Du bon usage d'ingrédients linguistiques spéciaux pour classer des recettes exceptionnelles
Bibliographic record
Abstract
Nous présentons un modèle d’apprentissage automatique qui combine modèles neuronaux et linguistiques pour traiter les tâches de classification dans lesquelles la distribution des étiquettes des instances est déséquilibrée. Les performances de ce modèle sont mesurées à l’aide d’expériences menées sur les tâches de classification de recettes de cuisine de la campagne DEFT 2013 (Grouin et al., 2013). Nous montrons que les plongements lexicaux (word embeddings) associés à des méthodes d’apprentissage profond obtiennent de meilleures performances que tous les algorithmes déployés lors de la campagne DEFT. Nous montrons aussi que ces mêmes classifieurs avec plongements lexicaux peuvent gagner en performance lorsqu’un modèle linguistique est ajouté au modèle neuronal. Nous observons que l’ajout d’un modèle linguistique au modèle neuronal améliore les performances de classification sur les classes rares.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".