# google-deepmind/code_contests

Repository: https://github.com/google-deepmind/code_contests
Canonical: https://ross.abutalabs.com/products/code_contests
Language: C++
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2023-10-03T13:17:45+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1675, "days_push": 1065, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2202, forks 225 (observed 2026-08-28T04:06:25.683445+00:00)

## What it is
CodeContests is a competitive programming dataset from DeepMind used to train AlphaCode. It contains programming problems with test cases plus correct and incorrect human solutions from Codeforces, AtCoder, CodeChef, HackerEarth, and Aizu.

## Use cases
- train a model to solve competitive programming problems
- get a dataset of coding problems with test cases
- evaluate code generation models on programming contests
- research dataset for program synthesis
- benchmark LLM code generation

## When to choose
- training or evaluating code generation models on contest problems
- you need problems paired with inputs/outputs and reference solutions
- replicating AlphaCode-style research

## When to avoid
- you need a general-purpose code dataset like web code
- you need a maintained library rather than a static dataset
- you need Windows/macOS support out of the box

## Facets
- artifact type: dataset
- maturity: maintenance
- function: machine-learning, data-science, parser
- domain: machine-learning, artificial-intelligence, large-language-models, education
- platform: python, cpp
- tags: competitive-programming, code-generation, alphacode, riegeli, dataset, linux

## Member repositories
- google-deepmind/code_contests (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:25.683445+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:47:02.294903+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/code_contests (fetched 2026-08-28T04:06:25.683445+00:00, sha 4df8ce55feb9)
- Data as of 2026-08-30T08:39:29.467469+00:00.