readme: ---
  license: mit
  widget:
    - text: "Demanda por fundos de <mask> para crianças cresce em 2022"
      example_title: "Exemplo 1"
    - text: "Havia uma <mask> no meio do caminho"
      example_title: "Exemplo 2"
    - text: "Na verdade, começar a <mask> cedo é ideal para ter um bom dinheiro no futuro"
      example_title: "Exemplo 3"
    - text: "Mitos e verdades sobre o <mask>. Doença que mais mata mulheres no Brasil."
      example_title: "Exemplo 4"
  model-index:
  - name: tgf-xlm-roberta-base-pt-br
    results: []
  ---
  
  # tgf-xlm-roberta-base-pt-br
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the [BrWac](https://huggingface.co/datasets/thegoodfellas/brwac_tiny) dataset.
  
  ## Model description
  
  This is a fine-tuned version of the Brazilian Portuguese language. It was trained using the [BrWac](https://huggingface.co/datasets/thegoodfellas/brwac_tiny) dataset and followed the principles from [Roberta's paper](https://arxiv.org/abs/1907.11692). The key strategies are:
  
  1. *Full-Sentences*: Quoted from the paper: "Each input is packed with full sentences sampled contiguously from one or more documents, such that the total length is at most 512 tokens. Inputs may cross document boundaries. When we reach the end of one document, we begin sampling sentences from the next document and add an extra separator token between documents".
  
  2. Tunned hyperparameters: adam_beta1=0.9, adam_beta2=0.98, adam_epsilon=1e-6 (as paper suggests)
  
  
  ## Availability
  
  The source code is available [here](https://github.com/the-good-fellas/xlm-roberta-pt-br)
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-4
  - train_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 512
  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.11.0a0+b6df043
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  
  ### Environment
  
  4xA100.88V NVIDIA
  
  Special thanks to [DataCrunch.io](https://datacrunch.io) with their amazing, and affordable GPUs.
  <img src="https://datacrunch.io/_next/static/media/Logo.6b773500.svg"  width="20%"/>
  