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: 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
0.4789	1.0	1000	0.8779	0.7163	0.7889	0.7364	0.7408	0.7163	0.7689	0.7163	0.7179	0.9050	0.6313	0.4269	0.7690	0.8726	0.8269	0.3345	0.5741	0.8670	0.7065	0.8276	0.9358	0.8956
0.2075	2.0	2000	0.5023	0.8558	0.8682	0.8744	0.8685	0.8558	0.8605	0.8558	0.8546	0.8822	0.7424	0.6361	0.9423	0.9086	0.9885	0.8683	0.7666	0.9606	0.9203	0.8818	0.9083	0.9615
0.1423	3.0	3000	0.4114	0.884	0.8792	0.8990	0.8856	0.884	0.8884	0.884	0.8840	0.8822	0.8081	0.7507	0.9475	0.8837	0.9885	0.9502	0.7981	0.9754	0.9275	0.8867	0.9266	0.9615
0.1048	4.0	4000	0.3871	0.8925	0.8996	0.9083	0.9020	0.8925	0.8961	0.8925	0.8919	0.9008	0.7677	0.7622	0.9554	0.9695	0.9962	0.9181	0.8265	0.9754	0.9493	0.8916	0.9174	0.9780
0.0871	5.0	5000	0.3639	0.9028	0.9077	0.9161	0.9104	0.9028	0.9067	0.9028	0.9028	0.9587	0.7694	0.8539	0.9396	0.9335	0.9923	0.9609	0.8265	0.9901	0.8877	0.9015	0.9174	0.9780
0.0604	6.0	6000	0.3634	0.908	0.9105	0.9223	0.9148	0.908	0.9123	0.908	0.9080	0.9339	0.7626	0.8682	0.9711	0.9280	0.9923	0.9644	0.8549	0.9852	0.9384	0.9015	0.9174	0.9725
0.0611	7.0	7000	0.3359	0.9125	0.9202	0.9259	0.9216	0.9125	0.9160	0.9125	0.9123	0.9483	0.7845	0.8166	0.9685	0.9612	0.9885	0.9644	0.8580	0.9901	0.9457	0.8966	0.9358	0.9780
0.0514	8.0	8000	0.3315	0.92	0.9235	0.9299	0.9258	0.92	0.9222	0.92	0.9198	0.9628	0.8215	0.8395	0.9659	0.9612	0.9923	0.9466	0.8549	0.9901	0.9384	0.9015	0.9358	0.9780
0.0398	9.0	9000	0.3208	0.9187	0.9204	0.9290	0.9235	0.9187	0.9214	0.9187	0.9187	0.9711	0.8199	0.8567	0.9528	0.9335	0.9923	0.9680	0.8486	0.9901	0.9239	0.9064	0.9358	0.9780
0.0396	10.0	10000	0.3151	0.9207	0.9230	0.9313	0.9262	0.9207	0.9228	0.9207	0.9206	0.9669	0.8199	0.8567	0.9580	0.9446	0.9885	0.9680	0.8486	0.9901	0.9348	0.9113	0.9358	0.9835

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-v4-10-512

ModernBERT-base-2-contract-sections-classification-v4-10-512

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

marcelovidigal/
ModernBERT-base-2-contract-sections-classification-v4-10-512