README.md

metadata

license: unknown
task_categories:
  - text-generation
tags:
  - preference
  - dpo
  - reward-model
size_categories:
  - 1K<n<10K

SENTIMENT_ANALYSIS

A preference dataset for SENTIMENT_ANALYSIS, harvested from real, human-labelled sources and curated by an automated harvesting harness with an LLM quality gate.

Format

Standard preference / DPO schema — each row:

column	meaning
prompt	the request (originally text)
chosen	the human-preferred response
rejected	a worse response to the same prompt
source	the dataset/URL the row was harvested from

Splits

80/10/10 train / validation / test (seeded shuffle): train:1665 / validation:208 / test:209

Stats

• Rows: 2082
• Distinct sources: 3

Sources

• Jean-Baptiste/financial_news_sentiment_mixte_with_phrasebank_75
• SetFit/amazon_polarity
• cardiffnlp/tweet_eval (sentiment)

Provenance

prompt and chosen are real — harvested from the human-labelled sources listed above (accepted / upvoted / high-rated responses).

rejected is partly synthetic. Most sources retain only the accepted answer, so where no genuine low-scored counterpart existed the negative was generated by defect injection: an LLM rewrites that row's own chosen with exactly one flaw introduced (omission, factual error, wrong remedy, unsupported claim, or misprioritisation) at matched length. Rows carrying a defect column are the generated ones; rows without it kept a negative that was present in the source.

Rows passed a per-row check (negative is unique, not a stub, not spliced from chosen) and a whole-corpus gate for repeated negatives, length shortcuts, train/eval leakage and bag-of-words separability.