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--- |
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license: mit |
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widget: |
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- text: "Demanda por fundos de <mask> para crianças cresce em 2022" |
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example_title: "Exemplo 1" |
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- text: "Havia uma <mask> no meio do caminho" |
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example_title: "Exemplo 2" |
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- text: "Na verdade, começar a <mask> cedo é ideal para ter um bom dinheiro no futuro" |
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example_title: "Exemplo 3" |
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- text: "Mitos e verdades sobre o <mask>. Doença que mais mata mulheres no Brasil." |
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example_title: "Exemplo 4" |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: tgf-xlm-roberta-base-pt-br |
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results: [] |
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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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# tgf-xlm-roberta-base-pt-br |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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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: 0.0001 |
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- train_batch_size: 64 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 512 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.23.1 |
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- Pytorch 1.11.0a0+b6df043 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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