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This is a model for word-based spell correction tasks. This model is generated by fine-tuning bart base model.

This model works best for ''WORD-BASED'' spell correction(not so good with the sequence of words).

How to Get Started with the Model

from transformers import AutoTokenizer, TFBartForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("veghar/spell_correct_bart_base")
model = TFBartForConditionalGeneration.from_pretrained("veghar/spell_correct_bart_base")

text_tok=tokenizer(text,padding=True, return_tensors='tf')
input_ids = text_tok['input_ids']
outputs = model.generate(input_ids=input_ids, max_length=10,num_return_sequences=3)
corrected_sentences = tokenizer.batch_decode(outputs, skip_special_tokens=True)

print('Misspelled word:', text)
print('Corrected word:', corrected_sentences)

>>Misspelled word: believ
>>Corrected word: ['believe', 'belief', 'believer']
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