lnccbrown / lnccbrown/LANfactory
Take stock of all models that currently can't be trained
Open
linear-lanfactory
- Dominant language
- Python
- Stars
- 16
- Forks
- 4
- Avg merge
- 3d 7h
- Merged PRs (30d)
- 8
Description
Full test run (this might need a bit of an adjustment on the tests), to run through all the models available in `ssm-simulators`.
This is to take stock of all failing models to define next steps.
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by running the full test suite across all models available in `ssm-simulators`, adjusting the tests if required to exercise them consistently. Record which models fail to train and use those failures to define the next steps; completion is a complete inventory of failing models.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100