AI Training Datasets

Course: FTEC 6V96 · Financial NLP & Textual Analysis · Author: Ashly Caicedo This project builds a story from two earlier homeworks, HW1 (a data-driven sentiment lexicon compared against TextBlob, VADER and Loughran-McDonald) and HW2 (RBM topic modelling), on a dataset of S&P 500, Dow and NASDAQ news headlines. As the assignment requires, it changes the target variable, the tokenization technique and the ML approach, and reports metrics on the train, test and inference partitions. HW1, the lexicon's edge was the market's mood. The custom lexicon beat all three benchmarks on train and test but lost on inference (0.604 vs. 0.623 precision). In October 2020 the market rose on 62% of days, and…

(1) The CEE-QA test question set comprises 1,200 questions from Registered Construction Engineer Examinations (RCEEs) and 1,881 self-designed questions covering 18 second-level knowledge sub-domains (KSD) by experts. (2) Each question from the CEE-QA test question set are manually annotated with four features, including the question source, single-answer question or multiple-answer question, and calculation question or non-calculation question. (3) This CEE-QA test question set is developed and maintained by Southeast University. (4) Make sure to read the specification and follow the rules. The answers could be submitted through…

Dataset · Text generation

QWE

SHRIJAN KUMAR TIWARI

Instruction-tuning dataset with chain-of-thought, merged and deduplicated from multiple public reasoning datasets. Every row is JSON with: - instruction - the user/task prompt - thought - reasoning wrapped in tags (always present) - response - the final answer - hasthinking - always true - sourcedataset - provenance of the row in row order. Sources include OpenThoughts3-1.2M, Mixture-of-Thoughts, Infinity-Instruct and Nemotron-Post-Training-Dataset (v1 and v2). Deduplication is exact full-line (md5), applied globally across all sources. - All model-identity references were normalized to "Speed by SKT AI LABS Private Limited". - A small fraction of rows (mostly from one legacy source) carry…

1,842 synthetic training examples for Qev decision models. Each example provides a context, a question, explicit answer options and a reviewed hard label. This release collects the self-generated portion of the training recipe used for the released Qev-9B. The same source inputs were available in Qev-2B's initial teacher-probability training. It includes original hard labels, not teacher probability caches, response targets or the later distillation pairs. It is a subset of the original mixed training corpus. All examples are in the train split. There is no held-out evaluation split: these inputs have already been used in model development and training. Original wording, candidate order…

Dataset · Text generation

Qwen3.5-0.8B-Japanese-SFT-v2.5-Data

Atemoya

Takenoko12345678/Qwen3.5-0.8B-Japanese-SFT-v2.5 の追加学習に使ったデータを、学習に使ったそのままの形で公開しています。中身は変えず、1件ごとに出典（source）・ライセンス（license）・文を書いたモデル（generator）の欄だけを足しています。 学習・データ作りのスクリプトは Pitcher プロジェクト の sft/q08bv25/ にあります。 2段目の chatthink 以外の 3,000件は、1段目のデータからの復習用（半分は理系）です。 思考ありの手本は、学習時に「 考える部分 答え」の形にしています（元の Qwen3.5 のチャットテンプレートで enablethinking=True）。 - このデータのために新しく書かせた文は、すべて Qwen/Qwen3.8-27B（Apache 2.0）によるものです。理系の問題の日本語訳と解き方、会話の手本、コードの回答、思考の部分がこれに当たります。tengentoppa・formatdrill は、公開済みの Japanese-SFT-Tengentoppa-Curated-12K の行をそのまま使っています（その元データの作り方は、元データの説明を見てください） - コードの手本の選別には Gemma 4 31B の採点を使っています（文は書いていません）。詳しくは Japanese-Code-Review-OSS の説明を見てください…

This release contains the files referenced by trainingmeshfivefrozensinglev2.portable.jsonl. Assets retain their paths relative to the prepared-data root and are split into tar archives to keep the Hub repository file count manageable. Use packagemanifest.json to inspect shard sizes and SHA-256 checksums. Redistribution and use remain subject to the licenses of the contributing source datasets.

Final manifest-referenced files for Basic Dynamics, Interaction short, Interaction-Long, and Arena. Raw capture directories,.dem replays, source64hz.mkv, logs, and production intermediates are excluded. Manifest paths are rewritten relative to this package. Files are copied as independent files. This is a component-licensed release. RIGen-authored annotations, actions, states, events, visibility labels, prompts, manifests, QA metadata, and documentation are CC BY 4.0. Xonotic-derived media and reference assets remain subject to applicable upstream terms; see LICENSE.md, NOTICE, and UPSTREAMLICENSES/. For a Hugging Face Dataset Card, use

Training data for Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing (IJCAI 2026). [[Paper]](https://www.ijcai.org/proceedings/2026/231) [[Code]](https://github.com/zwy-Giser/Uni-RS) Unzip the three archives into one directory: One JSON object per line. Rows are aligned by index with the RSICap and VRSBench caption datasets. A plan contains SCENE (global scene), OBJ (object instances with a coarse position on a 3x3 grid: TopLeft, TopCenter,..., BottomRight, plus optional count and attribute) and REL (relations between objects) lines: One JSON object per line. Rows are aligned by index with the RSICap and VRSBench caption datasets.…

Mirror of X-Humanoid/RoboMIND2.0-UR5 from ModelScope. All credit to the original authors. Not an official release from them. License unchanged (apache-2.0). For dataset file metadata and data files, please visit the "Files and versions" tab. The current dataset card uses the default template, and the dataset contributor has not provided a more detailed dataset introduction. However, you can download the dataset using the following GIT Clone command or through the ModelScope SDK.

RoboMME-10x is a LeRobot v2.1 dataset containing 16,000 episodes across 16 RoboMME tasks. It contains 7,636,924 frames sampled at 10 FPS. Images are stored directly in the per-episode Parquet files. Dataset metadata is available under meta/, and episode data is arranged as

Dataset · Robotics

Robot-EQ-Video

Tongji Emotion

Video release for RobotEQ. Access is gated: you must request permission and wait for manual approval before downloading any files. This repository is public so the dataset page can be found and cited, but video files are not downloadable until the authors accept your request. You must be logged in (hf auth login) with an account that has been approved. CC BY-NC 4.0. Non-commercial research use only.