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Hyperparameter of the number of distractor documents and the ratio of golden documents in training RAFT #325
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Hi, the optimal value for the P% hyperparameter may vary from dataset to dataset. To answer your question, the paper used various values of P%, not just the default value of 1.0 in the script. |
ShishirPatil
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Change `p` which dictates the fraction of dataset with golden documents in them (vs) no golden documents. So, p = 0.8 means, for 80% of the train data set, `A* = Q + D* + D1 .. Dn` and for 20% of the train data set `A* = Q + D1 .. Dn` where `D*` are/is the golden document with the answer `A*`. Close #325
devanshamin
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Change `p` which dictates the fraction of dataset with golden documents in them (vs) no golden documents. So, p = 0.8 means, for 80% of the train data set, `A* = Q + D* + D1 .. Dn` and for 20% of the train data set `A* = Q + D1 .. Dn` where `D*` are/is the golden document with the answer `A*`. Close ShishirPatil#325
aw632
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Change `p` which dictates the fraction of dataset with golden documents in them (vs) no golden documents. So, p = 0.8 means, for 80% of the train data set, `A* = Q + D* + D1 .. Dn` and for 20% of the train data set `A* = Q + D1 .. Dn` where `D*` are/is the golden document with the answer `A*`. Close ShishirPatil#325
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Hi, I just wonder what the number of distractor documents and the ratio of golden documents in training RAFT are in producing the results in the paper. I saw the default ratio in the script the ratio is 1.0, which means no distractor documents are used. I wonder whether the default number in the scripts represents the hyperparameter used to produce the results in the paper?
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