Comments (4)
localminimum
commented on August 23, 2024
Hi @nehaboob , as far as I'm concerned, there shouldn't be uncertainty at inference time. Could you explain in detail what the issue is? Are the questions exactly identical? (Not that the lower and upper case letters are treated differenty in QANet)
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nehaboob
commented on August 23, 2024
yes, questions are exactly identical, but same model is giving different predictions at different runs.
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theSage21
commented on August 23, 2024
Hmm. Dropout could be the cause of this. I'm not able to find where dropout is forced to 0 for demo.
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nehaboob
commented on August 23, 2024
Sorry, I was doing some mistake in graph initialisation which was causing different predictions.
This can be closed now.
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Related Issues (20)
- https://nlp.stanford.edu/data/glove.840B.300d.zip HOT 2
- how to predict answers for custom question and context by reusing loaded model HOT 1
- The embedding projection HOT 1
- Train Models With Macs HOT 2
- mask_logits in layer.py HOT 1
- layer normalization in layer? HOT 3
- Inference on my machine differs from other machines I tested HOT 3
- Is this snippet in prepro.py correct HOT 1
- Training stops after some time HOT 9
- how to adapt it for squad2.0 dataset? HOT 1
- This repo cannot reproduce the result of original paper HOT 2
- RuntimeError('cannot join current thread',) in <object repr() failed> HOT 1
- How do I resume training off a checkpoint? HOT 1
- how to train by changing/adding batch_size?
- Parameter setting problem
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- why did u add this kind of dropout in every residual block HOT 1
- ValueError: could not convert string to float: 'bewildered'
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