Evaluating Code Models
Skill AktivEvaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
To provide a standardized and reproducible method for evaluating and benchmarking code generation models using industry-standard datasets and metrics.
Funktionen
- Evaluates code generation models
- Supports HumanEval, MBPP, MultiPL-E, and 15+ benchmarks
- Measures pass@k metrics
- Provides multi-language evaluation
- Includes instruction-tuned model evaluation
Anwendungsfälle
- Benchmarking code generation models
- Comparing the coding abilities of different models
- Testing multi-language code generation support
- Measuring the functional correctness and quality of generated code
Nicht-Ziele
- General LLM benchmarking (e.g., MMLU, GSM8K)
- Real-world issue resolution (e.g., SWE-bench)
- Code understanding tasks (e.g., CodeXGLUE)
- Evaluating code's efficiency without functional correctness
Trust
- warning:Issues AttentionWith 17 issues opened and 4 closed in the last 90 days, the closure rate is below 50%, indicating slower attention to issues.
Installation
npx skills add davila7/claude-code-templatesFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
Vertrauenssignale
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