Open Source
instruct-eval
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca and Flan-T5 on held-out tasks.
Our verdict
We have not reviewed instruct-eval yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About instruct-eval
Community framework for quantitative evaluation of instruction-tuned models (e.g., Alpaca, Flan-T5) on held-out tasks. It provides a standardized benchmarking setup to measure model performance on unseen instructions.
Best for
Researchers and developers who need a simple, standardized way to evaluate instruction-tuned language models
Use cases
- Evaluate instruction-tuned models on a held-out task set
- Benchmark custom instruction-tuned models against baselines
- Compare output quality across different instruction-tuned architectures
Notes
553 stars on GitHub. Last updated 2024-03-10. Licensed Apache-2.0.
Pros
- Lightweight and focused solely on evaluation
- Open source with community support
- Provides a consistent, reproducible evaluation pipeline
Cons
- Limited to instruction-tuned models only
- May not cover all evaluation metrics needed for production
- Requires manual integration with specific model formats
Key facts
What we have checked
- GitHub stars
- 553
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Alternatives
Alternatives to instruct-eval
| Record | Type | How we know | Checked |
|---|---|---|---|
| lm-evaluation-harnessThis is an alternative to it | Open source | Named in the listing | Not dated |
| OpenAI EvalsThis is an alternative to it | Open source | Named in the listing | Not dated |
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Sources
Where this comes from
- github api via nightly crawl, recovered from git history e908f0e3f60ee9a6a9776accb9286b434cdbaebcRegistry
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Changelog
What changed
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