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

This model is a fine-tuned version of answerdotai/ModernBERT-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3958
Accuracy Evaluate: 0.9377
Precision Evaluate: 0.9454
Recall Evaluate: 0.9361
F1 Evaluate: 0.9397
Accuracy Sklearn: 0.9377
Precision Sklearn: 0.9396
Recall Sklearn: 0.9377
F1 Sklearn: 0.9376
Acuracia Rotulo Objeto: 0.9814
Acuracia Rotulo Obrigacoes: 0.9630
Acuracia Rotulo Valor: 0.9026
Acuracia Rotulo Vigencia: 0.9711
Acuracia Rotulo Rescisao: 0.9391
Acuracia Rotulo Foro: 0.9962
Acuracia Rotulo Reajuste: 0.8932
Acuracia Rotulo Fiscalizacao: 0.8297
Acuracia Rotulo Publicacao: 0.9409
Acuracia Rotulo Pagamento: 0.8877
Acuracia Rotulo Casos Omissos: 0.9163
Acuracia Rotulo Sancoes: 0.9541
Acuracia Rotulo Dotacao Orcamentaria: 0.9945

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: 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

为此模型做种