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Decision brief
Decision-critical facts for BIG-bench
Good fit when
- When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities.
- When your project requires tools specifically designed to measure human-like understanding of AI models beyond mere imitation.
Avoid when
- If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools.
- As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential
- Requirements:
- Python 3.5-3.8 required.; `pytest` is necessary for running automated tests.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Archived (748d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- 324 low (324 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Google. Display-only - separate from trust and ranking.
- Company
- Google·GitHub org profile·1mo
- Employees
- 47,756·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install BIG-benchSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Beyond the Imitation Game collaborative benchmark for measuring and extrapolating the capabilities of language models through various tasks and evaluation methods.
Capability facts
- Languages
- python
Source: github.language · Aug 6, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Quick start Colab notebooks
Using SeqIO to inspect and evaluate BIG-bench json tasks:
Creating new BIG-bench tasks
- lightweight task creation and evaluation
- manually perform BIG-bench tasks (after creating a task in your own branch, use this notebook to manually evaluate and verify that it is behaving correctly)
Quick start instructions to load BIG-bench json tasks using SeqIO :chair:+:telescope:
!pip install git+https://github.com/google/BIG-bench.git # This may take a few minutes
import seqio
from bigbench.bbseqio import tasks
---
## Installation of BIG-bench
**Requirements**
* Python 3.5-3.8
* pytest (for running the automated tests)
**Instructions**
First, clone the repository and set up the environment.
```bash
For agents
This page has a .md twin and JSON over the API.