google-research / google-research/FLAN
Could you share the training loss to improve reproducibility?
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 1.6k
- Forks
- 160
- PR merge metrics
- No merged PRs in 30d
Description
Hi, thanks for sharing the datasets! I'm trying to train a flan model using t5 and other backbone models. However i'm not confident enough on how well I reproduced your results. Specifically I got much lower MMLU scores. Could you please share the training loss curve (or simply the loss at convergence?) Below is mine:
I was using similar settings (batch size = 80, max_seq_len = 2300)
The final loss is around 0.6 after smoothing. What about the official values?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is named; start by checking the repository and issue thread for recorded training metrics or reproducibility notes. Done would mean documenting the official training-loss curve or convergence loss, with enough settings to compare it to the reported batch size and sequence length.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100