由掘金 A 股分钟收盘价生成。returnynoon2.pkl 与 returnynoon2.parquet 保存同一 pandas DataFrame：行索引为 Python date，列为六位股票代码， 值为小数收益率（0.01 表示 1%），保留 NaN。上传前精确校验两种格式和完整日期序列， 同一次提交发布两个数据文件及本说明。此数据集独立于 suncong/ylabels。 午间交易日 11:35 运行，使用 pandasmarketcalendars 的 SSE 日历判断休市和数据新鲜度，只使用严格早于运行日期的行情目录；掘金数据在每日收盘后更新。 历史计算序列依据有效行情目录；定时任务另外核对最后两日与 SSE 日历一致，缺少昨日行情时失败而不回退。 保持 CSV 原始行序，按日 ffill 后对完整分钟序列执行 rolling(2).mean()； 每天按 240 行位置采样，buymin=135、sellmin=245。 均价卖出的 (卖价 - 买价) / 买价。收益率最终执行 shift(-1).iloc[:-1]。 00/60 开头代码为 9.85%，其余为 19.85%；8/9 开头代码另按 change < 100 排除。 通过 Hugging Face 下载时固定提交哈希，以复现历史版本；只反序列化可信的 Pickle。 推荐下载 returnynoon2.parquet 后使用 pandas.readparquet。
SAVRN Model Hub
AI Training Datasets
Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
3,164 datasets, sorted by most downloaded.
Part of a dataset collection on Hugging Face. Complete Rocket Lab launch manifest — past and upcoming missions flown or planned by Peter Beck's small-launch company, sourced from The Space Devs Launch Library 2 API. Covers Electron (small-lift two-stage rocket using Rutherford 3D-printed engines, operational since 2017 from Mahia Peninsula in New Zealand and from LC-2 at Wallops Island, Virginia) and the forthcoming Neutron medium-lift partially reusable rocket. Each row captures mission identifier, vehicle, launch time, status, pad location, target orbit, and a free-text mission description. Includes the full Electron history from the May 2017 'It's a Test' debut through the current…
Gated chat/SFT data for training MAI Profile models. The repository contains user-profile examples generated by GPT-5.4. The V1 configurations contain the same rows as their V0 counterparts, with the user prompt replaced by a shorter task prompt. The Layer 3 and Layer 4 V1 configurations hold out at least 2,000 test rows each by user: 12.3% of users for L3 Persona, L3 Commercial, L4 Biography, and L4 Commercial Preference, 10.1% for L4 Mission Discovery, and 2% for L4 Mission Enhancement. These test users include every Layer 1/2 test user. The GPT-5.4 configurations use deterministic 2% user-level test splits, so a user never appears in both train and test within a layer. Full chat length…
This dataset was created using LeRobot.
COCO-Attr is the COCO-based evaluation dataset used in: Representation and Reference Selection in Training-Free Synthetic Image Attribution The dataset contains images from 10 synthetic image generators and supports two reference-based source attribution protocols: - Arbitrary references - Resynthesis references The dataset contains images from the following 10 generators: The query set contains 100 images per generator. The query images and pre-generated reference images are derived from disjoint COCO source splits. Only the 1,000 validation-side images actually used as queries in the reported experiments are included. query/ Contains the 1,000 evaluation queries, organized by generator.…
A community-driven dataset for the Pa'O (blk) language, designed to support artificial intelligence, natural language processing (NLP), large language models (LLMs), conversational AI, and language technology research. The dataset focuses on three core Pa'O language resources: The primary goal is to build high-quality, reusable Pa'O language data for AI research, language technology, digital language preservation, and future Pa'O-capable AI systems. pao-ai-qa-conversation-instruction/ ├── data/ │ │ └── qa.jsonl │ ├── conversation/ │ │ └── conversations.jsonl │ └── instruction/ │ └── instructions.jsonl ├── README.md Question-and-answer pairs for Pa'O language understanding and…
FormosanBank publication status This audio is associated with XML published in the public FormosanBank corpus and uses the same license recorded in that XML: CC BY-NC 4.0. View the published XML. Publication approval is recorded on the corresponding FormosanBank Basecamp card. FormosanBank/ILRDFDicts Commercial AI Use is prohibited without prior written permission. See the FormosanBank Terms of Use and AI Use Addendum. This is a canonical public audio dataset
Copyright (c) 2026 Toukir Studio. All Rights Reserved. These image assets are proprietary and created specifically for the LexiCore dictionary project. - Unauthorized copying, redistribution, scraping, modification, or republication of these assets is strictly prohibited. - These images may not be used for commercial purposes or included in any third-party training datasets without explicit written permission.
Current (현행) local legislation of Korean local governments — ordinances (조례), rules (규칙) and other local instruments — from the National Law Information Center (www.law.go.kr) of the Ministry of Government Legislation. Ongoing harvest in batches (data/-0000N.parquet); later batches add more local governments. - data/laws-0000N.parquet: one row per instrument (full text, title, effective/promulgation dates, number, issuing government). - data/articles-0000N.parquet: one row per article (제N조) plus one supplementary-provisions row (부칙, recordtype supplementaryprovision) per instrument; lawid joins to laws.id. laws.articlecount counts 제N조 articles only. Copyright/terms (summary in our words)…
Research snapshot of official national legislation from Nepal Law Commission (lawcommission.gov.np). Not legal advice. The official gazette / authentic source prevails over this corpus. - data/laws.parquet — one row per instrument. - data/articles.parquet — article/section rows for the same snapshot. - scrapers/collectnp.py — research collector used to build this snapshot. Official Nepal Law Commission texts; authentic text prevails; not legal advice. This dataset is not legal advice. The official source prevails. - https://www.lawcommission.gov.np/ Thin deepen 2026-09-18 PT: Hub-restore tip 52 from endomorphosis/ipfsnepallaws@8ebcab98cea47106485663d85b5556c9845fd91a then offline…
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
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The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
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Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.