Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval
Why this work is in the frame
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Bibliographic record
Abstract
Recent rapid advances in deep pre-trained language models and the introduction of large datasets have powered research in embedding-based neural retrieval. While many excellent research papers have emerged, most of them come with their own implementations, which are typically optimized for some particular research goals instead of efficiency or code organization. In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. Tevatron enables model training and evaluation for a variety of ranking components such as dense retrievers, sparse retrievers, and rerankers. It also provides a standardized pipeline that includes text processing, model training, corpus/query encoding, and search. In addition, Tevatron incorporates well-studied methods for improving retriever effectiveness such as hard negative mining and knowledge distillation. We provide an overview of Tevatron in this paper, demonstrating its effectiveness and efficiency on multiple IR and QA datasets. We highlight Tevatron's flexible design, which enables easy generalization across datasets, model architectures, and accelerator platforms (GPUs and TPUs). Overall, we believe that Tevatron can serve as a solid software foundation for research on neural retrieval systems, including their design, modeling, and optimization.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it