profsynapse/synaptic-tuner/dataset-publishing

Dataset Publishing

Publish a local dataset JSONL to a Hugging Face dataset repo with the skill-owned script: python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py

The script accepts:

• dataset_path
• repo_id

It also auto-uploads a matching metadata sidecar if present:

• dataset.jsonl
• dataset.metadata.json

Quick Reference

Defaults

• Reads HF_TOKEN from the environment or repo .env
• Creates the target dataset repo if needed
• Uploads the dataset file to path_in_repo = basename(dataset_path)
• Auto-detects *.metadata.json sidecars for dotted filenames correctly

Recommended Workflow

1. Build or filter the dataset locally.
2. Run --dry-run first.
3. Run the real upload command.
4. Point the next experiment spec at the uploaded HF dataset file.

Common Patterns

Upload a filtered SFT dataset:

python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset \
  --dry-run
 
python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset

Rename on upload:

python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset \
  --path-in-repo nonthinking_tools_sft_filtered_03.22.26.jsonl

Upload without a sidecar:

python3 .skills/dataset-publishing/scripts/publish_dataset_to_hf.py \
  Datasets/synthchat/my_filtered_dataset.jsonl \
  professorsynapse/claudesidian-synthetic-dataset \
  --no-metadata

CLI Discipline

• Use the checked-in script, not inline Python.
• Run --dry-run before the real upload when testing a new dataset variant.
• Keep dataset filenames descriptive and date-stamped.
• If you create a curated filtered variant, keep the rationale in the .metadata.json sidecar.