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
0.5007	1.0	1000	0.8716	0.744	0.8071	0.7604	0.7666	0.744	0.7924	0.744	0.7473	0.9153	0.6835	0.4527	0.6299	0.8310	0.8808	0.7402	0.7413	0.9064	0.5942	0.8227	0.8899	0.7967
0.2031	2.0	2000	0.5793	0.8415	0.8590	0.8558	0.8516	0.8415	0.8525	0.8415	0.8398	0.9298	0.7879	0.5788	0.8504	0.9030	0.9885	0.8648	0.6625	0.9360	0.8804	0.8867	0.8899	0.9670
0.1306	3.0	3000	0.4607	0.8705	0.8683	0.8866	0.8735	0.8705	0.8762	0.8705	0.8708	0.8967	0.8199	0.7794	0.8898	0.7812	0.9962	0.9110	0.7855	0.9655	0.8877	0.9163	0.9358	0.9615
0.1043	4.0	4000	0.4157	0.896	0.9015	0.9058	0.9013	0.896	0.9001	0.896	0.8955	0.9607	0.8232	0.7536	0.9790	0.9252	0.9846	0.8790	0.7950	0.9064	0.9130	0.9064	0.9541	0.9945
0.0779	5.0	5000	0.4192	0.9008	0.9004	0.9124	0.9047	0.9008	0.9042	0.9008	0.9009	0.9731	0.7946	0.8711	0.9291	0.9252	0.9962	0.8932	0.7886	0.9557	0.8949	0.9113	0.9450	0.9835
0.0483	6.0	6000	0.5271	0.8992	0.9081	0.9149	0.9088	0.8992	0.9068	0.8992	0.8990	0.9793	0.7290	0.8567	0.9659	0.9252	0.9923	0.8932	0.8297	0.9951	0.9058	0.9064	0.9266	0.9890
0.0508	7.0	7000	0.4042	0.9087	0.9079	0.9202	0.9116	0.9087	0.9139	0.9087	0.9093	0.9731	0.8064	0.8596	0.9711	0.9169	0.9808	0.9004	0.8328	0.9606	0.8986	0.9064	0.9725	0.9835
0.0389	8.0	8000	0.3789	0.913	0.9114	0.9241	0.9154	0.913	0.9176	0.913	0.9133	0.9752	0.8131	0.8768	0.9738	0.9169	0.9923	0.8897	0.8328	0.9606	0.9094	0.9163	0.9725	0.9835
0.0251	9.0	9000	0.3430	0.929	0.9384	0.9312	0.9338	0.929	0.9311	0.929	0.9289	0.9855	0.9158	0.8768	0.9738	0.9335	0.9923	0.8790	0.8423	0.9754	0.8877	0.8966	0.9633	0.9835
0.0226	10.0	10000	0.4588	0.9073	0.9126	0.9215	0.9128	0.9073	0.9163	0.9073	0.9078	0.9917	0.7609	0.8711	0.9711	0.9197	0.9923	0.8648	0.8612	0.9655	0.9130	0.8916	0.9817	0.9945
0.021	11.0	11000	0.3318	0.9325	0.9357	0.9298	0.9316	0.9325	0.9341	0.9325	0.9322	0.9855	0.9529	0.8854	0.9711	0.9418	0.9923	0.8897	0.8328	0.9015	0.8949	0.9015	0.9541	0.9835
0.0138	12.0	12000	0.3269	0.939	0.9389	0.9399	0.9384	0.939	0.9403	0.939	0.9388	0.9835	0.9529	0.8911	0.9685	0.9446	0.9923	0.8897	0.8486	0.9803	0.8913	0.9113	0.9817	0.9835
0.0144	13.0	13000	0.3691	0.9327	0.9434	0.9340	0.9377	0.9327	0.9359	0.9327	0.9331	0.9897	0.9293	0.8596	0.9633	0.9391	0.9962	0.8897	0.8644	0.9803	0.8877	0.9113	0.9541	0.9780
0.0062	14.0	14000	0.3847	0.9287	0.9412	0.9271	0.9323	0.9287	0.9334	0.9287	0.9291	0.9897	0.9495	0.8911	0.9580	0.9197	0.9962	0.8754	0.8233	0.9113	0.8949	0.9015	0.9633	0.9780
0.0051	15.0	15000	0.3604	0.9335	0.9426	0.9313	0.9355	0.9335	0.9357	0.9335	0.9332	0.9835	0.9613	0.8797	0.9764	0.9363	0.9962	0.8826	0.8328	0.9064	0.8986	0.8867	0.9725	0.9945
0.0059	16.0	16000	0.3538	0.935	0.9421	0.9345	0.9372	0.935	0.9366	0.935	0.9347	0.9814	0.9545	0.8797	0.9711	0.9446	0.9962	0.9004	0.8202	0.9360	0.8949	0.9113	0.9633	0.9945
0.0047	17.0	17000	0.3679	0.9323	0.9331	0.9310	0.9304	0.9323	0.9343	0.9323	0.9319	0.9793	0.9646	0.8911	0.9711	0.9252	1.0	0.8790	0.8076	0.9163	0.8913	0.9163	0.9725	0.9890
0.0045	18.0	18000	0.3664	0.9363	0.9391	0.9381	0.9372	0.9363	0.9377	0.9363	0.9358	0.9835	0.9579	0.8682	0.9685	0.9335	1.0	0.8968	0.8139	0.9951	0.8949	0.9064	0.9817	0.9945
0.004	19.0	19000	0.3635	0.937	0.9382	0.9363	0.9359	0.937	0.9392	0.937	0.9369	0.9897	0.9512	0.9169	0.9659	0.9335	0.9962	0.8754	0.8423	0.9458	0.8804	0.9163	0.9633	0.9945
0.0024	20.0	20000	0.3885	0.9327	0.9427	0.9309	0.9353	0.9327	0.9363	0.9327	0.9329	0.9876	0.9461	0.8682	0.9738	0.9391	0.9962	0.9075	0.8549	0.9064	0.8768	0.8966	0.9541	0.9945
0.0017	21.0	21000	0.3883	0.936	0.9399	0.9341	0.9351	0.936	0.9391	0.936	0.9360	0.9876	0.9613	0.9169	0.9685	0.9224	0.9962	0.8968	0.8202	0.9212	0.8841	0.9163	0.9633	0.9890
0.0016	22.0	22000	0.3651	0.9355	0.9357	0.9337	0.9336	0.9355	0.9373	0.9355	0.9354	0.9814	0.9495	0.9083	0.9738	0.9335	0.9962	0.8897	0.8517	0.9212	0.8877	0.8966	0.9541	0.9945
0.0027	23.0	23000	0.3749	0.9357	0.9397	0.9344	0.9359	0.9357	0.9377	0.9357	0.9358	0.9814	0.9512	0.8883	0.9711	0.9363	0.9962	0.8932	0.8612	0.9212	0.8877	0.9113	0.9541	0.9945
0.0044	24.0	24000	0.3880	0.9335	0.9380	0.9325	0.9341	0.9335	0.9353	0.9335	0.9333	0.9814	0.9596	0.8825	0.9738	0.9197	0.9923	0.8968	0.8328	0.9261	0.8841	0.9163	0.9633	0.9945
0.0028	25.0	25000	0.3880	0.935	0.9398	0.9332	0.9352	0.935	0.9369	0.935	0.9348	0.9835	0.9579	0.8911	0.9711	0.9418	0.9962	0.8897	0.8360	0.9212	0.8841	0.9064	0.9633	0.9890
0.0024	26.0	26000	0.3963	0.9333	0.9366	0.9330	0.9332	0.9333	0.9362	0.9333	0.9334	0.9897	0.9343	0.9083	0.9580	0.9391	1.0	0.8826	0.8454	0.9360	0.8804	0.9015	0.9541	1.0
0.0042	27.0	27000	0.4256	0.9315	0.9319	0.9300	0.9281	0.9315	0.9362	0.9315	0.9318	0.9793	0.9529	0.9284	0.9711	0.9197	0.9962	0.8612	0.8076	0.9261	0.8877	0.9064	0.9541	1.0
0.0032	28.0	28000	0.3806	0.9363	0.9407	0.9345	0.9366	0.9363	0.9379	0.9363	0.9361	0.9835	0.9444	0.8968	0.9790	0.9501	0.9962	0.9004	0.8486	0.9360	0.8768	0.8916	0.9450	1.0
0.0021	29.0	29000	0.3779	0.9395	0.9475	0.9375	0.9414	0.9395	0.9412	0.9395	0.9393	0.9793	0.9596	0.9083	0.9711	0.9612	0.9962	0.8897	0.8328	0.9458	0.8877	0.9113	0.9450	1.0
0.002	30.0	30000	0.3918	0.9333	0.9394	0.9315	0.9339	0.9333	0.9356	0.9333	0.9330	0.9814	0.9697	0.8739	0.9711	0.9252	0.9962	0.9004	0.8170	0.9212	0.8841	0.9113	0.9633	0.9945
0.0022	31.0	31000	0.3868	0.9373	0.9436	0.9349	0.9382	0.9373	0.9392	0.9373	0.9372	0.9855	0.9613	0.9054	0.9711	0.9280	0.9962	0.8932	0.8423	0.9409	0.8841	0.9064	0.9450	0.9945
0.0017	32.0	32000	0.4050	0.9325	0.9417	0.9309	0.9351	0.9325	0.9344	0.9325	0.9322	0.9814	0.9545	0.8854	0.9738	0.9363	0.9962	0.8861	0.8233	0.9261	0.8877	0.8966	0.9541	1.0
0.001	33.0	33000	0.3841	0.9375	0.9461	0.9346	0.9393	0.9375	0.9393	0.9375	0.9373	0.9835	0.9613	0.9054	0.9738	0.9446	0.9962	0.8932	0.8360	0.9212	0.8841	0.9113	0.9450	0.9945
0.0019	34.0	34000	0.4004	0.9337	0.9401	0.9312	0.9341	0.9337	0.9362	0.9337	0.9335	0.9835	0.9596	0.8997	0.9711	0.9363	0.9962	0.8932	0.8139	0.9212	0.8841	0.9015	0.9450	1.0
0.0027	35.0	35000	0.3941	0.935	0.9407	0.9329	0.9353	0.935	0.9374	0.935	0.9349	0.9814	0.9562	0.9140	0.9711	0.9307	0.9962	0.8897	0.8328	0.9212	0.8841	0.9015	0.9541	0.9945
0.0	36.0	36000	0.3833	0.9395	0.9470	0.9373	0.9411	0.9395	0.9411	0.9395	0.9393	0.9814	0.9630	0.9083	0.9685	0.9501	0.9962	0.9146	0.8265	0.9409	0.8841	0.9113	0.9450	0.9945
0.001	37.0	37000	0.3958	0.9357	0.9441	0.9341	0.9379	0.9357	0.9376	0.9357	0.9355	0.9855	0.9613	0.8883	0.9711	0.9363	0.9962	0.9004	0.8297	0.9212	0.8841	0.9064	0.9633	1.0
0.0012	38.0	38000	0.3790	0.938	0.9421	0.9347	0.9372	0.938	0.9398	0.938	0.9378	0.9876	0.9613	0.9112	0.9685	0.9446	0.9962	0.9004	0.8391	0.9163	0.8804	0.9064	0.9450	0.9945
0.0008	39.0	39000	0.3849	0.9363	0.9431	0.9348	0.9379	0.9363	0.9378	0.9363	0.9360	0.9835	0.9596	0.8968	0.9711	0.9363	0.9962	0.8932	0.8360	0.9409	0.8841	0.8966	0.9633	0.9945
0.0023	40.0	40000	0.3834	0.9383	0.9418	0.9364	0.9378	0.9383	0.9399	0.9383	0.9380	0.9855	0.9646	0.8911	0.9711	0.9418	0.9962	0.9181	0.8265	0.9212	0.8877	0.9113	0.9633	0.9945
0.0013	41.0	41000	0.3837	0.9387	0.9464	0.9364	0.9404	0.9387	0.9404	0.9387	0.9386	0.9835	0.9630	0.9054	0.9685	0.9474	0.9962	0.9039	0.8297	0.9409	0.8841	0.9113	0.9450	0.9945
0.0007	42.0	42000	0.3959	0.9353	0.9414	0.9341	0.9364	0.9353	0.9372	0.9353	0.9350	0.9814	0.9630	0.8854	0.9711	0.9335	0.9962	0.9004	0.8297	0.9212	0.8877	0.9064	0.9725	0.9945
0.0006	43.0	43000	0.3876	0.938	0.9454	0.9362	0.9397	0.938	0.9398	0.938	0.9378	0.9835	0.9630	0.9026	0.9711	0.9418	0.9962	0.8968	0.8265	0.9409	0.8877	0.9113	0.9541	0.9945
0.0016	44.0	44000	0.3886	0.936	0.9432	0.9347	0.9378	0.936	0.9378	0.936	0.9358	0.9814	0.9613	0.8997	0.9711	0.9335	0.9962	0.8932	0.8328	0.9212	0.8877	0.9064	0.9725	0.9945
0.0016	45.0	45000	0.3989	0.9375	0.9457	0.9354	0.9394	0.9375	0.9395	0.9375	0.9373	0.9855	0.9613	0.9112	0.9685	0.9391	0.9962	0.9004	0.8170	0.9409	0.8841	0.9113	0.9450	1.0
0.0	46.0	46000	0.3869	0.9383	0.9458	0.9363	0.9400	0.9383	0.9399	0.9383	0.9381	0.9835	0.9630	0.9054	0.9711	0.9391	0.9962	0.9004	0.8328	0.9409	0.8841	0.9064	0.9541	0.9945
0.0009	47.0	47000	0.3955	0.937	0.9448	0.9353	0.9389	0.937	0.9389	0.937	0.9368	0.9814	0.9630	0.8997	0.9711	0.9391	0.9962	0.8932	0.8265	0.9409	0.8877	0.9113	0.9541	0.9945
0.0019	48.0	48000	0.3959	0.9375	0.9452	0.9359	0.9395	0.9375	0.9393	0.9375	0.9373	0.9814	0.9630	0.9026	0.9711	0.9391	0.9962	0.8932	0.8265	0.9409	0.8877	0.9163	0.9541	0.9945
0.0011	49.0	49000	0.3951	0.9377	0.9454	0.9361	0.9397	0.9377	0.9396	0.9377	0.9376	0.9814	0.9630	0.9026	0.9711	0.9391	0.9962	0.8932	0.8297	0.9409	0.8877	0.9163	0.9541	0.9945
0.0006	50.0	50000	0.3958	0.9377	0.9454	0.9361	0.9397	0.9377	0.9396	0.9377	0.9376	0.9814	0.9630	0.9026	0.9711	0.9391	0.9962	0.8932	0.8297	0.9409	0.8877	0.9163	0.9541	0.9945

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-v4-50-512

ModernBERT-base-2-contract-sections-classification-v4-50-512

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

marcelovidigal/
ModernBERT-base-2-contract-sections-classification-v4-50-512