ModernBERT-base-2-contract-sections-classification-v4-10-max

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: 0.4801
Accuracy Evaluate: 0.917
Precision Evaluate: 0.9221
Recall Evaluate: 0.9209
F1 Evaluate: 0.9210
Accuracy Sklearn: 0.917
Precision Sklearn: 0.9173
Recall Sklearn: 0.917
F1 Sklearn: 0.9166
Acuracia Rotulo Objeto: 0.9649
Acuracia Rotulo Obrigacoes: 0.8838
Acuracia Rotulo Valor: 0.8166
Acuracia Rotulo Vigencia: 0.9816
Acuracia Rotulo Rescisao: 0.9474
Acuracia Rotulo Foro: 0.9385
Acuracia Rotulo Reajuste: 0.9039
Acuracia Rotulo Fiscalizacao: 0.8423
Acuracia Rotulo Publicacao: 0.9951
Acuracia Rotulo Pagamento: 0.8913
Acuracia Rotulo Casos Omissos: 0.9015
Acuracia Rotulo Sancoes: 0.9266
Acuracia Rotulo Dotacao Orcamentaria: 0.9780

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

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
2.3971	1.0	2000	0.8286	0.7678	0.8292	0.7831	0.7914	0.7678	0.8077	0.7678	0.7695	0.9504	0.6734	0.5244	0.6115	0.8975	0.8808	0.6512	0.7382	0.9113	0.7754	0.8227	0.7982	0.9451
1.3917	2.0	4000	0.6639	0.8558	0.8777	0.8708	0.8702	0.8558	0.8673	0.8558	0.8557	0.9587	0.7357	0.6619	0.8976	0.9030	0.9231	0.8719	0.7918	0.9655	0.8587	0.8867	0.8991	0.9670
0.887	3.0	6000	0.5830	0.874	0.8789	0.8885	0.8804	0.874	0.8812	0.874	0.8743	0.9442	0.7761	0.7421	0.9081	0.8615	0.9346	0.9004	0.8265	0.9852	0.8841	0.9015	0.9083	0.9780
0.9516	4.0	8000	0.5632	0.8885	0.8987	0.8992	0.8979	0.8885	0.8899	0.8885	0.8881	0.9194	0.8081	0.7736	0.9554	0.9474	0.9385	0.8719	0.8297	0.9803	0.8877	0.8818	0.9174	0.9780
0.7614	5.0	10000	0.5290	0.8998	0.9083	0.9102	0.9084	0.8998	0.9021	0.8998	0.8998	0.9628	0.8064	0.8052	0.9370	0.9418	0.9846	0.8897	0.8328	0.9951	0.8804	0.9015	0.9174	0.9780
0.6291	6.0	12000	0.5590	0.8978	0.9029	0.9089	0.9044	0.8978	0.9009	0.8978	0.8975	0.9421	0.7946	0.7880	0.9764	0.9391	0.9385	0.9075	0.8454	0.9951	0.8913	0.9064	0.9083	0.9835
0.4869	7.0	14000	0.4875	0.9103	0.9140	0.9156	0.9143	0.9103	0.9102	0.9103	0.9096	0.9587	0.8737	0.7966	0.9790	0.9446	0.9346	0.9039	0.8139	0.9951	0.8913	0.9064	0.9266	0.9780
0.5691	8.0	16000	0.4929	0.9083	0.9119	0.9162	0.9134	0.9083	0.9093	0.9083	0.9079	0.9607	0.8232	0.8195	0.9843	0.9446	0.9385	0.8932	0.8580	0.9951	0.8913	0.9064	0.9174	0.9780
0.3831	9.0	18000	0.4892	0.9153	0.9184	0.9198	0.9186	0.9153	0.9157	0.9153	0.9149	0.9649	0.8737	0.8138	0.9843	0.9446	0.9385	0.8968	0.8486	0.9951	0.8913	0.9015	0.9266	0.9780
0.3	10.0	20000	0.4801	0.917	0.9221	0.9209	0.9210	0.917	0.9173	0.917	0.9166	0.9649	0.8838	0.8166	0.9816	0.9474	0.9385	0.9039	0.8423	0.9951	0.8913	0.9015	0.9266	0.9780

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-v4-10-max

ModernBERT-base-2-contract-sections-classification-v4-10-max

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Seed this model