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Cookbook Assets

Prompt files and dataset details used by cookbook examples.

Text Classification

Dataset

  • Source: seed dimensions created with Seed.product
  • Dimensions: label, trail type, style, language, and model
  • Local output: examples/outputs/45_text_classification_cookbook.jsonl
  • Checkpoints: examples/checkpoints/45_text_classification_cookbook
  • Hub output: optional, controlled by DATAFAST_PUSH_TO_HUB=1

This cookbook models variation directly as seed dimensions so the label, trail type, style, language, and model are all explicit in the pipeline.

Prompt

File Style
text_classification_generation.txt One short trail comment per call, with label, trail type, style, and language injected

Persona Generation

Dataset

  • Source: xsum (Hugging Face), validation split
  • Fields used: id, document, summary
  • Filter: 300–500 words, first 100 matches
  • Local output: examples/outputs/43_persona_cookbook.jsonl
  • Checkpoints: examples/checkpoints/43_persona_cookbook
  • Hub output: set HF_REPO_ID to a repo under your own Hugging Face username or organization; the pipeline's Sink.hub pushes there privately

The example keeps first-match sampling for reproducibility. For local JSONL corpora with metadata such as document_filename, stratified sampling is usually a better fit.

Prompt Variants

Each LLM step picks one prompt at random per record. The script also assigns random life_stage and related_life_stage values before the corresponding LLM steps. Multiple variants add diversity.

Text-to-Persona

File Style
text_to_persona_v1.txt Direct inference of a reader persona
text_to_persona_v2.txt XML-tagged source text, writer/reader framing
text_to_persona_v3.txt System-role preamble, search-interest angle

Persona-to-Persona

File Style
persona_to_persona_v1.txt Close relationship, standalone description
persona_to_persona_v2.txt Rule-list format, explicit separation of description and relationship
persona_to_persona_v3.txt XML-tagged input, concise vivid output

Provenance

  • Text-to-Persona and Persona-to-Persona prompts are paper-aligned adaptations. The Persona Hub paper states its published prompts are simplified, not exact.
  • No Persona Hub code is reused. The workflow is built with datafast primitives.

Space Engineering Text Generation

Dataset

  • Source: seed dimensions created with Seed.product
  • Dimensions: document type, topic, expertise level, and language
  • Local output: examples/outputs/44_space_text_generation_cookbook.jsonl
  • Checkpoints: examples/checkpoints/44_space_text_generation_cookbook
  • Hub output: optional, controlled by DATAFAST_PUSH_TO_HUB=1

Prompt

The text-generation cookbook uses one compact prompt and relies on seed dimensions for variation.

File Style
space_text_generation.txt Minimal variable-driven request