readme: Create README.md
  ## ByT5 Small Portuguese Product Reviews
  
  #### Model Description
  This is a finetuned version from ByT5 Small by Google for Sentimental Analysis from Product Reviews in Portuguese.
  ##### Paper: https://arxiv.org/abs/2105.13626
  
  #### Training data
  It was trained from products reviews from a Americanas.com. You can found the data here: https://github.com/HeyLucasLeao/finetuning-byt5-model.
  
  #### Training Procedure
  It was finetuned using the Trainer Class available on the Hugging Face library. For evaluation it was used accuracy, precision, recall and f1 score.
  
  ##### Learning Rate: **1e-4**
  ##### Epochs: **1**
  ##### Colab for Finetuning: https://colab.research.google.com/drive/1EChTeQkGeXi_52lClBNazHVuSNKEHN2f
  ##### Colab for Metrics: https://colab.research.google.com/drive/1o4tcsP3lpr1TobtE3Txhp9fllxPWXxlw#scrollTo=PXAoog5vQaTn
  #### Score:
  ```python
  Training Set:
  'accuracy': 0.8974239585927603,
   'f1': 0.927229848590765,
   'precision': 0.9580290812115055,
   'recall': 0.8983492356469835
  Test Set:
  'accuracy': 0.8957881282882026,
   'f1': 0.9261366030421776,
   'precision': 0.9559431131213848,
   'recall': 0.8981326359661668
  
  Validation Set:
  'accuracy': 0.8925383190163382,
   'f1': 0.9239208204149773,
   'precision': 0.9525448733710351,
   'recall': 0.8969668904839083
  ```
  
  #### Goals
  
  My true intention was totally educational, thus making available a this version of the model as a example for future proposes.
  
  How to use
  ``` python
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  import torch
  
  if torch.cuda.is_available():
      device = torch.device('cuda')
  else:
      device = torch.device('cpu')
  print(device)
  
  tokenizer = AutoTokenizer.from_pretrained("HeyLucasLeao/byt5-small-pt-product-reviews")
  model = AutoModelForSeq2SeqLM.from_pretrained("HeyLucasLeao/byt5-small-pt-product-reviews")
  model.to(device)
  
  def classificar_review(review):
    inputs = tokenizer([review], padding='max_length', truncation=True, max_length=512, return_tensors='pt')
    input_ids = inputs.input_ids.to(device)
    attention_mask = inputs.attention_mask.to(device)
    output = model.generate(input_ids, attention_mask=attention_mask)
    pred = np.argmax(output.cpu(), axis=1)
    dici = {0: 'Review Negativo', 1: 'Review Positivo'}
    return dici[pred.item()]
    
  classificar_review(review)
  ```