Comparison
bigcode-evaluation-harness vs ACLUE
Verdict
Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick ACLUE if aCLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge.
Markdown twin · bigcode-evaluation-harness alternatives · ACLUE alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | bigcode-evaluation-harness | ACLUE |
|---|---|---|
| Maintenance | Dormant (378d since push) As of 2w · github_public_v1 | Dormant (868d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- bigcode-evaluation-harness
- A framework for evaluating autoregressive code generation language models.
- ACLUE
- Evaluation Benchmark for Ancient Chinese Language Comprehension
Stars
- bigcode-evaluation-harness
- 1.1k
- ACLUE
- 34
Forks
- bigcode-evaluation-harness
- 261
- ACLUE
- 0
Open issues
- bigcode-evaluation-harness
- 96
- ACLUE
- 0
Language
- bigcode-evaluation-harness
- Python
- ACLUE
- Python
Adopt for
- bigcode-evaluation-harness
- bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments.
- ACLUE
- ACLUE is an evaluation benchmark for testing how well large language models understand ancient Chinese texts covering syntax, semantics, reasoning, and knowledge.
Persona
- bigcode-evaluation-harness
- -
- ACLUE
- -
Runtime
- bigcode-evaluation-harness
- -
- ACLUE
- -
License
- bigcode-evaluation-harness
- bigcode-evaluation-harness is distributed under the Apache-2.0 license.
- ACLUE
- MIT License: Permissive open-source license allowing free use and modification of the software, including commercially.
Last pushed
- bigcode-evaluation-harness
- Jul 22, 2025
- ACLUE
- Mar 20, 2024
Categories
- bigcode-evaluation-harness
- Evaluation & Observability
- ACLUE
- Evaluation & Observability
Trust and health
Days since push
- bigcode-evaluation-harness
- 378d
- ACLUE
- 868d
Open issues (now)
- bigcode-evaluation-harness
- 96
- ACLUE
- 0
Owner type
- bigcode-evaluation-harness
- Organization
- ACLUE
- User
OSV dependency advisories
- bigcode-evaluation-harness
- Published findings
- ACLUE
- No lockfile (source not queried)
Full report
- bigcode-evaluation-harness
- Trust report
- ACLUE
- Trust report
Choose bigcode-evaluation-harness if…
- License: bigcode-evaluation-harness is Apache-2.0, ACLUE is MIT.
- Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation..
- Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python.
- bigcode-evaluation-harness ships Docker support for self-hosted deployment.
- When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
When NOT to use bigcode-evaluation-harness
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker.
- If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
Choose ACLUE if…
- License: ACLUE is MIT, bigcode-evaluation-harness is Apache-2.0.
- Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks.
- When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 tasks
When NOT to use ACLUE
- For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension
- When the focus is strictly on contemporary texts without a need for historical language understanding capabilities
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bigcode-project/bigcode-evaluation-harness) · observed Aug 5, 2026
- GitHub forks (bigcode-project/bigcode-evaluation-harness) · observed Aug 5, 2026
- Last push (bigcode-project/bigcode-evaluation-harness) · observed Jul 22, 2025
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (isen-zhang/ACLUE) · observed Aug 6, 2026
- GitHub forks (isen-zhang/ACLUE) · observed Aug 6, 2026
- Last push (isen-zhang/ACLUE) · observed Mar 20, 2024
- License file (MIT) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: bigcode-evaluation-harness 1.1k · ACLUE 34 (synced Aug 5, 2026).
Common questions
- What is the difference between bigcode-evaluation-harness and ACLUE?
- bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. ACLUE: Evaluation Benchmark for Ancient Chinese Language Comprehension. See the comparison table for live GitHub stats and shared categories.
- When should I choose bigcode-evaluation-harness over ACLUE?
- Choose bigcode-evaluation-harness over ACLUE when License: bigcode-evaluation-harness is Apache-2.0, ACLUE is MIT; Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
- When should I choose ACLUE over bigcode-evaluation-harness?
- Choose ACLUE over bigcode-evaluation-harness when License: ACLUE is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to ACLUE: ancient texts, chinese language, language models evaluation, nlp benchmarks; When evaluating the performance of LLMs specifically on comprehending ancient Chinese language across 15 tasks.
- When should I avoid bigcode-evaluation-harness?
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
- When should I avoid ACLUE?
- For benchmarking modern Chinese or other languages not related to ancient Chinese comprehension When the focus is strictly on contemporary texts without a need for historical language understanding capabilities
- Is bigcode-evaluation-harness or ACLUE more popular on GitHub?
- bigcode-evaluation-harness has more GitHub stars (1,055 vs 34). Stars measure visibility, not whether either tool fits your constraints.
- Are bigcode-evaluation-harness and ACLUE open source?
- Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, ACLUE: MIT).
- Where can I find alternatives to bigcode-evaluation-harness or ACLUE?
- GraphCanon lists graph-backed alternatives at bigcode-evaluation-harness alternatives and ACLUE alternatives (bigcode-evaluation-harness markdown twin, ACLUE markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, bigcode-evaluation-harness or ACLUE?
- bigcode-evaluation-harness: Dormant. ACLUE: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for bigcode-evaluation-harness and ACLUE?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: bigcode-evaluation-harness trust report; ACLUE trust report.