This model utilises T5-base pre-trained model. It was fine tuned using a modified version of the JFLEG dataset and Happy Transformer framework. 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.
The results of fine-tuning resulted in a final accuracy of 90%
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?".
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.
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