add-dataset

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Skill: Dataset Schema Inspection and Registration

Overview

Inspect a Hugging Face dataset schema with Marin's schema inspection tool, then add a dataset module under experiments/datasets/ so the dataset can be tokenized and consumed in Marin pipelines. Each leaf module exposes <name>_dataset() (one corpus) or <name>_datasets() -> dict[str, ...] (a keyed family) built with the lazy data builders in marin.experiment.data.

Single-corpus datasets: add a small leaf module (e.g. experiments/datasets/svg.py) exposing a <name>_dataset().
Multipart/complex datasets: create a dedicated file (e.g. experiments/datasets/nemotron.py) exposing <name>_datasets().
Datasets with HF-exposed subsets/splits: pattern-match on nemotron.py, keying one handle per subset in the returned dict.

Prerequisites

Prefer repo-managed dependencies over ad hoc pip install.
For repeated work in a checked-out Marin repo, install the synced environment with uv sync --all-packages, then run uv run lib/marin/tools/get_hf_dataset_schema.py.
For one-off schema inspection without a provisioned environment, use ephemeral deps: uv run --with datasets --with pyyaml lib/marin/tools/get_hf_dataset_schema.py ...
Ensure access to the dataset (Hugging Face Hub ID, local path, or other supported format).

Usage

Command line:

uv run lib/marin/tools/get_hf_dataset_schema.py <dataset_name> [options]

Python import:

from marin.tools.get_hf_dataset_schema import get_schema
schema = get_schema(dataset_name="wikitext", config_name="wikitext-103-v1")

Rules

Config handling: always check whether a dataset requires a config first. If required, the tool returns {"error": "Config name is required.", "available_configs": [...]} — select an appropriate config from the list and retry with --config_name.
Text field selection: prioritize a field named exactly text; fall back to fields containing text; consider string-type fields if no obvious text field exists. Examine sample_row to verify field contents.
Error handling: handle missing config, dataset not found, and remote code execution required. Retry with appropriate parameters (e.g. --trust_remote_code).
Performance: the tool streams to avoid full downloads; expect quick responses. sample_row may be empty for some datasets.

Output Format

The tool returns a JSON object:

{
  "splits": ["train", "validation", ...],
  "text_field_candidates": ["text", "content", ...],
  "features": {
    "text": "string",
    "label": "int64",
    ...
  },
  "sample_row": {
    "text": "Example content...",
    ...
  }
}

Example: dataset requiring a config

$ uv run lib/marin/tools/get_hf_dataset_schema.py wikitext
{
  "error": "Config name is required.",
  "available_configs": ["wikitext-103-raw-v1", "wikitext-103-v1", ...]
}

$ uv run lib/marin/tools/get_hf_dataset_schema.py wikitext --config_name wikitext-103-v1
{
  "splits": ["train", "validation", "test"],
  "text_field_candidates": ["text"],
  "features": {"text": "string"},
  "sample_row": {"text": "Article content..."}
}

For datasets needing remote code, add --trust_remote_code.

Next Steps

Once the schema is inspected and the dataset is registered, cargo-cult existing dataset configs for tokenization:

Apply transformations (e.g. field mapping).
Estimate token counts and file sizes.
Find similar dataset configurations in Marin's existing experiments.
Copy and adapt tokenization configs from similar datasets.
Run ablations or trials.