--- title: "Curator vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-nemo-curator-vs-xlang-ai-ds-1000" tools: ["nvidia-nemo-curator", "xlang-ai-ds-1000"] --- # Curator vs DS-1000 *GraphCanon updated Aug 24, 2026* ## Verdict Pick Curator if scalable toolkit for data pre-processing tailored to LLMs, featuring deduplication and quality checks; 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. [Curator](https://github.com/NVIDIA-NeMo/Curator) reports 1.7k GitHub stars, 320 forks, and 280 open issues, last pushed Aug 21, 2026. [DS-1000](https://ds1000-code-gen.github.io) has 276 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [Curator's repository](https://github.com/NVIDIA-NeMo/Curator) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [Curator](/tools/nvidia-nemo-curator.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Tagline | Scalable data pre-processing and curation toolkit for LLMs | Benchmark and code for evaluating large language models on data science tasks | | Stars | 1,731 | 276 | | Forks | 320 | 31 | | Open issues | 280 | 2 | | Language | Python | Python | | Adopt for | Scalable toolkit for data pre-processing tailored to LLMs, featuring deduplication and quality checks. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | CC-BY-SA-4.0 | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Curator](/tools/nvidia-nemo-curator.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 644d | | Open issues (now) | 280 | 2 | | Stars delta | +50 (30d) | Unknown | | Open issues delta | +8 (30d) | Unknown | | Full report | [trust report](/tools/nvidia-nemo-curator/trust.md) | [trust report](/tools/xlang-ai-ds-1000/trust.md) | ## Shared compatibility - **Python**: [Curator](/tools/nvidia-nemo-curator.md) - Python runtime; [DS-1000](/tools/xlang-ai-ds-1000.md) - Python runtime ## Decision facts: Curator - **Adopt for:** Scalable toolkit for data pre-processing tailored to LLMs, featuring deduplication and quality checks. ## Decision facts: DS-1000 - **Adopt for:** 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. ## Choose when ### Choose Curator if… - License: Curator is Apache-2.0, DS-1000 is CC-BY-SA-4.0. - Tags unique to Curator: curation toolkit, data pre-processing, deduplication, llms. - You're working with NVIDIA NeMo models and require seamless integration. ### Choose DS-1000 if… - License: DS-1000 is CC-BY-SA-4.0, Curator is Apache-2.0. - Tags unique to DS-1000: benchmark, code generation, data-science, large language models. - 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 Curator - Your dataset doesn't align with NVIDIA hardware specifications. - You prefer data curation tools that do not emphasize semantic processing. ## 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. ## Common questions ### What is the difference between Curator and DS-1000? Curator: Scalable data pre-processing and curation toolkit for LLMs. 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 Curator over DS-1000? Choose Curator over DS-1000 when License: Curator is Apache-2.0, DS-1000 is CC-BY-SA-4.0; Tags unique to Curator: curation toolkit, data pre-processing, deduplication, llms; You're working with NVIDIA NeMo models and require seamless integration. ### When should I choose DS-1000 over Curator? Choose DS-1000 over Curator when License: DS-1000 is CC-BY-SA-4.0, Curator is Apache-2.0; Tags unique to DS-1000: benchmark, code generation, data-science, large language models; 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 Curator? Your dataset doesn't align with NVIDIA hardware specifications. You prefer data curation tools that do not emphasize semantic processing. ### 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 Curator or DS-1000 more popular on GitHub? Curator has more GitHub stars (1,731 vs 276). Stars measure visibility, not whether either tool fits your constraints. ### Are Curator and DS-1000 open source? Yes - both are open-source projects on GitHub (Curator: Apache-2.0, DS-1000: CC-BY-SA-4.0). ### Where can I find alternatives to Curator or DS-1000? GraphCanon lists graph-backed alternatives at [Curator alternatives](/tools/nvidia-nemo-curator/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([Curator markdown twin](/tools/nvidia-nemo-curator/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/alternatives.md)), 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](/compare/nvidia-nemo-curator-vs-xlang-ai-ds-1000.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Curator or DS-1000? Curator: 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 Curator and DS-1000? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Curator trust report](/tools/nvidia-nemo-curator/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-nemo-curator`](/api/graphcanon/graph?tool=nvidia-nemo-curator) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_