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license: cc-by-4.0
language:
- ru
- en
task_categories:
- question-answering
- text-generation
size_categories:
- n<1K
pretty_name: ABench75
tags:
- benchmark
- evaluation
- reasoning
- cybersecurity
- math
- logic
- code
---
# ABench75
A compact bilingual (Russian + English) benchmark for instruction-following and
short-answer reasoning across five domains: **math, logic, code, cybersecurity,
general**.
- **Total tasks:** 375 (75 per domain)
- **Public split:** 300 tasks (`data/tasks_public.jsonl`)
- **Private holdout:** 75 tasks (15 per domain, not published) — for honest tracking
- **Languages:** ~59% English / ~41% Russian
- **Scoring:** regex match on the final answer (`re` field, case-insensitive)
> Canary: `ABENCH75_CANARY_8f3c1d9a`. If you find this string in a training corpus,
> the benchmark has leaked — treat contaminated scores accordingly.
## Format
Each line of `data/tasks_public.jsonl` is one JSON object:
```json
{"id": "math_ru_0", "lang": "ru", "domain": "math",
"q": "Сколько будет 74 умножить на 56? Ответь только числом.",
"re": "\\b4144\\b"}
```
- `id` — stable identifier
- `lang` — `ru` | `en`
- `domain` — `math` | `logic` | `code` | `cyber` | `general`
- `q` — the prompt
- `re` — Python regex the generated answer must match (search, ignore case)
## Usage
```bash
pip install -r requirements.txt # torch, transformers, peft (optional)
python run_eval.py --model <hf-model-or-path> --tasks data/tasks_public.jsonl
# optional: LoRA adapter + reasoning-level tag
python run_eval.py --model ./merged --adapter ./lora --effort high --batch 8
```
The runner prints overall accuracy and breakdowns by domain and language.
Scoring is intentionally simple: the answer must match `re`. Models that emit
reasoning should put the final answer in plain text (the runner strips common
`<think>…</think>` wrappers first).
## Results (public split, 300 tasks)
| model | params | overall | math | logic | code | cyber | general |
|---|---|---|---|---|---|---|---|
| Velary 1.2 Flash (medium) | 1.2B | 82.7% | 97% | 49% | 85% | 96% | 85% |
| Velary 1.2 Flash (high) | 1.2B | 81.6% | 96% | 43% | 85% | 96% | 88% |
| Velary Mini (no-think) | 0.35B | 50.7% | 32% | 39% | 67% | 55% | 61% |
Numbers are from the public 300-task split; the private 75-task holdout is reported
separately by the maintainers.
## Design notes & limitations
- Short-answer regex scoring favours direct final answers; it is not a judge for
long-form quality.
- 75 tasks per domain is enough for a quick signal, not for fine-grained ranking;
treat differences of a few points as noise.
- Categories are deliberately broad; see `data/tasks_public.jsonl` for coverage.
## Split policy
The public/private split is stratified by domain (60 public / 15 private per domain).
The private holdout is kept out of this repository to reduce contamination and to
provide a cleaner progress signal.
## License
- **Data** (`data/tasks_public.jsonl`): CC BY 4.0.
- **Code** (`run_eval.py`, tooling): MIT — see `LICENSE`. |