EnglishVoice/t5-base-us-to-uk-english

About the model

The model has been trained on a dataset containing 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/

Sample code

Sample Python code:

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.