readme: ---
  language:
  - en
  tags:
  - grammatical error correction
  - text2text
  - t5
  license: apache-2.0
  datasets:
  - clang-8
  - conll-14
  - conll-13
  metrics:
  - f0.5
  ---
  
  This model is an implementation of the paper [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC).
  We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70).
  
  To effectively use the "Hosted inference API", write "gec: [YOUR SENTENCE HERE]".
  
  In order to use the model, look at the following snippet:
  ```python
  from transformers import T5ForConditionalGeneration, T5Tokenizer
  
  model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small")
  tokenizer = T5Tokenizer.from_pretrained('t5-small')
  
  sentence = "I like to swimming"
  tokenized_sentence = tokenizer('gec: ' + sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt')
  corrected_sentence = tokenizer.decode(
      model.generate(
          input_ids = tokenized_sentence.input_ids,
          attention_mask = tokenized_sentence.attention_mask, 
          max_length=128,
          num_beams=5,
          early_stopping=True,
      )[0],
      skip_special_tokens=True, 
      clean_up_tokenization_spaces=True
  )
  print(corrected_sentence) # -> I like swimming.
  ```