datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ReFusion
ReFusion
Dataset Summary
This dataset is the training corpus used for ReFusion, as described in our paper. It comprises approximately 3.7 million high-quality instruction tuning samples consolidated from several state-of-the-art open-source datasets. The data covers diverse domains including mathematics, coding, and general instruction following.
Composition & Sources
The dataset is constructed from the following sources:
MAmmoTH
OpenMathInstruct-2 (1M… See the full description on the dataset page: https://huggingface.co/datasets/GSAI-ML/ReFusion.gsat-115Machine-gradable exam benchmarks produced by any-to-bench. Each subset is one
exam: the viewer table shows one row per answerable question (figures embedded);
the raw, byte-faithful bundle lives under <subset>/bundle/ — exam.json
(structured paper), answer_schema.json (strict JSON Schema an answer sheet must
satisfy), grading.json (deterministic rules + judge rubrics), manifest.json
(provenance), and assets/ (figures).
Usage
Benchmark any model against an exam:
a2b download… See the full description on the dataset page: https://huggingface.co/datasets/JacobLinCool/gsat-115.faa-balloon-flying-handbook
FAA Balloon Flying Handbook Dataset
This dataset was created by processing the official FAA Balloon Flying Handbook (FAA-H-8083-11B).
If you're interested in understanding how this dataset was created, check out this blog post
or explore the details directly in the GitHub repository.
Usage:
from datasets import load_dataset
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("gsantopaolo/faa-balloon-flying-handbook")
print(dataset)
# Print the first 5 rows… See the full description on the dataset page: https://huggingface.co/datasets/gsantopaolo/faa-balloon-flying-handbook.TW-GSAT-Chinese
台灣本土語言模型語料庫：台灣學科能力測驗-中文考科
響應台灣 AI 在地化、AI 十大建設的議題，繁體中文訓練資料最為重要該資料集為 Apache 2.0 開源許可，可用於 商業、研究、私人使用為台灣 AI 在地化盡一份力
使用須知
考試試題之使用符合著作權法
根據中華民國政府的著作權法 - 第9條
下列各款不得為著作權之標的︰ 一、憲法、法律、命令或公文。 二、中央或地方機關就前款著作作成之翻譯物或編輯物。 三、標語及通用之符號、名詞、公式、數表、表格、簿冊或時曆。 四、單純為傳達事實之新聞報導所作成之語文著作。 五、依法令舉行之各類考試試題及其備用試題。
依法舉辦的考試試題是不具備著作權的 適用該條款的考試，包含：學測、會考、學校段考試題，但是不包含補習班、出版商自製的試題 複雜情況：若學校段考考題使用了出版商的題目，那該題目仍然受到著作權的保護，為了規避法律風險，最佳實踐方案是只收集大考考試試題 該資料有調整題目敘述，即重製題目，讓資料更適合 NLP 之任務… See the full description on the dataset page: https://huggingface.co/datasets/TsukiOwO/TW-GSAT-Chinese.ChartMuseum
gsarch/ChartMuseum
This dataset includes images and annotations with keys:
image, question, answer, reasoning_type, source, hash.
Splits
test: 1000 rows
dev: 162 rows
Images are embedded via the datasets.Image feature, so they are available
directly when loading the dataset with datasets.load_dataset("gsarch/ChartMuseum").
tw-gsat-chat
tw-gsat — 台灣學測 SFT 資料集（國文 + 社會科，110–115 學年度）
本資料集為合成 SFT 訓練資料，涵蓋台灣學測國文與社會科選擇題。
子集
Subset
筆數
說明
chinese
152
學測國文（110–115）
society
237
學測社會（110–115）
default (merged)
389
合併版
chinese_v2
152
國文 v2——結構化 think + 豐富 output + \boxed{X}
society_v2
237
社會 v2——同上
merged_v2
389
v2 合併版
Schema
與 lianghsun/secret-chat 相同格式：
unique_id, messages, turn, question, think, answer, tools,
system_prompt, lang_question, lang_answer, lang_think,
tags… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/tw-gsat-chat.