Instructions to use KAUSTAcademy/RecipeMatching_VerifiedUncapped_Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use KAUSTAcademy/RecipeMatching_VerifiedUncapped_Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("KAUSTAcademy/RecipeMatching_VerifiedUncapped_Qwen3-Embedding-0.6B") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
RecipeMatching uncapped verified arm (Qwen3-Embedding-0.6B)
A checkpoint from the paper Recipe-Matching, Not Equivalence (Ali Habibullah, Mohammad Alshiekh, Yazan Alshoibi, Salman Khan and Naeemullah Khan, 2026), shared so the paper's evaluations of this model can be re-run without retraining. It is not one of the three models the paper releases as its main artefacts. Code, data and results: https://github.com/KAUST-Academy/recipe-matching-not-equivalence.
What it is. The paper's uncapped verified arm: Qwen3-Embedding-0.6B fine-tuned (all weights), with no row cap, on the 13,747 rows of data/cas_pairs/pairs.jsonl that pass the contamination gates (the file holds 14,361), the same computer-algebra-verified pairs the verified arm samples from. It is also the fine-tuned parent of the paper's model soups. Seed 42; the full training record, command line included, is
run_config.json in this repository, and every training file named here is in the GitHub repository.
Results of this checkpoint (seed 42, R@1 / R@5 / R@10; a single run; these are the numbers the paper reports):
| Evaluation | R@1 | R@5 | R@10 |
|---|---|---|---|
| MathNet-Retrieve easy tier (15,000 queries, 117,088 documents) | 15.92 | 78.18 | 87.21 |
| MathNet-Retrieve medium tier | 3.86 | 40.80 | 55.92 |
| MathNet-Retrieve hard tier | 0.16 | 6.59 | 19.47 |
| Cross-language duplicates (strict) | 47.58 | 70.99 | 79.13 |
Usage. Encode queries with the model's query prompt, the setting every number above uses.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("KAUSTAcademy/RecipeMatching_VerifiedUncapped_Qwen3-Embedding-0.6B")
q = model.encode(["Find all real x with x^2 = 2x."], prompt_name="query")
d = model.encode(["Determine every real solution of x^2 - 2x = 0."])
print(model.similarity(q, d))
Citation
@misc{habibullah2026recipematchingequivalence,
title={Recipe-Matching, Not Equivalence},
author={Ali Habibullah and Mohammad Alshiekh and Yazan Alshoibi and Salman Khan and Naeemullah Khan},
year={2026},
eprint={2609.31927},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2609.31927},
}
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