Comments (6)
Yeah. We will pass an empty vec to dataloader_test. I haven't test that, so I passed full data to dataloader_test like the example provided in burn when developing. Please feel free to remove it.
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Sorry, one more question: is config.seed supposed to be random in actual use (outside of tests)?
I recommend making it fixed. Because I don't want the user complain that the optimized parameters are always different over each training.
Is requestedRetention relevant for the training process, or something that only applies after the weights have been generated?
The requestedRetention is unrelated for the training. Users should set it by themselves.
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Test dataset will be empty in real optimization for users. FSRS will all data in the training.
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Sorry, I may need an ELI5 here :-) Are you saying that when users train their real data, we'll pass in the full vec of FSRS items to dataloader_train, and an empty vec to dataloader_test? What advantages are there in passing in the full data to dataloader_test when developing?
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Sorry, one more question: is config.seed supposed to be random in actual use (outside of tests)?
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And one more :-) Is requestedRetention relevant for the training process, or something that only applies after the weights have been generated?
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Related Issues (20)
- [Enhancement] Use more splits while training with larger datasets HOT 3
- Request: Ignore reviews before "Forget" HOT 9
- Enhancement: Include incomplete revlogs even when training HOT 4
- Consider time-frame limitation? HOT 3
- TODO: speed up finding optimal retention via Brent's method
- Better outlier filter for trainset HOT 25
- Skip reviews with time = 0 when calculating average answer times HOT 1
- What's the difference between this repo and rs-fsrs? HOT 1
- User guide HOT 3
- Add an option to turn off outlier filter when benchmark HOT 1
- Inference.rs uses the new power curve, but the default parameters are from v4 HOT 17
- Add a example file HOT 4
- Reference usage? HOT 5
- Pre-training Only when the number of reviews is less than 1000 HOT 5
- [BUG] Potential inconsistency in optimal_retention.rs HOT 20
- [Question] How to choose "Days to simulate"? HOT 14
- [Feature Request[ Use two different sets of initial parameters, then average out the results HOT 4
- Use the first revlog in the "known" review history for converting SM-2 ivl & ease to memory states HOT 13
- Achieve parity with the Python optimizer HOT 10
- support WASM HOT 4
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