skillfed

model-index

Create a source of truth for ML model results and browse it on Papers with Code

model-index v0.1.11 234.8K downloads/30d#9,014 on PyPI36
Permissive license MIT Abandoned released

What it is and what it does

model-index is a tool for organizing and publishing machine learning model metadata in a standardized, browsable format. It lets you define model information—training parameters, results, dataset details, paper links, and code references—in a single YAML file or spread across markdown files, then optionally upload that metadata to Papers with Code for community discovery.

The package is designed around flexibility: you can store metadata however is convenient (YAML, JSON, or markdown annotations), and model-index collects it into a unified index. All fields except model name are optional, and you can add custom fields beyond the standard ones. The main use case is maintaining a source-of-truth registry for a machine learning library or research project.

Use it for:

  • Publish a structured registry of trained models in a research repository or ML library for Papers with Code discovery
  • Maintain model metadata (FLOPs, parameters, training resources, results) in version-controlled YAML alongside code
  • Embed model documentation directly in markdown files and aggregate it into a searchable index
  • Track model performance metrics and results across datasets in a standardized, comparable format
  • Link model weights, papers, and code implementations in a single queryable source of truth

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Maintains a structured index of machine learning model metadata (parameters, results, training data, paper links) stored as YAML, JSON, or markdown annotations, with optional integration to Papers with Code.

No. The package is abandoned (no commits since June 2021, no releases since March 2021) and has received no maintenance or security updates in over four years. While install friction is low and the license is permissive, the lack of active maintenance means dependency compatibility cannot be guaranteed, and any bugs or breaking changes in its runtime dependencies will go unfixed. Use only if you have an existing project already relying on it and can maintain a local fork if needed.

Install

model-index on PyPI

pip

pip install model-index

uv

uv add model-index

poetry

poetry add model-index

Installing model-index

Before you install

Low install friction with four straightforward runtime dependencies. However, the package is abandoned—last release was 0.1.11 in March 2021, with no commits since June 2021. No active maintenance or security updates should be expected.

License in practice

MIT license permits unrestricted use, modification, and distribution with minimal restrictions, making it suitable for both open and proprietary projects.

Quickstart

pip install model-index

from model_index import ModelIndex
index = ModelIndex.from_file('model-index.yml')

Verify before relying

  • Whether the package still works with current versions of its runtime dependencies (pyyaml, markdown, ordered-set, click)
  • Whether Papers with Code still accepts model-index submissions and whether the integration remains functional
  • Python version compatibility—requires_python is unspecified in the fact sheet

Package facts

License MIT (permissive)
Python support not specified
Install friction low — pure-Python wheel
Runtime dependencies 4 — pyyaml, markdown, ordered-set, click
Maintenance abandoned — 1,971 days since the last release
Last repo commit
First released
Downloads 234,800/month — #9,014 on PyPI (30-day window, as of 2026-08-14)
Known vulnerabilities none known (OSV.dev, checked 2026-08-14)

Evidence: model_index-0.1.11-py3-none-any.whl

Tags

ml model metadata indexmachine learning model registrymodel documentation yamlpapers with code integrationmodel results trackingml model source of truthmodel metadata management
ml-metadatamodel-registryabandoned

More Artificial Intelligence packages