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kmkurn / pytorch-crf

(Linear-chain) Conditional random field in PyTorch.
https://pytorch-crf.readthedocs.io
MIT License
935 stars 151 forks source link

Torch 1.1.0 Support? #39

Closed timonbimon closed 4 years ago

timonbimon commented 5 years ago

Hey :) Are there any plans to support PyTorch 1.1 any time soon?

kmkurn commented 5 years ago

Hi, I'd love to support PyTorch 1.1 but unfortunately my schedule has been quite full for some time now so I won't be able to do it anytime soon :(

timonbimon commented 5 years ago

Ok thanks for the reply and thanks for contributing this in the first place :)

kmkurn commented 4 years ago

Hi, I just realised that I've put PyTorch 1.1 as a version to test against in travis. Could you check if it indeed works for PyTorch 1.1?

nlpersimon commented 4 years ago

Hi, I just realised that I've put PyTorch 1.1 as a version to test against in travis. Could you check if it indeed works for PyTorch 1.1?

Hi! Thanks for your awesome work. It indeed works for PyTorch 1.1. When I used default initial value of CRF, the performance didn't improve, and then I derived transition matrix from training set, making it be the initial value of CRF, the validation loss almost reduced by half. I am curious about this weird results. Do you have any idea ? Thanks again!

kmkurn commented 4 years ago

Great! I can close this issue now.

Wow that's impressive. May I know how you exactly derived the transition matrix from the training set?

nlpersimon commented 4 years ago

I just maintained a matrix (number of tags x number of tags), and went through the tags in training set. It's to count the frequency of transition from tag[i] to tag[j]. Finally, each row of the matrix would be normalized by sum of each them.

Kemal Kurniawan notifications@github.com 於 2019年8月28日 週三 上午4:46寫道：

Great! I can close this issue now.

Wow that's impressive. May I know how you exactly derived the transition matrix from the training set?

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kmkurn commented 4 years ago

I see. I'm not sure why that works nor why that shouldn't work. But thanks for sharing. Hopefully that helps others too.