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
1.7552	1.0	1000	1.9079	0.496	0.6020	0.4649	0.4675	0.496	0.5822	0.496	0.4775	0.8264	0.8283	0.2149	0.4121	0.2632	0.5231	0.2633	0.3817	0.6749	0.2681	0.7586	0.6239	0.0055
1.1842	2.0	2000	1.5372	0.622	0.7106	0.5977	0.6107	0.622	0.6829	0.622	0.6108	0.8781	0.8704	0.2321	0.5092	0.6122	0.8962	0.3381	0.5394	0.7389	0.4493	0.7931	0.6881	0.2253
0.813	3.0	3000	1.2294	0.6975	0.7280	0.6856	0.6952	0.6975	0.7176	0.6975	0.6951	0.8657	0.8653	0.3668	0.5433	0.6260	0.9231	0.6548	0.5868	0.8128	0.6920	0.7931	0.6330	0.5495
0.5719	4.0	4000	1.0358	0.743	0.7902	0.7325	0.7510	0.743	0.7686	0.743	0.7440	0.9091	0.8788	0.5072	0.5564	0.7258	0.9115	0.6904	0.7161	0.7783	0.6268	0.8128	0.7064	0.7033
0.4072	5.0	5000	0.8484	0.797	0.8183	0.7912	0.7998	0.797	0.8073	0.797	0.7962	0.9174	0.8771	0.6963	0.5696	0.8144	0.9231	0.8149	0.7287	0.8719	0.7029	0.8128	0.6881	0.8681
0.2805	6.0	6000	0.7369	0.8153	0.8329	0.8113	0.8194	0.8153	0.8216	0.8153	0.8154	0.8905	0.8855	0.7994	0.6220	0.7784	0.9192	0.8185	0.7760	0.8522	0.7681	0.8079	0.7339	0.8956
0.2155	7.0	7000	0.6316	0.846	0.8552	0.8436	0.8468	0.846	0.8510	0.846	0.8459	0.9174	0.9024	0.8481	0.6982	0.8199	0.9269	0.8292	0.8013	0.9557	0.7645	0.7734	0.8073	0.9231
0.1517	8.0	8000	0.5717	0.8548	0.8599	0.8557	0.8558	0.8548	0.8593	0.8548	0.8551	0.8988	0.8939	0.8453	0.7664	0.8283	0.9231	0.8327	0.8202	0.9507	0.7572	0.8276	0.8349	0.9451
0.1117	9.0	9000	0.5221	0.8682	0.8719	0.8682	0.8683	0.8682	0.8718	0.8682	0.8685	0.8884	0.9074	0.8625	0.8451	0.8227	0.9346	0.8185	0.8391	0.9754	0.7935	0.8227	0.8532	0.9231
0.0909	10.0	10000	0.4948	0.8782	0.8785	0.8752	0.8751	0.8782	0.8820	0.8782	0.8787	0.9153	0.9108	0.8797	0.8635	0.8366	0.9269	0.8256	0.8517	0.9754	0.8225	0.8079	0.8440	0.9176
0.0766	11.0	11000	0.4817	0.8798	0.8840	0.8776	0.8783	0.8798	0.8854	0.8798	0.8802	0.9215	0.9209	0.8567	0.8793	0.8449	0.9346	0.8256	0.7950	0.9852	0.8152	0.8276	0.8349	0.9670
0.0545	12.0	12000	0.4598	0.8872	0.8912	0.8857	0.8870	0.8872	0.8905	0.8872	0.8874	0.8905	0.9394	0.8768	0.8766	0.8560	0.9346	0.8292	0.8517	0.9901	0.8225	0.8325	0.8532	0.9615
0.0506	13.0	13000	0.4764	0.8882	0.8971	0.8862	0.8891	0.8882	0.8939	0.8882	0.8889	0.9029	0.9360	0.9054	0.8583	0.8476	0.9308	0.8399	0.8644	0.9803	0.8188	0.8128	0.8624	0.9615
0.039	14.0	14000	0.4569	0.8942	0.8982	0.8928	0.8930	0.8942	0.8989	0.8942	0.8946	0.9091	0.9276	0.8940	0.9239	0.8643	0.9346	0.8363	0.8170	0.9803	0.8514	0.8177	0.8716	0.9780
0.0337	15.0	15000	0.4747	0.891	0.8953	0.8901	0.8900	0.891	0.8971	0.891	0.8918	0.9215	0.9175	0.8825	0.9029	0.8504	0.9385	0.8363	0.8328	0.9803	0.8297	0.8424	0.8532	0.9835
0.0317	16.0	16000	0.4436	0.898	0.9022	0.8947	0.8966	0.898	0.9013	0.898	0.8981	0.9050	0.9276	0.9054	0.9423	0.8947	0.9346	0.8363	0.8202	0.9852	0.8442	0.8177	0.8624	0.9560
0.0322	17.0	17000	0.4583	0.903	0.9096	0.9017	0.9039	0.903	0.9062	0.903	0.9031	0.8884	0.9276	0.9198	0.9685	0.8837	0.9346	0.8541	0.8423	0.9901	0.8225	0.8571	0.8716	0.9615
0.0247	18.0	18000	0.4442	0.9018	0.9053	0.9012	0.9013	0.9018	0.9052	0.9018	0.9020	0.8988	0.9242	0.8997	0.9449	0.8892	0.9385	0.8505	0.8360	0.9852	0.8551	0.8325	0.8716	0.9890
0.0237	19.0	19000	0.4582	0.9058	0.9115	0.9053	0.9071	0.9058	0.9081	0.9058	0.9058	0.8884	0.9310	0.9169	0.9475	0.8975	0.9385	0.8577	0.8328	0.9901	0.8623	0.8571	0.8716	0.9780
0.0194	20.0	20000	0.4580	0.906	0.9115	0.9042	0.9065	0.906	0.9083	0.906	0.9060	0.8905	0.9343	0.9226	0.9528	0.8892	0.95	0.8541	0.8391	0.9901	0.8514	0.8621	0.8624	0.9560
0.0159	21.0	21000	0.4595	0.907	0.9142	0.9075	0.9093	0.907	0.9100	0.907	0.9072	0.9050	0.9209	0.9140	0.9475	0.8975	0.95	0.8470	0.8423	0.9901	0.8514	0.8670	0.8807	0.9835
0.0141	22.0	22000	0.4668	0.9048	0.9122	0.9045	0.9068	0.9048	0.9073	0.9048	0.9047	0.9050	0.9259	0.9169	0.9501	0.9058	0.9385	0.8470	0.8233	0.9901	0.8370	0.8473	0.8991	0.9725
0.0109	23.0	23000	0.4506	0.9097	0.9159	0.9088	0.9110	0.9097	0.9120	0.9097	0.9097	0.9008	0.9276	0.9112	0.9790	0.8975	0.95	0.8577	0.8360	0.9507	0.8623	0.8768	0.8807	0.9835
0.011	24.0	24000	0.4544	0.9095	0.9116	0.9090	0.9084	0.9095	0.9125	0.9095	0.9096	0.9070	0.9192	0.9226	0.9711	0.8920	0.9577	0.8505	0.8360	0.9951	0.8623	0.8424	0.8716	0.9890
0.0093	25.0	25000	0.4787	0.9117	0.9171	0.9120	0.9129	0.9117	0.9147	0.9117	0.9119	0.9050	0.9343	0.9169	0.9580	0.9030	0.95	0.8577	0.8233	0.9901	0.8623	0.8818	0.8899	0.9835
0.0098	26.0	26000	0.4674	0.9093	0.9156	0.9093	0.9109	0.9093	0.9119	0.9093	0.9092	0.8967	0.9276	0.9255	0.9659	0.8892	0.9615	0.8577	0.8139	0.9951	0.8768	0.8473	0.8807	0.9835
0.0084	27.0	27000	0.4786	0.912	0.9190	0.9125	0.9136	0.912	0.9166	0.912	0.9123	0.9029	0.9343	0.9370	0.9606	0.8864	0.9615	0.8577	0.8233	0.9951	0.8478	0.8768	0.8899	0.9890
0.009	28.0	28000	0.4959	0.9107	0.9183	0.9103	0.9123	0.9107	0.9145	0.9107	0.9109	0.9132	0.9377	0.9198	0.9554	0.8892	0.95	0.8577	0.8202	0.9901	0.8551	0.8670	0.8899	0.9890
0.0068	29.0	29000	0.4824	0.9135	0.9199	0.9146	0.9153	0.9135	0.9172	0.9135	0.9136	0.8905	0.9310	0.9341	0.9738	0.8975	0.9692	0.8577	0.8044	0.9951	0.8804	0.8867	0.8807	0.9890
0.0085	30.0	30000	0.4846	0.9103	0.9230	0.9091	0.9140	0.9103	0.9147	0.9103	0.9105	0.9008	0.9327	0.9198	0.9790	0.8975	0.9577	0.8541	0.8170	0.9901	0.8587	0.8571	0.8807	0.9725
0.0057	31.0	31000	0.4978	0.9107	0.9132	0.9131	0.9110	0.9107	0.9142	0.9107	0.9108	0.8905	0.9293	0.9140	0.9633	0.8975	0.9692	0.8648	0.8139	1.0	0.8623	0.8768	0.8991	0.9890
0.0036	32.0	32000	0.5003	0.9077	0.9181	0.9090	0.9117	0.9077	0.9112	0.9077	0.9078	0.8864	0.9377	0.8968	0.9659	0.8947	0.9692	0.8541	0.8076	1.0	0.8587	0.8719	0.8899	0.9835
0.006	33.0	33000	0.5001	0.9087	0.9136	0.9095	0.9096	0.9087	0.9119	0.9087	0.9088	0.8967	0.9276	0.9198	0.9633	0.8975	0.9577	0.8612	0.8076	1.0	0.8623	0.8621	0.8899	0.9780
0.005	34.0	34000	0.4861	0.9117	0.9169	0.9131	0.9132	0.9117	0.9150	0.9117	0.9119	0.9008	0.9293	0.8968	0.9659	0.9058	0.9615	0.8505	0.8202	1.0	0.8841	0.8768	0.8899	0.9890
0.0046	35.0	35000	0.4997	0.9093	0.9145	0.9113	0.9111	0.9093	0.9122	0.9093	0.9092	0.8802	0.9259	0.9169	0.9633	0.9058	0.9654	0.8577	0.8013	1.0	0.8841	0.8768	0.8807	0.9890
0.0021	36.0	36000	0.5209	0.9087	0.9151	0.9101	0.9100	0.9087	0.9135	0.9087	0.9090	0.8926	0.9327	0.9226	0.9580	0.9030	0.9577	0.8505	0.8107	1.0	0.8333	0.8916	0.8899	0.9890
0.0029	37.0	37000	0.4948	0.9083	0.9120	0.9105	0.9096	0.9083	0.9109	0.9083	0.9082	0.8719	0.9310	0.9198	0.9396	0.9114	0.9692	0.8577	0.8139	1.0	0.8913	0.8571	0.8899	0.9835
0.0038	38.0	38000	0.5256	0.909	0.9160	0.9106	0.9114	0.909	0.9125	0.909	0.9091	0.9070	0.9209	0.8911	0.9738	0.9030	0.9577	0.8541	0.8139	1.0	0.8551	0.8768	0.8899	0.9945
0.0019	39.0	39000	0.5229	0.9087	0.9188	0.9105	0.9127	0.9087	0.9128	0.9087	0.9090	0.8864	0.9242	0.9198	0.9685	0.9058	0.9577	0.8541	0.8107	1.0	0.8587	0.8768	0.8899	0.9835
0.0028	40.0	40000	0.5419	0.9097	0.9194	0.9101	0.9123	0.9097	0.9145	0.9097	0.9099	0.8967	0.9377	0.9169	0.9738	0.8975	0.95	0.8541	0.8044	1.0	0.8442	0.8768	0.8899	0.9890
0.0015	41.0	41000	0.5141	0.91	0.9114	0.9133	0.9102	0.91	0.9133	0.91	0.9101	0.8884	0.9158	0.9054	0.9685	0.9086	0.9654	0.8612	0.8013	1.0	0.8877	0.8867	0.8899	0.9945
0.0024	42.0	42000	0.5203	0.9115	0.9163	0.9135	0.9132	0.9115	0.9142	0.9115	0.9115	0.9029	0.9226	0.8968	0.9633	0.9114	0.9654	0.8612	0.8139	1.0	0.8768	0.8768	0.8899	0.9945
0.0016	43.0	43000	0.5236	0.9123	0.9148	0.9159	0.9134	0.9123	0.9153	0.9123	0.9123	0.8781	0.9226	0.9054	0.9633	0.9086	1.0	0.8648	0.8107	1.0	0.8877	0.8867	0.8899	0.9890
0.0015	44.0	44000	0.5263	0.9093	0.9137	0.9118	0.9110	0.9093	0.9119	0.9093	0.9092	0.8884	0.9158	0.9026	0.9711	0.9058	0.9692	0.8541	0.8170	1.0	0.8768	0.8768	0.8807	0.9945
0.0026	45.0	45000	0.5248	0.9107	0.9180	0.9120	0.9134	0.9107	0.9136	0.9107	0.9108	0.9008	0.9209	0.9169	0.9711	0.9058	0.9615	0.8541	0.8107	1.0	0.8623	0.8768	0.8807	0.9945
0.0002	46.0	46000	0.5317	0.91	0.9144	0.9120	0.9113	0.91	0.9130	0.91	0.9100	0.8905	0.9226	0.9026	0.9711	0.9003	0.9692	0.8541	0.8170	1.0	0.8768	0.8768	0.8807	0.9945
0.0017	47.0	47000	0.5278	0.9117	0.9182	0.9140	0.9147	0.9117	0.9144	0.9117	0.9118	0.8843	0.9209	0.9169	0.9685	0.9086	0.9654	0.8648	0.8170	1.0	0.8841	0.8818	0.8807	0.9890
0.0022	48.0	48000	0.5294	0.9093	0.9135	0.9114	0.9104	0.9093	0.9127	0.9093	0.9093	0.8802	0.9226	0.9112	0.9711	0.9030	0.9692	0.8505	0.8170	1.0	0.8768	0.8768	0.8807	0.9890
0.0013	49.0	49000	0.5299	0.9097	0.9148	0.9117	0.9115	0.9097	0.9128	0.9097	0.9098	0.8864	0.9209	0.9083	0.9711	0.9058	0.9692	0.8541	0.8170	1.0	0.8732	0.8768	0.8807	0.9890
0.0012	50.0	50000	0.5314	0.9097	0.9151	0.9117	0.9116	0.9097	0.9128	0.9097	0.9098	0.8864	0.9209	0.9083	0.9711	0.9058	0.9692	0.8541	0.8170	1.0	0.8732	0.8768	0.8807	0.9890

Framework versions

Transformers 4.48.3
Pytorch 2.6.0+cu124
Datasets 3.3.0
Tokenizers 0.21.0

marcelovidigal/albert-base-v2-2-contract-sections-classification-v4-50

albert-base-v2-2-contract-sections-classification-v4-50

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

marcelovidigal/
albert-base-v2-2-contract-sections-classification-v4-50