Integrated Reproducibility with Self-describing Machine Learning Models
Bibliographic record
Abstract
Researchers and data scientists frequently want to collaborate on machine learning models. However, in the presence of sharing and simultaneous experimentation, it is challenging both to determine if two models were trained identically and to reproduce precisely someone else’s training process. We demonstrate how provenance collection that is tightly integrated into a machine learning library facilitates reproducibility. We present MERIT, a reproducibility system that leverages a robust configuration system and extensive provenance collection to exactly reproduce models, given only a model object. We integrate MERIT with Tribuo, an open-source Java-based machine learning library. Key features of this integrated reproducibility framework include controlling for sources of non-determinism in a multi-threaded environment and exposing the training differences between two models in a human-readable form. Our system allows simple reproduction of deployed Tribuo models without any additional information, ensuring data science research is reproducible. Our framework is open-source and available under an Apache 2.0 license.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".