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--- |
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library_name: transformers |
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license: mit |
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base_model: |
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- neuralmind/bert-base-portuguese-cased |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: congretimbau3 |
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results: [] |
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datasets: |
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- belisards/ementas_senado_1946_2024 |
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- belisards/ementas_camarabr_1934_2024 |
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language: |
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- pt |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# congretimbau3 |
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This model is a fine-tuned version of [BERTimbau](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on a dataset with bill of Brazilian law proposals. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.4885 |
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- eval_runtime: 798.5704 |
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- eval_samples_per_second: 169.279 |
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- eval_steps_per_second: 1.324 |
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- epoch: 2.3669 |
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- step: 10000 |
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## Model description |
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The model is used to classify law bills into one of the following categories: |
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- Violência contra a mulher |
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- Direitos sociais |
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- Maternidade |
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- Direitos sexuais e reprodutivos |
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- Dignidade sexual |
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- Economia |
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- Gênero |
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- Política |
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- Feminicídio |
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## Intended uses & limitations |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 300 |
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- num_epochs: 10 |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
