NL2MANIM_Dataset

This dataset maps natural-language scene descriptions and prompts to executable Manim Community Edition (ManimCE) Python code. It was prepared to train/finetune instruction-following models for generating Manim animations from textual specifications.

Key facts

• Curated by: Repository owner (see project repository)
• Dataset name: NL2MANIM_Dataset
• Size: 8,352 JSONL examples (training file: data/dataset_peft_train.jsonl)
• Language(s): English
• License: Not specified (please add an explicit license before public redistribution)

Dataset Details

Dataset Description

NL2MANIM_Dataset contains examples where a natural-language prompt (often framed as a <TEXT_SCRIPT> description) requests a Manim scene, and the target output is an executable Manim Python snippet (typically wrapped in a <CODE> block). The examples are formatted as line-delimited JSON (JSONL), intended for instruction tuning / PEFT workflows.

• Typical use cases: training code-generation models, instruction-following fine-tuning, automated generation of educational animations.
• Not designed for natural-language understanding benchmark evaluation beyond code generation tasks.

Dataset Sources

• Repository / origin: Created and maintained inside this project workspace.

Uses

Direct Use

• Fine-tuning or instruct-tuning language models to generate Manim animations from textual descriptions.
• Developing systems that convert pedagogical text into visual animations.

Out-of-Scope Use

• Composing datasets for general NLP tasks unrelated to code or animation generation.
• Using without license clearance if any examples include third-party copyrighted content.

Dataset Structure

Each line in the JSONL is a single training example (one JSON object). Observed schema (examples):

• messages (array): sequence of chat-style messages. Each message is an object with keys:
  • role: one of system, user, assistant.
  • content: string containing the message text. For user, content typically includes the natural-language scene description (often wrapped in a <TEXT_SCRIPT> block). For assistant, content is the target Manim code (often wrapped in <CODE> and fenced with a Python block).

Example (abbreviated)

{
  "messages": [
    {"role":"system","content":"You are an expert Manim..."},
    {"role":"user","content":"<TEXT_SCRIPT>Scene Sequence: 1. Display the title... </TEXT_SCRIPT>"},
    {"role":"assistant","content":"<CODE>```python\nfrom manim import *\nclass Scene...\n```</CODE>"}
  ]
}

Splits

The repository currently includes a single training JSONL at data/dataset_peft_train.jsonl containing 8,352 examples. No official validation/test splits are provided in the repo — you may create held-out splits for evaluation.

Dataset Creation

Curation Rationale

The dataset was created to provide pairs of instruction-like prompts and executable Manim code to enable models to learn mapping from pedagogical natural language to animation-generating code.

Data Collection and Processing

• Examples appear to be authored/semi-structured: prompts follow a consistent <TEXT_SCRIPT> pattern and assistant outputs contain Manim Python code inside <CODE> blocks.
• Minimal normalization performed: content preserved as text/code blocks; no attempt at executing or linting every code sample when assembling the JSONL.

Who are the source data producers?

• The examples appear authored/curated by repository contributors (project maintainers / dataset curator). No external annotator metadata is present.

Annotations

• None beyond the contained messages chat structure.

Bias, Risks, and Limitations

• The dataset is narrowly focused on Manim code generation; models trained on it will specialize in this domain and may not generalize to other code tasks.
• Some code examples may contain content that is copyrighted or that embeds third-party code snippets — verify licensing before public release.
• Code in the dataset may include unsafe patterns if executed (e.g., arbitrary Python execution). Do not run untrusted examples without review.

Recommendations

• Add an explicit license file and dataset license metadata before publishing.
• Create a curated validation split and perform linting/formatting checks on generated code samples if you plan to run them.

Citation

No formal paper. If you use this dataset, cite the repository and include a short description of how examples were generated.

More Information

For the raw training file, see: data/dataset_peft_train.jsonl

Dataset Card Authors

• Maintainer: repository owner / contributors

Contact

• Please open an issue in the project repository or contact the dataset maintainer listed in the repository.