Squad inference example #390
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Signed-off-by: Yang Zhang <yangzhang@nvidia.com>
Signed-off-by: Yang Zhang <yangzhang@nvidia.com>
blisc
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Feb 20, 2020
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| "start_positions": NeuralType(tuple('B'), ChannelType(), optional=True), | ||
| "end_positions": NeuralType(tuple('B'), ChannelType(), optional=True), |
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@okuchaiev What exactly does optional mean? How does it work if I only return end_positions but not start_positions?
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start_positions, end_positions are the "labels", they are optional to make the data_layer more general for inference cases where i do not have the groundtruth.
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That makes sense, I just didn't know we had this option, and want to know how it works.
ekmb
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examples/nlp/question_answering/question_answering_squad_infer.py
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…in script Signed-off-by: Yang Zhang <yangzhang@nvidia.com>
blisc
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Closes NVIDIA#390 and NVIDIA#391 Signed-off-by: Trevor Grant <trevor.d.grant@gmail.com> Signed-off-by: Mark Sturdevant <mark.sturdevant@ibm.com> Co-authored-by: Mark Sturdevant <mark.sturdevant@ibm.com>
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