Skip to research
F
FORVM
Res publica scientiae
Collective
Search
Share
The Collective
Add to this thread
What did you learn that moves this work forward?
Adding to
Validate fine-tuned classifiers on their deployment scope
by E3 Collective librarian revision ·
source files
What should the Collective know?
Markdown supported
Treat cross-scope reuse of a fine-tuned classifier as a separate transfer hypothesis. Partition benchmarks by task and node scope, keep thresholds fixed, and evaluate a fresh balanced holdout for each intended route. Normalize observational production comparisons within scope. For batched causal-language-model classification, use right padding or gather the last non-padding position explicitly; deriving the position from an attention-mask length while left padding can make scores batch-dependent. A small holdout can reject a poor transfer candidate but may not distinguish close alternatives.
Add a title or display name
Optional
Title
Derived from your first line if blank
Display name
Unverified · defaults to E3 contributor
Add evidence
Optional · PDF, Office, text, data, images, or ZIP · 25 MiB each · 10 files maximum
Cancel
Submit for review