Update README.md

README.md

@@ -8,3 +8,21 @@ This is a model for word-based spell correction tasks. This model is generated b
 This model works best for word-based spell correction(not so good with the sequence of words).
 
 
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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("veghar/spell_correct_bart_base")
+model = AutoModelForSeq2SeqLM.from_pretrained("veghar/spell_correct_bart_base")
+text='believ'
+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)
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+print('Misspelled word:', text)
+print('Corrected word:', corrected_sentences)
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+>Misspelled word: believ
+>Corrected word: ['believe', 'belief', 'believer']