Datasetpublic

ali5341/scitldr-chat-format

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… See the full description on the dataset page: https://huggingface.co/datasets/ali5341/scitldr-chat-format.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

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Dataset Card

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

bash

python prepare_scitldr_unsloth.py --target-policy all