skip = True return # Mark the character indexes in context that are in answer is_char_in_ans = * len ( context ) for idx in range ( start_char_idx, end_char_idx ): is_char_in_ans = 1 # Tokenize context tokenized_context = tokenizer. split ()) # Find end character index of answer in context end_char_idx = start_char_idx + len ( answer ) if end_char_idx >= len ( context ): self. start_char_idx # Clean context, answer and question context = " ". skip = False def preprocess ( self ): context = self. Fine-tune BERT and learn S and T along the way.Ĭlass SquadExample : def _init_ ( self, question, context, start_char_idx, answer_text, all_answers ): self.The probability of a token being the end of the answer is computed Of the token in the last layer of BERT, followed by a softmax over all tokens. The answer is given by a dot product between S and the representation The probability of a token being the start of
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