CoT
Datasets
All datasets matching “CoT”NuminaMath-CoT
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.Alpaca-CoT
Instruction-Finetuning Dataset Collection (Alpaca-CoT)
This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.
We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in https://github.com/PhoebusSi/alpaca-CoT.
If you think this dataset collection is helpful to you, please like… See the full description on the dataset page: https://huggingface.co/datasets/QingyiSi/Alpaca-CoT.cot-eval-traces-2.0CoTracker3_Kubric
Kubric Dataset for CoTracker 3
Overview
This dataset was specifically created for training CoTracker 3, a state-of-the-art point tracking model. The dataset was generated using the Kubric engine.
Dataset Specifications
Size: ~6,000 sequences
Resolution: 512×512 pixels
Sequence Length: 120 frames per sequence
Camera Movement: Carefully rendered with subtle camera motion to simulate realistic scenarios
Format: Generated using Kubric engine
Usage
The… See the full description on the dataset page: https://huggingface.co/datasets/facebook/CoTracker3_Kubric.Zebra-CoT
Zebra‑CoT
A diverse large-scale dataset for interleaved vision‑language reasoning traces.
Dataset Description
Zebra‑CoT is a diverse large‑scale dataset with 182,384 samples containing logically coherent interleaved text‑image reasoning traces across four major categories: scientific reasoning, 2D visual reasoning, 3D visual reasoning, and visual logic & strategic games.
Dataset Structure
Each example in Zebra‑CoT consists of:
Problem statement:… See the full description on the dataset page: https://huggingface.co/datasets/multimodal-reasoning-lab/Zebra-CoT.Visual-CoT
VisCoT Dataset Card
There is a shortage of multimodal datasets for training multi-modal large language models (MLLMs) that require to identify specific regions in an image for additional attention to improve response performance. This type of dataset with grounding bbox annotations could possibly help the MLLM output intermediate interpretable attention area and enhance performance.
To fill the gap, we curate a visual CoT dataset. This dataset specifically focuses on identifying… See the full description on the dataset page: https://huggingface.co/datasets/deepcs233/Visual-CoT.