Handle NER usecase #263
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Handle NER usecase #263
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Dref360
commented
Jul 21, 2023
| @@ -79,7 +79,7 @@ def predict_on_dataset_generator( | |||
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| out = map_on_tensor(lambda o: o.view([iterations, -1, *o.size()[1:]]), out) | |||
| out = map_on_tensor(lambda o: o.permute(1, 2, *range(3, o.ndimension()), 0), out) | |||
| out = map_on_tensor(lambda o: o.permute(1, *range(3, o.ndimension()), 2, 0), out) | |||
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This also makes the result actually match the documentation
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Slight modification to handle NER.
NER pipeline outputs [?, Sequence, NClass], but we expect [?, NClass, Sequence]. So I changed that in
predict_on_dataset_generator.