Comments (3)
Bortrex
commented on July 20, 2024
1
Also, in #2341 it was shown that the NLTK lib has some issues, related to the SmoothingFunction() and therefore received an update to fix it.
Hence, it is no longer possible to achieved the same results.
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guxd
commented on July 20, 2024
Thanks for pointing out.
There seems to be a big deviation to the original results since recently.
Somebody reported better results than that reported in the paper for the DailyDial dataset.
We are not sure whether it is due to any change of environment other than those written in the "requirements.txt". We are figuring it out and will let you know.
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bsantraigi
commented on July 20, 2024
Ok, thanks. Although we were using an environment as per the requirements.txt only. Also like you said, we also noticed quite a bit of variance between different runs. (Even when the seed is given as an argument)
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Related Issues (11)
- Stop training the context during train_G/train_D? HOT 3
- Code explanation HOT 3
- SWDA seems to give lot of repetition in the sample responses for the test data. HOT 4
- Wasserstein distance between prior and posterior HOT 3
- Confused about the evaluation of inter-dist metrics. HOT 4
- Warning when run sample.py：RNN module weights are not part of single contiguous chunk of memory. HOT 3
- seems the loss of both generator and discriminator would collapse? HOT 1
- Could I apply for your pretrained model in DailyDialog?
- It is reasonable if I set n_samples to 1 when I run the sample.py HOT 2
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