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Releases: ksjae/KoGPT

HF Transformers compatible KoGPT2

08 Mar 00:53
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Someone asked me to make a Transformers-compatible version of KoGPT2.
Well, here you go.

Sorry this is not a full version, couldn't get Transformers to train properly (ugh)

https://nas.ksjit.com:5001/sharing/AcmROoopD

If link is down, don't hesitate to email me. It is likely that I may not know.

Version 0.2 final release

27 Oct 03:07
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Will not be updated until I get my hands on more data.
Couldn't make the 2.7B model on TPUs because OOM.

Available from my server

v0.2 for SNS article generation

02 Oct 04:31
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Do note that this model was originally for book content generation, but experiments show this model is more suited for SNS content generation.

Available from my server and Google Drive

v0.2 optimized for plain written text

25 Sep 12:10
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All releases will be available from my server. If Google drive gives too much hassle(e.g. headless linux), just wget from there.

v0.2-news is not trained on the book dataset(any dataset incl. books) but everything else.
Trained on v3-8 TPUs for 5 days(about 205K steps)

My server : here
Google Drive : uploading

From this release, I'm switching to home-brew code. This makes the model unable to be loaded in HF Transformers. I'll make a pull request later this year(lots of paperwork involved).
For demo/generation, check out the train code(ksjae/KoGPT2-train).

1.5B is finally here!

22 Sep 06:39
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1.5B is finally here! Pre-release
Pre-release

All releases will be available from my server. If Google drive gives too much hassle(e.g. headless linux), just wget from there.

v0.2-beta is only trained on ~700MB web corpus, on v3-8 TPUs for 36 hours(about 80K steps)

My server : here
Google Drive : here

From this release, I'm switching to home-brew code. This makes the model unable to be loaded in HF Transformers. I'll make a pull request later this year(lots of paperwork involved).
For demo/generation, check out the train code

v0.1-beta

15 Sep 06:33
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Now Includes KNLI-WEB & KNLI-WRITTEN corpus!

Known Issues
Using very short prompts (e.g. "안녕하세요.") may output parts of original corpus.

Available from Google Storage:
gs://kogpt2/code/transformers/kogptN1
or This link

KoGPT2-large

04 Sep 02:23
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KoGPT2-large trained on

  1. Kookmin Uni. Dataset
  2. Sejong corpus
  3. Namuwiki dump

Trained for 3 weeks on 2*V100

image
Sorry for that. Get it from Google drive