AI Training Datasets

Dataset

objaverse

Ai2

Objaverse is a Massive Dataset with 800K+ Annotated 3D Objects. In the meantime, please see our paper and website for additional details. The use of the dataset as a whole is licensed under the ODC-By v1.0 license. Individual objects in Objaverse are all licensed as creative commons distributable objects, and may be under the following licenses: - CC-BY 4.0 - 721K objects - CC-BY-NC 4.0 - 25K objects - CC-BY-NC-SA 4.0 - 52K objects - CC-BY-SA 4.0 - 16K objects - CC0 1.0 - 3.5K objects The metadata will provide the license for each object. To cite Objaverse, please use the following BibTeX entry

Dataset · Robotics

10Kh-RealOmin-OpenData

Genrobot.ai

Boasting over 13,000 hours of cumulative data and 5 million+ clips, it ranks as the largest open-source embodied intelligence dataset in the industry. Update Notes：Stage 3 data upload completed. 13,000+ hours of pure dual-hand data with frame-level alignment latency < 1ms Full high-precision trajectory reconstruction, breaking the limit of superficial open source, fully ready-to-use 3,000+ contributors and 10,000+ real household scenarios with exceptional diversity

Dataset · Text classification

super_glue

Amanpreet Singh

SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard. An example of 'test' looks as follows. An example of 'test' looks as follows. An example of 'train' looks as follows. An example of 'train' looks as follows. An example of 'train' looks as follows. The data fields are the same among all splits. - sentence1: a string feature. - sentence2: a string feature. - idx: a int32 feature. - label: a classification label, with possible values including entailment (0), notentailment (1). - premise: a string feature. - hypothesis: a string feature. - idx: a…

DCLM-baseline is a 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. Below are comparisions of model trained on DCLM-baseline with other models in the 7B regime. DCLM-Baseline is intended to be used as a research baseline for the DCLM benchmark. It demonstrates the importance of data curation in training performant language models. DCLM-Baseline is not intended for training production-ready models or for specific domains such as code and math. It may not perform as well as domain-specific datasets for these tasks. Due to these limitations, the dataset is intended for research use only. DCLM-Baseline is a subset of the DCLM-Pool, which…

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. MINT-1T is designed to facilitate research in multimodal pretraining. MINT-1T is created by a team from the University of Washington in collaboration with Salesforce Research, other academic institutions including Stanford University, University of Texas at Austin, and University of California Berkeley. You are currently viewing the HTML subset of…

Dataset

OpenAoE-2000h

inclusionAI

Open-AoE — Egocentric Hand Manipulation Dataset Release Roadmap Tier Duration Status nano ~3 h Released tiny ~100 h Released full 2000 h Uploading Release notes 2026-07-30: Removed samples flagged in PR #1 for camera-intrinsics vs. video-resolution mismatches. 2026-07-31: Uploaded ~323h of data. 2026-08-12: Uploaded ~694h of data. 2026-09-03: Uploaded ~189h of data. Additional data for the full ~2000h release is still

I also seperately provide just the prompts in prompts.json keys are the imageid, and the values are the captions generated Captions generated by moondream: vikhyatk/moondream2 Latents generated by SDXL VAE: madebyollin/sdxl-vae-fp16-fix Embeddings generated by SigLIP: hf-hub:timm/ViT-SO400M-14-SigLIP-384 Original dataset: common-canvas/commoncatalog-cc-by Latents f32 and embeddings are f16 bytes Compute cost: 16x3090 for 3 day. Approximately.

The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released here as part of Program Synthesis with Large Language Models, Austin et. al., 2021. This dataset is used to evaluate code generations. English - Python code - sourcefile: unknown - text/prompt: description of programming task - code: solution for programming task - testsetupcode/testimports: necessary code…

Dataset · Image classification

typed_digital_signatures

Ben

Typed Digital Signatures Dataset This comprehensive dataset contains synthetic digital signatures rendered across 30 different Google Fonts, specifically selected for their handwriting and signature-style characteristics. Each font contributes unique stylistic elements, making this dataset ideal for robust signature analysis and font recognition tasks. Total Fonts: 30 different Google Fonts Images per Font: 3,000 signatures Total Dataset Size

Dataset · Question answering

openbookqa

Ai2

OpenBookQA aims to promote research in advanced question-answering, probing a deeper understanding of both the topic (with salient facts summarized as an open book, also provided with the dataset) and the language it is expressed in. In particular, it contains questions that require multi-step reasoning, use of additional common and commonsense knowledge, and rich text comprehension. OpenBookQA is a new kind of question-answering dataset modeled after open book exams for assessing human understanding of a subject. An example of 'train' looks as follows: An example of 'train' looks as follows: The data fields are the same among all splits. - id: a string feature. - questionstem: a string…

Dataset · Robotics

language_table_lerobot

IPEC at Shanghai AI Laboratory

This dataset was created using LeRobot.

Production-ready pipeline (Python package videovec2wav2tokenizer, CLI command video2dataset) that turns a folder of videos into clean AI training datasets for speech recognition (ASR) and text-to-speech (TTS). Video processing — recursive scan of mp4 / mkv / avi / mov / webm, FFmpeg audio Speech recognition — faster-whisper, CPU & CUDA, automatic language detection, word-level timestamps. Segmentation — cut audio by transcript timestamps into dataset/audio/000001.wav …. Dataset generation — metadata.csv, dataset.jsonl, ttsmetadata.csv. Feature extraction (optional) — streaming features/train.bin + train.dat with float32 samples, mel spectrograms, duration and sample rate. Statistics…