JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
Paper
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1702.04066
•
Published
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Trinidad English Creole Parser
This model was trained as a parser to Trinidad English Creole.
Model
This model utilises T5-base pre-trained model. It was fine tuned using a combination of a custom dataset and creolised JFLEG dataset. JFLEG dataset was creolised using the file encoding feature of the Caribe library. For more on Caribbean Creole checkout the library Caribe.
Usage with Transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("KES/T5-TTParser")
model = AutoModelForSeq2SeqLM.from_pretrained("KES/T5-TTParser")
txt = "Ah have live with mi paremnts en London"
inputs = tokenizer("grammar:"+txt, 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: Ah live with meh parents in London.
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