This model utilises T5-base pre-trained model. It was fine tuned using a custom dataset This model was fine-tuned for capitalisation on text that includes multiple sentences or questions.
Interested in Caribbean Creole? Checkout the library Caribe for more info and future updates.
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KES/caribe-capitalise")
model = AutoModelForSeq2SeqLM.from_pretrained("KES/caribe-capitalise")
text = "john is a boy. he is 12 years old. his sister's name is Joy."
inputs = tokenizer("text:"+text, truncation=True, return_tensors='pt')
output = model.generate(inputs['input_ids'], num_beams=4, max_length=512, early_stopping=True)
capitalised_text=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(capitalised_text)) #Capitalised Output: John is a boy. He is 12 years old. His sister's name is Joy.
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