metadata

library_name: transformers
license: mit
base_model:
  - neuralmind/bert-base-portuguese-cased
tags:
  - generated_from_trainer
model-index:
  - name: congretimbau3
    results: []
datasets:
  - belisards/ementas_senado_1946_2024
  - belisards/ementas_camarabr_1934_2024
language:
  - pt

congretimbau

This model is a fine-tuned version of BERTimbau on a dataset with bills of Brazilian law proposals. It achieves the following results on the evaluation set:

eval_loss: 0.4885
eval_runtime: 798.5704
eval_samples_per_second: 169.279
eval_steps_per_second: 1.324
epoch: 2.3669
step: 10000

Training and evaluation data

Data from the Chamber of Deputies and the Federal Senate.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 300
num_epochs: 10

Framework versions

Transformers 4.45.1
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0