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@@ -41,7 +41,8 @@ We fine-tuned BERT for question answering using the Stanford Question Answering
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  4. **Answer prediction**: BERT generates a probability distribution over the tokens in the reference text for both the start and end positions of the answer. The model identifies the tokens with the highest probabilities for the start and end positions as the answer span.
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  5. **Answer extraction**: The answer is extracted by combining the tokens from the identified start to end positions.
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  # References
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  [1] https://huggingface.co/ml6team/keyphrase-extraction-kbir-inspec <br>
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  [2] https://arxiv.org/pdf/1810.04805v2.pdf <br>
 
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  4. **Answer prediction**: BERT generates a probability distribution over the tokens in the reference text for both the start and end positions of the answer. The model identifies the tokens with the highest probabilities for the start and end positions as the answer span.
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  5. **Answer extraction**: The answer is extracted by combining the tokens from the identified start to end positions.
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  # References
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  [1] https://huggingface.co/ml6team/keyphrase-extraction-kbir-inspec <br>
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  [2] https://arxiv.org/pdf/1810.04805v2.pdf <br>