readme: ---
  language:
  - en
  tags:
  - text2text-generation
  - paraphrase-generation
  license: apache-2.0
  widget:
   - text: "UK to US: My favourite colour is yellow."
  ---
  
  ### About the model
  
  The model has been trained on a dataset containing [264519 sentences with UK English spelling](https://www.englishvoice.ai/p/uk-to-us/ "264519 sentences with UK English spelling"), along with their US English equivalent.
  
  The purpose of the model is to rewrite sentences from UK English to US English. It is capable not only of changing the spelling of words (such as "colour" to "color") but also changes the vocabulary appropriately (for example, "underground" to "subway", "solicitor" to "lawyer" and so on).
  
  ### Generation examples
  
  | Input | Output |
  | :------------ | :------------ |
  | My favourite colour is yellow. | My favorite color is yellow. |
  | I saw a bloke in yellow trainers at the underground station. | I saw a guy in yellow sneakers at the subway station. |
  | You could have got hurt! | You could have gotten 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-uk-to-us-english")
  tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-uk-to-us-english")
  model = model.to(device)
  
  input = "My favourite colour is yellow."
  
  text =  "UK to US: " + 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 favorite color is yellow.```
  