datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ArXivSignals-DeepSummaries
ArXivSignals DeepSummaries — Agent-Built Visual Paper Explainers
A continuously-updated, day-partitioned dataset of deep, visual summaries of
arXiv papers, each built by a coding agent working inside the paper's own
LaTeX source: the agent reads the full text, authors an editorial narrative as
a structured content spec, and the paper's real figures and tables
(extracted and rendered from the LaTeX, web-optimized) ride along as an
embedded, variable-length image array. The… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/ArXivSignals-DeepSummaries.Ko-Agent-Trajectories-1.0
Ko-Agent-Trajectories-1.0
Dataset card v1.1.1 (2026-09-22). The pipeline code is now released in this repository
under pipeline/, together with the API catalogue, the scenario templates and the complete
prompt set. The card reports the completed human review study and the v1.1 artefacts
(behaviour DPO config, per-item validation scores, manifest, filter asset).
Korean edition: README.ko.md.
TL;DR
A Korean multi-turn agent ↔ tool trajectory corpus synthesized… See the full description on the dataset page: https://huggingface.co/datasets/taejoon89/Ko-Agent-Trajectories-1.0.TinyStories-Farsi
Tiny Stories Farsi
The Tiny Stories Farsi project is a continuous effort to translate the Tiny Stories dataset into the Persian (Farsi) language. The primary goal is to produce a high-quality Farsi dataset, maintaining equivalency with the original English version, and subsequently to utilize it for training language models in Farsi. This seeks to affirm that the advancements and trends observed in English language models are replicable and applicable in other languages. Thus far… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/TinyStories-Farsi.ArXivSignals-FullText
ArXivSignals FullText — arXiv Papers OCR'd to Markdown + Layout
A continuously-updated, day-partitioned dataset of arXiv papers converted to
clean full text by a vision OCR pipeline: each paper's PDF is rendered to
Markdown (headings, paragraphs, tables as HTML, math as LaTeX) plus a structured
layout JSON (typed, bounding-boxed blocks). It is the full-text companion to
taesiri/ArXivSignals
(metadata + LLM signal & summaries) and joins it on paper_id.
How it's made… See the full description on the dataset page: https://huggingface.co/datasets/taesiri/ArXivSignals-FullText.KoAlpaca_hira_v1.1a
Dataset Card for "KoAlpaca-v1.1a"
Project Repo
Github Repo: Beomi/KoAlpaca
How to use
>>> from datasets import load_dataset
>>> ds = load_dataset("beomi/KoAlpaca-v1.1a", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 21272
})
>>> ds[0]
{'instruction': '양파는 어떤 식물 부위인가요? 그리고 고구마는 뿌리인가요?',
'output': '양파는 잎이 아닌 식물의 줄기 부분입니다. 고구마는 식물의 뿌리 부분입니다. \n\n식물의 부위의 구분에 대해 궁금해하는 분이라면 분명 이 질문에 대한 답을 찾고 있을 것입니다. 양파는 잎이 아닌 줄기… See the full description on the dataset page: https://huggingface.co/datasets/Taegyuu/KoAlpaca_hira_v1.1a.KoAlpaca-v1.1a
Dataset Card for "KoAlpaca-v1.1a"
Project Repo
Github Repo: Beomi/KoAlpaca
How to use
>>> from datasets import load_dataset
>>> ds = load_dataset("beomi/KoAlpaca-v1.1a", split="train")
>>> ds
Dataset({
features: ['instruction', 'input', 'output'],
num_rows: 21155
})
>>> ds[0]
{'instruction': '양파는 어떤 식물 부위인가요? 그리고 고구마는 뿌리인가요?',
'output': '양파는 잎이 아닌 식물의 줄기 부분입니다. 고구마는 식물의 뿌리 부분입니다. \n\n식물의 부위의 구분에 대해 궁금해하는 분이라면 분명 이 질문에 대한 답을 찾고 있을 것입니다. 양파는 잎이 아닌 줄기… See the full description on the dataset page: https://huggingface.co/datasets/Taegyuu/KoAlpaca-v1.1a.