207	0.9042	0.9124	167	1.0	0.9854	0.9926	137	0.9261	0.9447	0.9353	0.9848
0.046	18.0	1728	0.0511	0.8571	0.9574	0.9045	94	0.9434	0.8982	0.9202	167	0.9781	0.9781	0.9781	137	0.9327	0.9397	0.9362	0.9862
0.042	19.0	1824	0.0516	0.8654	0.9574	0.9091	94	0.9136	0.8862	0.8997	167	0.9781	0.9781	0.9781	137	0.9231	0.9347	0.9288	0.9851
0.0423	20.0	1920	0.0431	0.8725	0.9468	0.9082	94	0.9321	0.9042	0.9179	167	0.9926	0.9854	0.9890	137	0.9375	0.9422	0.9398	0.9867
0.0406	21.0	2016	0.0439	0.8846	0.9787	0.9293	94	0.9212	0.9102	0.9157	167	1.0	0.9854	0.9926	137	0.9381	0.9523	0.9451	0.9867
0.0362	22.0	2112	0.0427	0.8725	0.9468	0.9082	94	0.9030	0.8922	0.8976	167	1.0	0.9854	0.9926	137	0.9279	0.9372	0.9325	0.9867
0.0356	23.0	2208	0.0527	0.8824	0.9574	0.9184	94	0.9313	0.8922	0.9113	167	0.9781	0.9781	0.9781	137	0.9348	0.9372	0.9360	0.9878
0.0334	24.0	2304	0.0499	0.9	0.9574	0.9278	94	0.9563	0.9162	0.9358	167	0.9853	0.9781	0.9817	137	0.9520	0.9472	0.9496	0.9884
0.0357	25.0	2400	0.0487	0.89	0.9468	0.9175	94	0.9030	0.8922	0.8976	167	0.9853	0.9781	0.9817	137	0.9277	0.9347	0.9312	0.9851
0.035	26.0	2496	0.0482	0.8889	0.9362	0.9119	94	0.9096	0.9042	0.9069	167	0.9640	0.9781	0.9710	137	0.9233	0.9372	0.9302	0.9859
0.0322	27.0	2592	0.0473	0.8980	0.9362	0.9167	94	0.9212	0.9102	0.9157	167	0.9853	0.9781	0.9817	137	0.9373	0.9397	0.9385	0.9870
0.0313	28.0	2688	0.0500	0.89	0.9468	0.9175	94	0.9379	0.9042	0.9207	167	0.9781	0.9781	0.9781	137	0.9397	0.9397	0.9397	0.9873
0.029	29.0	2784	0.0520	0.8922	0.9681	0.9286	94	0.9560	0.9102	0.9325	167	0.9926	0.9854	0.9890	137	0.9521	0.9497	0.9509	0.9878
0.0282	30.0	2880	0.0583	0.8812	0.9468	0.9128	94	0.8947	0.9162	0.9053	167	0.9854	0.9854	0.9854	137	0.9218	0.9472	0.9343	0.9848
0.0298	31.0	2976	0.0511	0.8990	0.9468	0.9223	94	0.9367	0.8862	0.9108	167	0.9926	0.9854	0.9890	137	0.9466	0.9347	0.9406	0.9870
0.0288	32.0	3072	0.0567	0.8762	0.9787	0.9246	94	0.9375	0.8982	0.9174	167	0.9781	0.9781	0.9781	137	0.9353	0.9447	0.94	0.9862
0.0266	33.0	3168	0.0538	0.8980	0.9362	0.9167	94	0.9317	0.8982	0.9146	167	0.9781	0.9781	0.9781	137	0.9394	0.9347	0.9370	0.9876
0.0257	34.0	3264	0.0523	0.9271	0.9468	0.9368	94	0.9012	0.9281	0.9145	167	0.9853	0.9781	0.9817	137	0.9356	0.9497	0.9426	0.9873
0.026	35.0	3360	0.0527	0.8911	0.9574	0.9231	94	0.9068	0.8743	0.8902	167	0.9853	0.9781	0.9817	137	0.9296	0.9296	0.9296	0.9856
0.0256	36.0	3456	0.0504	0.89	0.9468	0.9175	94	0.9329	0.9162	0.9245	167	1.0	0.9854	0.9926	137	0.9449	0.9472	0.9460	0.9881
0.023	37.0	3552	0.0554	0.89	0.9468	0.9175	94	0.9277	0.9222	0.9249	167	0.9710	0.9781	0.9745	137	0.9332	0.9472	0.9401	0.9870
0.0225	38.0	3648	0.0492	0.9091	0.9574	0.9326	94	0.9394	0.9281	0.9337	167	0.9854	0.9854	0.9854	137	0.9476	0.9548	0.9512	0.9884
0.0229	39.0	3744	0.0517	0.8738	0.9574	0.9137	94	0.9255	0.8922	0.9085	167	0.9926	0.9781	0.9853	137	0.9348	0.9372	0.9360	0.9873
0.0207	40.0	3840	0.0522	0.8812	0.9468	0.9128	94	0.9162	0.9162	0.9162	167	0.9781	0.9781	0.9781	137	0.9284	0.9447	0.9365	0.9867
0.0235	41.0	3936	0.0560	0.875	0.9681	0.9192	94	0.9042	0.9042	0.9042	167	0.9854	0.9854	0.9854	137	0.9240	0.9472	0.9355	0.9862
0.0199	42.0	4032	0.0530	0.8922	0.9681	0.9286	94	0.9375	0.8982	0.9174	167	0.9926	0.9854	0.9890	137	0.9447	0.9447	0.9447	0.9881
0.0204	43.0	4128	0.0575	0.89	0.9468	0.9175	94	0.9	0.9162	0.9080	167	0.9926	0.9854	0.9890	137	0.9286	0.9472	0.9378	0.9859
0.0201	44.0	4224	0.0620	0.8585	0.9681	0.91	94	0.9231	0.8623	0.8916	167	0.9926	0.9781	0.9853	137	0.9295	0.9271	0.9283	0.9856
0.0202	45.0	4320	0.0497	0.9091	0.9574	0.9326	94	0.9152	0.9042	0.9096	167	0.9854	0.9854	0.9854	137	0.9377	0.9447	0.9412	0.9873
0.0199	46.0	4416	0.0473	0.8990	0.9468	0.9223	94	0.9268	0.9102	0.9184	167	0.9854	0.9854	0.9854	137	0.94	0.9447	0.9424	0.9881
0.0194	47.0	4512	0.0551	0.8679	0.9787	0.9200	94	0.9207	0.9042	0.9124	167	0.9926	0.9854	0.9890	137	0.9310	0.9497	0.9403	0.9862
0.0177	48.0	4608	0.0576	0.9010	0.9681	0.9333	94	0.9202	0.8982	0.9091	167	0.9854	0.9854	0.9854	137	0.9377	0.9447	0.9412	0.9867
0.0171	49.0	4704	0.0667	0.875	0.9681	0.9192	94	0.9255	0.8922	0.9085	167	0.9854	0.9854	0.9854	137	0.9328	0.9422	0.9375	0.9859
0.0197	50.0	4800	0.0641	0.8846	0.9787	0.9293	94	0.9198	0.8922	0.9058	167	0.9854	0.9854	0.9854	137	0.9330	0.9447	0.9388	0.9851
0.0166	51.0	4896	0.0642	0.875	0.9681	0.9192	94	0.9313	0.8922	0.9113	167	0.9854	0.9854	0.9854	137	0.9352	0.9422	0.9387	0.9870
0.0178	52.0	4992	0.0590	0.8969	0.9255	0.9110	94	0.9048	0.9102	0.9075	167	0.9781	0.9781	0.9781	137	0.9279	0.9372	0.9325	0.9867
0.0175	53.0	5088	0.0673	0.8835	0.9681	0.9239	94	0.9308	0.8862	0.9080	167	0.9781	0.9781	0.9781	137	0.9348	0.9372	0.9360	0.9870
0.0161	54.0	5184	0.0588	0.8738	0.9574	0.9137	94	0.9136	0.8862	0.8997	167	0.9854	0.9854	0.9854	137	0.9279	0.9372	0.9325	0.9867
0.0155	55.0	5280	0.0625	0.8911	0.9574	0.9231	94	0.8916	0.8862	0.8889	167	0.9854	0.9854	0.9854	137	0.9233	0.9372	0.9302	0.9865
0.017	56.0	5376	0.0595	0.8725	0.9468	0.9082	94	0.9091	0.8982	0.9036	167	0.9926	0.9854	0.9890	137	0.9280	0.9397	0.9338	0.9865
0.016	57.0	5472	0.0590	0.8835	0.9681	0.9239	94	0.9379	0.9042	0.9207	167	0.9853	0.9781	0.9817	137	0.94	0.9447	0.9424	0.9881
0.0148	58.0	5568	0.0595	0.9082	0.9468	0.9271	94	0.9379	0.9042	0.9207	167	0.9781	0.9781	0.9781	137	0.9444	0.9397	0.9421	0.9881
0.0148	59.0	5664	0.0575	0.9184	0.9574	0.9375	94	0.9268	0.9102	0.9184	167	0.9781	0.9781	0.9781	137	0.9424	0.9447	0.9435	0.9881
0.0121	60.0	5760	0.0620	0.8969	0.9255	0.9110	94	0.9167	0.9222	0.9194	167	0.9781	0.9781	0.9781	137	0.9328	0.9422	0.9375	0.9865
0.0159	61.0	5856	0.0629	0.9072	0.9362	0.9215	94	0.9162	0.9162	0.9162	167	0.9781	0.9781	0.9781	137	0.9352	0.9422	0.9387	0.9870
0.0155	62.0	5952	0.0692	0.9010	0.9681	0.9333	94	0.9317	0.8982	0.9146	167	0.9781	0.9781	0.9781	137	0.9398	0.9422	0.9410	0.9870
0.0143	63.0	6048	0.0656	0.8776	0.9149	0.8958	94	0.9048	0.9102	0.9075	167	0.9781	0.9781	0.9781	137	0.9231	0.9347	0.9288	0.9859
0.0137	64.0	6144	0.0657	0.9278	0.9574	0.9424	94	0.9259	0.8982	0.9119	167	0.9781	0.9781	0.9781	137	0.9444	0.9397	0.9421	0.9870
0.0135	65.0	6240	0.0683	0.8969	0.9255	0.9110	94	0.9268	0.9102	0.9184	167	0.9781	0.9781	0.9781	137	0.9372	0.9372	0.9372	0.9859
0.0128	66.0	6336	0.0657	0.91	0.9681	0.9381	94	0.9259	0.8982	0.9119	167	0.9708	0.9708	0.9708	137	0.9373	0.9397	0.9385	0.9876
0.0142	67.0	6432	0.0651	0.9278	0.9574	0.9424	94	0.9329	0.9162	0.9245	167	0.9781	0.9781	0.9781	137	0.9472	0.9472	0.9472	0.9881
0.0127	68.0	6528	0.0668	0.9062	0.9255	0.9158	94	0.9157	0.9102	0.9129	167	0.9853	0.9781	0.9817	137	0.9372	0.9372	0.9372	0.9867
0.0129	69.0	6624	0.0631	0.9010	0.9681	0.9333	94	0.925	0.8862	0.9052	167	0.9779	0.9708	0.9744	137	0.9370	0.9347	0.9358	0.9878
0.0125	70.0	6720	0.0636	0.89	0.9468	0.9175	94	0.9259	0.8982	0.9119	167	0.9781	0.9781	0.9781	137	0.9348	0.9372	0.9360	0.9873
0.0119	71.0	6816	0.0631	0.91	0.9681	0.9381	94	0.9390	0.9222	0.9305	167	0.9853	0.9781	0.9817	137	0.9475	0.9523	0.9499	0.9887
0.0111	72.0	6912	0.0621	0.91	0.9681	0.9381	94	0.9222	0.9222	0.9222	167	0.9852	0.9708	0.9779	137	0.9403	0.9497	0.9450	0.9881
0.0121	73.0	7008	0.0617	0.9271	0.9468	0.9368	94	0.9333	0.9222	0.9277	167	0.9779	0.9708	0.9744	137	0.9471	0.9447	0.9459	0.9890
0.0114	74.0	7104	0.0641	0.9091	0.9574	0.9326	94	0.9212	0.9102	0.9157	167	0.9853	0.9781	0.9817	137	0.94	0.9447	0.9424	0.9884
0.0109	75.0	7200	0.0660	0.91	0.9681	0.9381	94	0.9273	0.9162	0.9217	167	0.9781	0.9781	0.9781	137	0.9403	0.9497	0.9450	0.9878
0.0105	76.0	7296	0.0663	0.9368	0.9468	0.9418	94	0.9217	0.9162	0.9189	167	0.9853	0.9781	0.9817	137	0.9471	0.9447	0.9459	0.9878
0.01	77.0	7392	0.0678	0.9091	0.9574	0.9326	94	0.9146	0.8982	0.9063	167	0.9781	0.9781	0.9781	137	0.935	0.9397	0.9373	0.9865
0.0102	78.0	7488	0.0671	0.9175	0.9468	0.9319	94	0.9096	0.9042	0.9069	167	0.9926	0.9781	0.9853	137	0.9397	0.9397	0.9397	0.9876
0.0095	79.0	7584	0.0655	0.9278	0.9574	0.9424	94	0.9222	0.9222	0.9222	167	0.9852	0.9708	0.9779	137	0.9449	0.9472	0.9460	0.9890
0.0104	80.0	7680	0.0713	0.8544	0.9362	0.8934	94	0.9207	0.9042	0.9124	167	0.9779	0.9708	0.9744	137	0.9231	0.9347	0.9288	0.9859
0.011	81.0	7776	0.0678	0.9	0.9574	0.9278	94	0.9152	0.9042	0.9096	167	0.9926	0.9781	0.9853	137	0.9375	0.9422	0.9398	0.9876
0.0102	82.0	7872	0.0693	0.9	0.9574	0.9278	94	0.9212	0.9102	0.9157	167	0.9853	0.9781	0.9817	137	0.9377	0.9447	0.9412	0.9873
0.0092	83.0	7968	0.0683	0.8911	0.9574	0.9231	94	0.9096	0.9042	0.9069	167	0.9779	0.9708	0.9744	137	0.9280	0.9397	0.9338	0.9867
0.009	84.0	8064	0.0700	0.8911	0.9574	0.9231	94	0.9152	0.9042	0.9096	167	0.9853	0.9781	0.9817	137	0.9328	0.9422	0.9375	0.9867
0.0087	85.0	8160	0.0667	0.9278	0.9574	0.9424	94	0.9387	0.9162	0.9273	167	0.9853	0.9781	0.9817	137	0.9520	0.9472	0.9496	0.9887
0.0091	86.0	8256	0.0672	0.9192	0.9681	0.9430	94	0.9379	0.9042	0.9207	167	0.9781	0.9781	0.9781	137	0.9471	0.9447	0.9459	0.9887
0.0101	87.0	8352	0.0689	0.8824	0.9574	0.9184	94	0.9152	0.9042	0.9096	167	0.9779	0.9708	0.9744	137	0.9280	0.9397	0.9338	0.9867
0.009	88.0	8448	0.0680	0.9192	0.9681	0.9430	94	0.9264	0.9042	0.9152	167	0.9779	0.9708	0.9744	137	0.9422	0.9422	0.9422	0.9881
0.0102	89.0	8544	0.0705	0.8922	0.9681	0.9286	94	0.9198	0.8922	0.9058	167	0.9781	0.9781	0.9781	137	0.9327	0.9397	0.9362	0.9870
0.0082	90.0	8640	0.0693	0.9010	0.9681	0.9333	94	0.9146	0.8982	0.9063	167	0.9708	0.9708	0.9708	137	0.9303	0.9397	0.9350	0.9870
0.01	91.0	8736	0.0710	0.9010	0.9681	0.9333	94	0.9259	0.8982	0.9119	167	0.9781	0.9781	0.9781	137	0.9375	0.9422	0.9398	0.9876
0.0072	92.0	8832	0.0715	0.8824	0.9574	0.9184	94	0.9264	0.9042	0.9152	167	0.9781	0.9781	0.9781	137	0.9328	0.9422	0.9375	0.9873
0.007	93.0	8928	0.0720	0.9010	0.9681	0.9333	94	0.9321	0.9042	0.9179	167	0.9781	0.9781	0.9781	137	0.94	0.9447	0.9424	0.9876
0.0083	94.0	9024	0.0720	0.91	0.9681	0.9381	94	0.9375	0.8982	0.9174	167	0.9781	0.9781	0.9781	137	0.9446	0.9422	0.9434	0.9878
0.0085	95.0	9120	0.0705	0.91	0.9681	0.9381	94	0.9317	0.8982	0.9146	167	0.9781	0.9781	0.9781	137	0.9422	0.9422	0.9422	0.9876
0.0087	96.0	9216	0.0711	0.91	0.9681	0.9381	94	0.9321	0.9042	0.9179	167	0.9781	0.9781	0.9781	137	0.9424	0.9447	0.9435	0.9878
0.0079	97.0	9312	0.0725	0.91	0.9681	0.9381	94	0.925	0.8862	0.9052	167	0.9781	0.9781	0.9781	137	0.9395	0.9372	0.9384	0.9873
0.0072	98.0	9408	0.0718	0.91	0.9681	0.9381	94	0.9255	0.8922	0.9085	167	0.9781	0.9781	0.9781	137	0.9397	0.9397	0.9397	0.9876
0.0087	99.0	9504	0.0717	0.91	0.9681	0.9381	94	0.9255	0.8922	0.9085	167	0.9781	0.9781	0.9781	137	0.9397	0.9397	0.9397	0.9876
0.0086	100.0	9600	0.0714	0.91	0.9681	0.9381	94	0.9255	0.8922	0.9085	167	0.9781	0.9781	0.9781	137	0.9397	0.9397	0.9397	0.9876

Framework versions

Transformers 4.39.3
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.15.2

apwic/nerui-pt-pl10-0

nerui-pt-pl10-0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

apwic/
nerui-pt-pl10-0