Comparison
data-juicer vs DS-1000
Verdict
Pick data-juicer if a Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation; pick DS-1000 if the DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.
Markdown twin · data-juicer alternatives · DS-1000 alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | data-juicer | DS-1000 |
|---|---|---|
| Maintenance | Very active (4d since push) As of 4d · github_public_v1 | Dormant (644d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) 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
- data-juicer
- Data processing for and with foundation models
- DS-1000
- Benchmark and code for evaluating large language models on data science tasks
Stars
- data-juicer
- 6.9k
- DS-1000
- 276
Forks
- data-juicer
- 404
- DS-1000
- 31
Open issues
- data-juicer
- 59
- DS-1000
- 2
Language
- data-juicer
- Python
- DS-1000
- Python
Adopt for
- data-juicer
- A Python library for foundational AI model data processing, offering a pipeline for tasks like instruction tuning and synthetic data generation.
- DS-1000
- The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc.
Persona
- data-juicer
- -
- DS-1000
- -
Runtime
- data-juicer
- -
- DS-1000
- -
License
- data-juicer
- Apache-2.0
- DS-1000
- CC-BY-SA-4.0
Last pushed
- data-juicer
- Aug 13, 2026
- DS-1000
- Oct 30, 2024
Categories
- data-juicer
- Data & Retrieval, Model Training
- DS-1000
- Data & Retrieval, Model Training
Trust and health
Maintenance
- data-juicer
- Very active (96%)
- DS-1000
- Dormant (18%)
Days since push
- data-juicer
- 4d
- DS-1000
- 644d
Open issues (now)
- data-juicer
- 59
- DS-1000
- 2
Stars delta
- data-juicer
- +166 (30d)
- DS-1000
- Unknown
Open issues delta
- data-juicer
- -3 (30d)
- DS-1000
- Unknown
Full report
- data-juicer
- Trust report
- DS-1000
- Trust report
Shared compatibility
- Python · data-juicer: Python runtime · DS-1000: Python runtime
Choose data-juicer if…
- License: data-juicer is Apache-2.0, DS-1000 is CC-BY-SA-4.0.
- Tags unique to data-juicer: foundation-models, instruction-tuning, llm, synthetic-data.
- data-juicer ships Docker support for self-hosted deployment.
- When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning.
When NOT to use data-juicer
- If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation.
Choose DS-1000 if…
- License: DS-1000 is CC-BY-SA-4.0, data-juicer is Apache-2.0.
- Tags unique to DS-1000: benchmark, code generation, data-science, semantic-parsing.
- When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.
When NOT to use DS-1000
- Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python.
- It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datajuicer/data-juicer) · observed Aug 17, 2026
- GitHub forks (datajuicer/data-juicer) · observed Aug 17, 2026
- Last push (datajuicer/data-juicer) · observed Aug 13, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (xlang-ai/DS-1000) · observed Aug 5, 2026
- GitHub forks (xlang-ai/DS-1000) · observed Aug 5, 2026
- Last push (xlang-ai/DS-1000) · observed Oct 30, 2024
- License file (CC-BY-SA-4.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: data-juicer 6.9k · DS-1000 276 (synced Aug 17, 2026).
Common questions
- What is the difference between data-juicer and DS-1000?
- data-juicer: Data processing for and with foundation models. DS-1000: Benchmark and code for evaluating large language models on data science tasks. See the comparison table for live GitHub stats and shared categories.
- When should I choose data-juicer over DS-1000?
- Choose data-juicer over DS-1000 when License: data-juicer is Apache-2.0, DS-1000 is CC-BY-SA-4.0; Tags unique to data-juicer: foundation-models, instruction-tuning, llm, synthetic-data; data-juicer ships Docker support for self-hosted deployment; When you need to preprocess large datasets specifically for training large language models (LLMs) with pipelines that support sophisticated processes like instruction tuning.
- When should I choose DS-1000 over data-juicer?
- Choose DS-1000 over data-juicer when License: DS-1000 is CC-BY-SA-4.0, data-juicer is Apache-2.0; Tags unique to DS-1000: benchmark, code generation, data-science, semantic-parsing; When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries.
- When should I avoid data-juicer?
- If your project does not involve foundational AI model training or if you do not require advanced data processing capabilities such as synthetic data generation.
- When should I avoid DS-1000?
- Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks.
- Is data-juicer or DS-1000 more popular on GitHub?
- data-juicer has more GitHub stars (6,897 vs 276). Stars measure visibility, not whether either tool fits your constraints.
- Are data-juicer and DS-1000 open source?
- Yes - both are open-source projects on GitHub (data-juicer: Apache-2.0, DS-1000: CC-BY-SA-4.0).
- Where can I find alternatives to data-juicer or DS-1000?
- GraphCanon lists graph-backed alternatives at data-juicer alternatives and DS-1000 alternatives (data-juicer markdown twin, DS-1000 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, data-juicer or DS-1000?
- data-juicer: Very active. DS-1000: 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 data-juicer and DS-1000?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: data-juicer trust report; DS-1000 trust report.