license: apache-2.0
language:

• en
  task_categories:
• text-generation
  tags:
• BlackTarot
• uncensored
• instruct
• curated
• synthetic-big
• sharegpt
• alpaca
• instruction-tuning
• sft
• supervised-fine-tuning
• chatml
• reasoning
• text-generation
• synthetic-data
  pretty_name: BlackTarot Curated
  size_categories:
• 1K<n<10K
  viewer: true
  configs:
• config_name: default
  data_files:
  • split: train
    path: data/sharegpt/train.parquet
  • split: test
    path: data/sharegpt/test.parquet
• config_name: sharegpt
  data_files:
  • split: train
    path: data/sharegpt/train.parquet
  • split: test
    path: data/sharegpt/test.parquet
• config_name: alpaca
  data_files:
  • split: train
    path: data/alpaca/train.parquet
  • split: test
    path: data/alpaca/test.parquet
• config_name: text
  data_files:
  • split: train
    path: data/text/train.parquet
  • split: test
    path: data/text/test.parquet

BlackTarot Curated

BlackTarot Uncensored — Curated core by Grimxlock. 5,070 records (4,871 train / 199 test) from unbound-instruct canonical pipeline: 78 human-authored + 4,992 synthetic-big (0.9559 mean quality, 0.16% dedup).

• sharegpt: system + conversations[{from: human/gpt, value}] — Axolotl/LLaMA-Factory
• alpaca: instruction/input/output — Aisha shape
• text: text with <|system|>/<|user|>/<|response|> — dolphin-2 shape

from datasets import load_dataset
ds = load_dataset("Grimxlock/BlackTarot-Curated", "sharegpt") # or alpaca, text
print(ds["train"][0])
# streaming
ds = load_dataset("Grimxlock/BlackTarot-Curated", "alpaca", streaming=True)

Built via python -m unbound.build (schema validate → quality gate → MinHash dedup → deterministic split → 3 formats). See manifest.json for sha256 + stats. Parent BIG synthetic: Grimxlock/BlackTarot-Uncensored (250k).

Why Competitive with Popular

Same quality bar as BIG (0.956) but curated for fine-tuning: 5,070 hand-verified + synthetic mix, 0.16% dedup, 3 configs vs single-format popular python_code_instructions_18k (347 likes). To reach 29k downloads tier, add to HF Collections and enable trending.

Citation

@dataset{blacktarot_curated_2026,
  title={BlackTarot Curated},
  author={Grimxlock},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Grimxlock/BlackTarot-Curated}
}