learning_rate: 1e-06
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Evaluate	Precision Evaluate	Recall Evaluate	F1 Evaluate	Accuracy Sklearn	Precision Sklearn	Recall Sklearn	F1 Sklearn	Acuracia Rotulo Objeto	Acuracia Rotulo Obrigacoes	Acuracia Rotulo Valor	Acuracia Rotulo Vigencia	Acuracia Rotulo Rescisao	Acuracia Rotulo Foro	Acuracia Rotulo Reajuste	Acuracia Rotulo Fiscalizacao	Acuracia Rotulo Publicacao	Acuracia Rotulo Pagamento	Acuracia Rotulo Casos Omissos	Acuracia Rotulo Sancoes	Acuracia Rotulo Dotacao Orcamentaria
0.5171	1.0	1000	0.9144	0.732	0.7887	0.7494	0.7499	0.732	0.7735	0.732	0.7332	0.8822	0.6886	0.4556	0.6745	0.8033	0.8846	0.7509	0.7224	0.8276	0.4565	0.8227	0.9266	0.8462
0.2169	2.0	2000	0.6043	0.8325	0.8593	0.8461	0.8475	0.8325	0.8407	0.8325	0.8308	0.8926	0.8182	0.5587	0.7638	0.9003	0.9885	0.8434	0.7571	0.8818	0.8659	0.8571	0.8991	0.9725
0.1396	3.0	3000	0.4994	0.8602	0.8659	0.8764	0.8660	0.8602	0.8679	0.8602	0.8596	0.9050	0.8148	0.6447	0.8924	0.8061	0.9923	0.8932	0.8202	0.9655	0.8587	0.9015	0.9266	0.9725
0.1134	4.0	4000	0.4213	0.8925	0.8971	0.8970	0.8943	0.8925	0.8959	0.8925	0.8917	0.9483	0.8822	0.7249	0.9475	0.9363	0.9962	0.8505	0.7823	0.8966	0.8841	0.9015	0.9266	0.9835
0.09	5.0	5000	0.4038	0.9012	0.9057	0.9062	0.9046	0.9012	0.9031	0.9012	0.9010	0.9669	0.8687	0.7966	0.9318	0.9391	1.0	0.8648	0.8170	0.9163	0.8587	0.9064	0.9358	0.9780
0.0569	6.0	6000	0.4327	0.8925	0.8914	0.9055	0.8959	0.8925	0.8965	0.8925	0.8917	0.9773	0.7609	0.7966	0.9685	0.9307	0.9962	0.8470	0.8202	0.9507	0.8913	0.9015	0.9358	0.9945
0.0573	7.0	7000	0.3939	0.911	0.9142	0.9140	0.9122	0.911	0.9140	0.911	0.9108	0.9835	0.8939	0.7536	0.9633	0.9307	0.9962	0.8790	0.8675	0.9015	0.8696	0.9113	0.9541	0.9780
0.0433	8.0	8000	0.3762	0.92	0.9204	0.9193	0.9174	0.92	0.9238	0.92	0.9200	0.9814	0.9512	0.7822	0.9659	0.9197	0.9923	0.8790	0.8423	0.9015	0.8768	0.9261	0.9541	0.9780
0.0267	9.0	9000	0.3790	0.9215	0.9290	0.9192	0.9221	0.9215	0.9255	0.9215	0.9214	0.9855	0.9461	0.7851	0.9764	0.9335	0.9923	0.8754	0.8580	0.8867	0.8804	0.9064	0.9450	0.9780
0.0242	10.0	10000	0.4134	0.9153	0.9131	0.9174	0.9095	0.9153	0.9259	0.9153	0.9174	0.9876	0.9259	0.8252	0.9475	0.8476	0.9962	0.8577	0.8833	0.9113	0.8913	0.8966	0.9725	0.9835
0.0257	11.0	11000	0.3048	0.9365	0.9361	0.9346	0.9346	0.9365	0.9371	0.9365	0.9361	0.9835	0.9579	0.8596	0.9816	0.9529	0.9923	0.8826	0.8612	0.9163	0.8986	0.9163	0.9633	0.9835
0.0164	12.0	12000	0.3451	0.9313	0.9320	0.9292	0.9290	0.9313	0.9336	0.9313	0.9312	0.9773	0.9613	0.8768	0.9659	0.9446	0.9923	0.8719	0.8486	0.9163	0.8623	0.9212	0.9633	0.9780
0.0182	13.0	13000	0.3905	0.9275	0.9395	0.9254	0.9307	0.9275	0.9310	0.9275	0.9273	0.9917	0.9579	0.7908	0.9685	0.9363	0.9962	0.8897	0.8707	0.9409	0.8623	0.8966	0.9450	0.9835
0.0089	14.0	14000	0.4056	0.929	0.9340	0.9290	0.9301	0.929	0.9313	0.929	0.9286	0.9897	0.9579	0.8080	0.9685	0.9363	0.9962	0.8612	0.8454	0.9557	0.8913	0.9113	0.9725	0.9835
0.007	15.0	15000	0.3713	0.9313	0.9279	0.9321	0.9285	0.9313	0.9331	0.9313	0.9310	0.9773	0.9461	0.8539	0.9790	0.9280	0.9962	0.8790	0.8391	0.9310	0.9094	0.9163	0.9725	0.9890
0.0067	16.0	16000	0.3521	0.934	0.9322	0.9332	0.9316	0.934	0.9355	0.934	0.9340	0.9711	0.9596	0.8711	0.9764	0.9197	0.9885	0.9004	0.8644	0.9261	0.8913	0.9163	0.9633	0.9835
0.0053	17.0	17000	0.3731	0.9325	0.9337	0.9306	0.9305	0.9325	0.9346	0.9325	0.9322	0.9814	0.9630	0.8567	0.9816	0.9391	1.0	0.8861	0.8265	0.9163	0.8841	0.9163	0.9633	0.9835
0.005	18.0	18000	0.3707	0.9343	0.9337	0.9343	0.9329	0.9343	0.9359	0.9343	0.9341	0.9855	0.9512	0.8596	0.9711	0.9335	0.9962	0.8683	0.8675	0.9507	0.8877	0.9113	0.9633	1.0
0.0037	19.0	19000	0.3963	0.9343	0.9333	0.9337	0.9319	0.9343	0.9375	0.9343	0.9345	0.9897	0.9293	0.9054	0.9816	0.9307	0.9962	0.8683	0.8675	0.9458	0.8768	0.9015	0.9450	1.0
0.0026	20.0	20000	0.4162	0.9293	0.9343	0.9258	0.9283	0.9293	0.9324	0.9293	0.9291	0.9897	0.9613	0.8539	0.9790	0.9307	0.9923	0.8897	0.8328	0.9212	0.8551	0.9015	0.9450	0.9835
0.0021	21.0	21000	0.3894	0.9337	0.9364	0.9312	0.9326	0.9337	0.9357	0.9337	0.9336	0.9897	0.9630	0.8625	0.9659	0.9363	0.9962	0.8826	0.8517	0.9261	0.8877	0.9064	0.9541	0.9835
0.0018	22.0	22000	0.3775	0.9355	0.9354	0.9338	0.9334	0.9355	0.9372	0.9355	0.9353	0.9855	0.9596	0.8510	0.9764	0.9335	0.9962	0.8897	0.8770	0.9458	0.8804	0.9064	0.9541	0.9835
0.003	23.0	23000	0.3692	0.9353	0.9353	0.9337	0.9334	0.9353	0.9368	0.9353	0.9351	0.9855	0.9579	0.8711	0.9711	0.9335	0.9962	0.8861	0.8612	0.9261	0.8913	0.9113	0.9633	0.9835
0.0036	24.0	24000	0.4267	0.9285	0.9309	0.9271	0.9267	0.9285	0.9317	0.9285	0.9283	0.9793	0.9697	0.8539	0.9711	0.9224	0.9923	0.8861	0.8265	0.9163	0.8587	0.9113	0.9817	0.9835
0.0034	25.0	25000	0.4385	0.928	0.9338	0.9253	0.9273	0.928	0.9321	0.928	0.9281	0.9876	0.9613	0.8481	0.9711	0.9141	0.9962	0.8861	0.8423	0.9310	0.8659	0.8966	0.9450	0.9835
0.0021	26.0	26000	0.3957	0.9363	0.9364	0.9368	0.9349	0.9363	0.9388	0.9363	0.9364	0.9773	0.9428	0.8940	0.9790	0.9391	0.9962	0.8790	0.8675	0.9507	0.8841	0.8966	0.9725	1.0
0.0038	27.0	27000	0.4856	0.9227	0.9351	0.9218	0.9265	0.9227	0.9288	0.9227	0.9234	0.9917	0.9478	0.8109	0.9580	0.9169	0.9923	0.8612	0.8580	0.9360	0.8696	0.9015	0.9450	0.9945
0.0035	28.0	28000	0.4028	0.9353	0.9375	0.9325	0.9337	0.9353	0.9374	0.9353	0.9352	0.9917	0.9596	0.8596	0.9738	0.9280	0.9962	0.9146	0.8644	0.9212	0.8732	0.9113	0.9450	0.9835
0.0022	29.0	29000	0.4053	0.9367	0.9420	0.9345	0.9373	0.9367	0.9385	0.9367	0.9366	0.9876	0.9596	0.8539	0.9790	0.9529	0.9923	0.8861	0.8738	0.9458	0.8659	0.9064	0.9450	1.0
0.0017	30.0	30000	0.3755	0.936	0.9365	0.9347	0.9347	0.936	0.9375	0.936	0.9358	0.9855	0.9579	0.8510	0.9790	0.9307	0.9962	0.8932	0.8738	0.9507	0.8768	0.9113	0.9450	1.0
0.0025	31.0	31000	0.4102	0.9347	0.9403	0.9320	0.9347	0.9347	0.9375	0.9347	0.9348	0.9917	0.9630	0.8711	0.9685	0.9280	0.9962	0.8826	0.8580	0.9458	0.8768	0.9064	0.9450	0.9835
0.0011	32.0	32000	0.4056	0.935	0.9369	0.9330	0.9338	0.935	0.9371	0.935	0.9350	0.9876	0.9596	0.8653	0.9738	0.9280	0.9962	0.8861	0.8612	0.9507	0.8804	0.9064	0.9450	0.9890
0.0009	33.0	33000	0.4007	0.936	0.9380	0.9339	0.9348	0.936	0.9381	0.936	0.9360	0.9876	0.9596	0.8653	0.9738	0.9363	0.9962	0.8861	0.8644	0.9507	0.8804	0.9064	0.9450	0.9890
0.0021	34.0	34000	0.4073	0.9327	0.9368	0.9302	0.9322	0.9327	0.9351	0.9327	0.9326	0.9876	0.9596	0.8481	0.9816	0.9307	0.9962	0.8897	0.8549	0.9163	0.8768	0.9064	0.9450	1.0
0.0023	35.0	35000	0.3993	0.9355	0.9389	0.9345	0.9353	0.9355	0.9377	0.9355	0.9354	0.9835	0.9579	0.8797	0.9790	0.9252	0.9923	0.8861	0.8549	0.9507	0.8804	0.9064	0.9633	0.9890
0.0	36.0	36000	0.3990	0.9365	0.9383	0.9342	0.9353	0.9365	0.9381	0.9365	0.9364	0.9897	0.9596	0.8625	0.9790	0.9363	0.9962	0.8897	0.8644	0.9507	0.8768	0.9064	0.9450	0.9890
0.001	37.0	37000	0.3950	0.9393	0.9406	0.9376	0.9383	0.9393	0.9405	0.9393	0.9391	0.9897	0.9596	0.8625	0.9790	0.9363	0.9923	0.9039	0.8801	0.9507	0.8804	0.9113	0.9541	0.9890
0.001	38.0	38000	0.3969	0.9373	0.9400	0.9349	0.9366	0.9373	0.9388	0.9373	0.9372	0.9876	0.9613	0.8596	0.9790	0.9363	0.9962	0.8932	0.8707	0.9458	0.8841	0.9064	0.9450	0.9890
0.0	39.0	39000	0.4129	0.9367	0.9370	0.9356	0.9352	0.9367	0.9385	0.9367	0.9366	0.9876	0.9596	0.8596	0.9790	0.9280	0.9923	0.8897	0.8770	0.9507	0.8804	0.9064	0.9633	0.9890
0.002	40.0	40000	0.4130	0.9335	0.9346	0.9332	0.9325	0.9335	0.9354	0.9335	0.9332	0.9835	0.9613	0.8596	0.9738	0.9280	0.9923	0.8897	0.8328	0.9507	0.8877	0.9113	0.9725	0.9890
0.0007	41.0	41000	0.3864	0.939	0.9441	0.9365	0.9395	0.939	0.9402	0.939	0.9388	0.9876	0.9630	0.8625	0.9764	0.9557	0.9962	0.9039	0.8612	0.9458	0.8768	0.9113	0.9450	0.9890
0.0006	42.0	42000	0.4130	0.936	0.9388	0.9349	0.9359	0.936	0.9375	0.936	0.9358	0.9876	0.9596	0.8367	0.9790	0.9418	0.9962	0.8897	0.8738	0.9507	0.8804	0.9064	0.9633	0.9890
0.0007	43.0	43000	0.4165	0.9367	0.9388	0.9346	0.9358	0.9367	0.9384	0.9367	0.9366	0.9897	0.9596	0.8510	0.9790	0.9363	0.9962	0.8897	0.8770	0.9507	0.8804	0.9064	0.9450	0.9890
0.001	44.0	44000	0.4147	0.9357	0.9370	0.9349	0.9346	0.9357	0.9378	0.9357	0.9356	0.9897	0.9613	0.8596	0.9738	0.9307	0.9923	0.8897	0.8612	0.9507	0.8768	0.9064	0.9725	0.9890
0.0017	45.0	45000	0.4130	0.9363	0.9371	0.9342	0.9346	0.9363	0.9382	0.9363	0.9362	0.9897	0.9596	0.8653	0.9764	0.9307	0.9962	0.8897	0.8612	0.9507	0.8804	0.9113	0.9450	0.9890
0.0	46.0	46000	0.4122	0.936	0.9409	0.9336	0.9361	0.936	0.9381	0.936	0.9359	0.9897	0.9613	0.8625	0.9764	0.9307	0.9962	0.8897	0.8644	0.9507	0.8804	0.9064	0.9450	0.9835
0.001	47.0	47000	0.4153	0.9353	0.9377	0.9340	0.9344	0.9353	0.9376	0.9353	0.9352	0.9897	0.9613	0.8653	0.9738	0.9280	0.9962	0.8897	0.8454	0.9507	0.8841	0.9113	0.9633	0.9835
0.0015	48.0	48000	0.4161	0.9355	0.9382	0.9340	0.9347	0.9355	0.9379	0.9355	0.9354	0.9897	0.9613	0.8682	0.9738	0.9280	0.9962	0.8897	0.8454	0.9507	0.8841	0.9113	0.9541	0.9890
0.0008	49.0	49000	0.4165	0.935	0.9378	0.9335	0.9343	0.935	0.9373	0.935	0.9349	0.9897	0.9613	0.8653	0.9738	0.9280	0.9962	0.8897	0.8454	0.9507	0.8804	0.9113	0.9541	0.9890
0.0005	50.0	50000	0.4160	0.9353	0.9380	0.9337	0.9345	0.9353	0.9376	0.9353	0.9352	0.9897	0.9613	0.8653	0.9738	0.9280	0.9962	0.8897	0.8454	0.9507	0.8841	0.9113	0.9541	0.9890

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.0
Tokenizers 0.21.0

marcelovidigal/ModernBERT-base-2-contract-sections-classification-v4-50-1024

ModernBERT-base-2-contract-sections-classification-v4-50-1024

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

marcelovidigal/
ModernBERT-base-2-contract-sections-classification-v4-50-1024