readme: ---
  
  language: en
  
  tags:
  
  - sentence correction
  
  - text2text-generation
  
  license: cc-by-nc-sa-4.0
  
  datasets:
  
  - jfleg
  
  ---
  
  # Model
  This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the [JFLEG](https://arxiv.org/abs/1702.04066) dataset and [Happy Transformer framework](https://github.com/EricFillion/happy-transformer). This model was fine-tuned for sentence correction on normal English translations and positional English translations of local Caribbean English Creole. This model will be updated periodically as more data is compiled. For more on the Caribbean English Creole checkout the library [Caribe](https://pypi.org/project/Caribe/).
  
  ___
  
  
  # Re-training/Fine Tuning
  
  The results of fine-tuning resulted in a final accuracy of 90%
  
  
  # Usage 
  
  
  
  ```python
  
  from happytransformer import HappyTextToText, TTSettings
  
  pre_trained_model="T5"
  model = HappyTextToText(pre_trained_model, "KES/T5-KES")
  
  arguments = TTSettings(num_beams=4, min_length=1)
  sentence = "Wat iz your nam"
  
  correction = model.generate_text("grammar: "+sentence, args=arguments)
  if(correction.text.find(" .")):
      correction.text=correction.text.replace(" .", ".")
  
  print(correction.text) # Correction: "What is your name?".
  
  ```
  ___
  # Usage with Transformers
  
  ```python
  
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  
  tokenizer = AutoTokenizer.from_pretrained("KES/T5-KES")
  
  model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-KES")
  
  text = "I am lived with my parenmts "
  inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')
  
  output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
  correction=tokenizer.batch_decode(output, skip_special_tokens=True)
  print("".join(correction)) #Correction: I am living with my parents.
  
  ```
  ___
  