readme: ---
  language:
  - en
  tags:
  - text2text-generation
  - paraphrase-generation
  license: apache-2.0
  widget:
   - text: "US  to UK: My favorite color is yellow."
  ---
  
  ### About the model
  
  The model has been trained on a dataset containing [249525  sentences with US English spelling](https://www.englishvoice.ai/p/us-to-uk/ "249525  sentences with US English spelling"), along with their UK English equivalent.
  
  The purpose of the model is to rewrite sentences from US English to UK English. It is capable not only of changing the spelling of words (such as "color" to "colour") but also changes the vocabulary appropriately (for example, "subway" to "underground", "lawyer" to "solicitor" and so on).
  
  ### Generation examples
  
  | Input | Output |
  | :------------ | :------------ |
  | My favorite color is yellow. | My favourite colour is yellow. |
  | I saw a guy in yellow sneakers at the subway station. | I saw a bloke in yellow trainers at the underground station. |
  | You could have gotten hurt! | You could have got hurt! |
  
  ### The dataset
  
  The dataset was developed by English Voice AI Labs. You can download it from our website:
  [https://www.EnglishVoice.ai/](https://www.EnglishVoice.ai/ "https://www.EnglishVoice.ai/")
  
  ### Sample code
  
  Sample Python code:
  
  ```python
  import torch
  from transformers import T5ForConditionalGeneration,T5Tokenizer
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-us-to-uk-english")
  tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-us-to-uk-english")
  model = model.to(device)
  
  input = "My favorite color is yellow."
  
  text =  "US to UK: " + input
  encoding = tokenizer.encode_plus(text, return_tensors = "pt")
  input_ids = encoding["input_ids"].to(device)
  attention_masks = encoding["attention_mask"].to(device)
  beam_outputs = model.generate(
      input_ids = input_ids,
      attention_mask = attention_masks,
      early_stopping = True,
  )
  
  result = tokenizer.decode(beam_outputs[0], skip_special_tokens=True)
  print(result)
  
  ```
  
  Output:
  
  ```My favourite colour is yellow.```
  