README.md

metadata

language:
  - en
pretty_name: SciTLDR Chat-Format
license: apache-2.0
task_categories:
  - summarization
task_ids:
  - text2text-generation
source_datasets:
  - allenai/scitldr
tags:
  - text
  - science
  - summarization
  - chat-format
  - instruction-tuning
  - datasets
  - allenai/scitldr
  - arxiv:2004.15011

SciTLDR (Chat-Format Preparation)

This dataset is a chat-format preparation of SciTLDR for summarization SFT.

Format

This format is commonly referred to as:

• chat-format SFT data
• instruction-tuning conversations
• OpenAI-style messages format

Included files

• train.jsonl
• validation.jsonl
• stats.json
• prepare_scitldr_unsloth.py

Source

• Base dataset: allenai/scitldr
• Variants used:
  • A
  • AIC
  • FullText

Original Dataset Highlights

• Original dataset: allenai/scitldr
• Focus: extreme summarization of scientific papers (TLDR generation).
• Reported scale on source card: 5.4K TLDRs over ~3.2K papers.
• Multi-target setup: each paper can have multiple valid TLDR summaries.
• Paper: TLDR: Extreme Summarization of Scientific Documents

Preparation summary

• Task: one-sentence scientific TLDR generation.
• User input is built from paper title and source.
• Assistant target is drawn from target.
• Supports:
  • target-policy first: first target only
  • target-policy all: one row per target
• Final train/validation splits are balanced across A, AIC, and FullText.

Schema

Each JSONL row contains:

• messages
  • user: instruction + title + paper content
  • assistant: TLDR summary sentence
• meta: split, source variant, paper_id, target index/count

Reproduction

python prepare_scitldr_unsloth.py --target-policy all