fine-tuning instrument
| Definition | Continuing to train an encoder on a new objective or corpus, so that its quotient changes. Chapter 11 section 11.8 and chapter 12 section 12.4. Also fine-tun. |
|---|---|
| Example | An encoder tuned on translation pairs put seven of thirteen backbone areas in one class that the general encoder kept apart. |
| Book | Data Mining as Observation, draft 0.2, commit f3914f0; entry id fine-tuning, kind instrument. |
| Status | no ledger row names this entry. Corrections: none recorded. |
| Defining equation | none |
| Assumptions and scope |
|
| Prior art | none recorded |
| Evidence | lean/DataMiningAsObservation/Contrastive.lean, lean/DataMiningAsObservation/CrossCorpusGate.lean |
| Reviewed | not yet reviewed; generated 2026-09-10 from records at the commits on the provenance page. |
Equation
none
Conditions
- Continuing to train an encoder on a new objective or corpus, so that its quotient changes. A contrastive objective pulls declared pairs together, and the cross-corpus gate asks whether the tuned encoder still separates on a corpus it never saw.
- An encoder fine-tuned on one relation is evaluated on that relation and on one it never saw, with a paired interval on each, and the within-corpus score of 0.75 to 0.955 collapsed to 0.47 to 0.55 across corpora until cross-corpus positives were added.
Conditions are curated in entries.toml rather than read
from a record.
Ledger
none
First stated
Chapter 11 section 11.8 and chapter 12 section 12.4 of Data
Mining as Observation, with the translation-trained encoder in
turboquant-pro/docs/RESULTS_multilingual_strata.md:55-90.
Measurements
none
Failures and corrections
none
Invariance envelope
none declared
Machine checked
lean/DataMiningAsObservation/Contrastive.lean,
theorems loss_nonneg, loss_eq_zero_iff,
not_both, loss_mono_margin, at
observation-data-mining f3914f0; what the check covers is stated in the
book’s appendix
C.
lean/DataMiningAsObservation/CrossCorpusGate.lean,
theorems clears_bow, clears_untrained,
not_validated_of_saturated, margin_example,
validated_comp, at observation-data-mining f3914f0; what
the check covers is stated in the book’s appendix
C.
Used in
Data Mining as Observation chapters 11, 12.
Related
contrastive objective; encoder; cross-corpus gate; leakage; paraphrase class.
See also
Book equations stated beside the entry’s terms, not defining it: 12.3, 11.1.
Ledger rows that cite the entry’s records without naming it: GO-B-Llama, GO-B-Llama-rematch, GO-B-legal (035→036).
Sources-table rows that share a record with the entry without naming it: chapter 10 section 10.4, chapter 11 section 11.8, chapter 12 section 12.4, chapter 12 section 12.5.
Status
Generated 2026-09-10 by encyclopedia/generate.py; book
at observation-data-mining f3914f0; the commit of every record is listed
in the encyclopedia’s provenance.