unitxt
Load any mixture of text to text data in one line of code
Decision gist · record as of 2026-08-14
Yes. Unitxt is actively maintained, has no known vulnerabilities, and offers low-friction installation. It solves a real problem—standardizing AI model evaluation across diverse data types and models—with a permissive license suitable for enterprise use. Install it if you need to benchmark models against established datasets or build reproducible evaluation workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with four runtime dependencies (datasets, evaluate, scipy, diskcache).
- Actively maintained with a recent release and no known vulnerabilities.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits commercial use, modification, and distribution with attribution; suitable for enterprise adoption.
last release 2026-05-27 (79 days) · last repo commit 2026-05-27 · 218 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 92,135 downloads/mo, #13,476 on PyPI
Alternatives
Verify before relying
pip install unitxt
from unitxt import load_dataset
dataset = load_dataset(
card="cards.gpqa.diamond",
split="test",
format="formats.chat_api",
)- Whether the catalog contains thousands of datasets as implied by 'thousands of datasets' claim in description
- Performance characteristics and memory footprint when evaluating large-scale benchmarks
- Specific model provider integrations beyond HuggingFace, OpenAI, and WatsonX mentioned
What it is and what it does
Unitxt is a Python library designed for evaluating AI model performance in a unified, modular way. It provides a large catalog of pre-built benchmarks and datasets that can be loaded and formatted for any model, supporting text, tables, vision, speech, and code evaluation. The library works model-agnostically with HuggingFace, OpenAI, WatsonX, and custom inference engines, and emphasizes reproducibility through shareable, composable components.
You use Unitxt either through its command-line interface for quick benchmark runs or programmatically by loading datasets, defining tasks with metrics, and running inference and evaluation. It handles data preparation, formatting for chat APIs, and metric computation in one workflow, reducing the boilerplate needed to benchmark models at scale.
Use it for
- Evaluate a custom LLM against standard benchmarks like MMLU Pro or GPQA without writing dataset loaders
- Run multi-task evaluation across different domains in a single command-line call
- Load and format datasets in chat API format for any model without manual preprocessing
- Build reproducible evaluation pipelines with modular, shareable task and template components
- Compare model performance across text, code, and structured data tasks using unified metrics
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Unitxt is actively maintained, has no known vulnerabilities, and offers low-friction installation. It solves a real problem—standardizing AI model evaluation across diverse data types and models—with a permissive license suitable for enterprise use. Install it if you need to benchmark models against established datasets or build reproducible evaluation workflows.
Install
unitxt on PyPI
Before you install
Low friction installation with four runtime dependencies (datasets, evaluate, scipy, diskcache). Actively maintained with a recent release and no known vulnerabilities.
License in practice
Apache License 2.0 permits commercial use, modification, and distribution with attribution; suitable for enterprise adoption.
Quickstart
pip install unitxt
from unitxt import load_dataset
dataset = load_dataset(
card="cards.gpqa.diamond",
split="test",
format="formats.chat_api",
)Verify before relying
- Whether the catalog contains thousands of datasets as implied by 'thousands of datasets' claim in description
- Performance characteristics and memory footprint when evaluating large-scale benchmarks
- Specific model provider integrations beyond HuggingFace, OpenAI, and WatsonX mentioned
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdatasetsevaluatescipydiskcache |
| Maintenance | Actively maintained 79 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 92,135 / month, #13,476 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: unitxt-1.26.10-py3-none-any.whl
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