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README.md
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@@ -66,8 +66,8 @@ To sum up,my model performs nearly as well as the SOTA rule-based model evaluate
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```python
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from transformers import BartTokenizer, BartForConditionalGeneration
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tokenizer = BartTokenizer.from_pretrained("MarkS/
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model = BartForConditionalGeneration.from_pretrained("MarkS/
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input_text = "question: what day is it today? answer: Tuesday"
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input = tokenizer(input_text, return_tensors='pt')
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```python
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from transformers import BartTokenizer, BartForConditionalGeneration
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tokenizer = BartTokenizer.from_pretrained("MarkS/bart-base-qa2d")
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model = BartForConditionalGeneration.from_pretrained("MarkS/bart-base-qa2d")
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input_text = "question: what day is it today? answer: Tuesday"
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input = tokenizer(input_text, return_tensors='pt')
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