paperbd/paper_instructions_300K-v1

--- task_categories: - question-answering - summarization - text-to-speech language: - en size_categories: - 10K<n<100K --- ## Dataset Summary This dataset contains synthetic supervised fine-tuning data generated from academic papers using `text-albumentations`. - Rows: 300,000 - Source documents: 1,500 papers - Format: Alpaca-style instruction tuning rows Data was synthetically generated using Qwen3.5-4B with `text-albumentations` library (https://github.com/avbiswas/text-albumentations) Each row is derived from source text through structured augmentations such as: - bullet extraction - question-answer generation - rephrasing - continuation-style supervision - comparison and retrieval-style tasks - knowledge graph triplet extraction The goal is to turn long-form technical text into diverse, task-shaped supervision for distillation and SFT workflows. ## Supported Tasks - supervised fine-tuning - instruction tuning - distillation ## Data Structure Each example follows an Alpaca-style schema: ```json { "instruction": "string", "input": "string", "output": "string" } ``` ## Source Data The dataset was generated from a collection of 1,500 ML/AI papers. The source material was transformed into synthetic instruction-response pairs through structured augmentation pipelines rather than copied as raw passages alone. ## Limitations - This is synthetic data, not human-written gold supervision. - Output quality depends on the underlying model and prompting pipeline used during generation. - The dataset may contain factual omissions, formatting inconsistencies, or augmentation artifacts. - Coverage and style are shaped by the source papers and the selected augmentation families. ## License This dataset is for educational purposes. Please ensure your use is compatible with the licenses and terms of the original source papers.