datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
taco-datasetsThis repo consists of the datasets used for the TaCo paper. There are four datasets:
Multilingual Alpaca-52K GPT-4 dataset
Multilingual Dolly-15K GPT-4 dataset
TaCo dataset
Multilingual Vicuna Benchmark dataset
We translated the first three datasets using Google Cloud Translation.
The TaCo dataset is created by using the TaCo approach as described in our paper, combining the Alpaca-52K and Dolly-15K datasets.
If you would like to create the TaCo dataset for a specific language, you can… See the full description on the dataset page: https://huggingface.co/datasets/saillab/taco-datasets.TACO-verified
Introduction
This dataset contains verified solutions from the TACO dataset's training set. Solutions that fail to pass all the test cases are removed. Problems with no correct solution are also removed.
The solutions were executed on Intel E5-2620 v3 CPUs with the execution timeout set to 10 seconds.
Statistics in the training set
Dataset
# Problems
# Solutions
TACO
25443
1468722
TACO-verified
12898
1043251
Correct Ratio
50.69 %
71.03 %… See the full description on the dataset page: https://huggingface.co/datasets/likaixin/TACO-verified.TACO-hf
BEE-spoke-data/TACO-hf
Simple re-host of https://huggingface.co/datasets/BAAI/TACO but saved as hf dataset for ease of use.
Features:
DatasetDict({
"train": Dataset({
"features": [
"question",
"solutions",
"starter_code",
"input_output",
"difficulty",
"raw_tags",
"name",
"source",
"tags",
"skill_types",
"url",
"Expected Auxiliary… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/TACO-hf.thinking_taco_play_lerobot_output_qwen3vlterminal_bench_2_tasktrove_dq_taco_step15_30b_a3b_20260729_222705
Agent trace dataset
OpenCode/Harbor rollout traces from the MarinSkyRL run
rl-tasktrove-dq-sweep-30b-qwen3-coder-30-20260726-235656-574ba8, exported with
make_and_upload_trace_dataset --episodes last (the last episode of each trial — the rollouts
the policy was trained on).
Coverage
Built from the complete trial set on durable object storage, not from a local evidence bundle.
quantity
value
trial directories on object storage
21711
trials with a… See the full description on the dataset page: https://huggingface.co/datasets/laion/terminal_bench_2_tasktrove_dq_taco_step15_30b_a3b_20260729_222705.taco
View on Pictograph · Pictograph Research · Creative Commons Attribution 4.0
About
TACO is a computer-vision dataset curated and annotated on Pictograph. The most common detected objects are grass, sidewalk, bush, bottle, frisbee, ruins. On Pictograph you can browse every annotated image, fork it into your own workspace in one click, export it in a dozen formats, or train a model on it directly.
At a glance
Metric
Value
Images
1,500
Annotations
4… See the full description on the dataset page: https://huggingface.co/datasets/pictograph/taco.tacos-captioningalpaca_hebrew_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_hebrew_taco.taco_play_train_augmented
taco_play_train_augmented
Overview
Codebase version: v3.0
Robots: google_robot, images, jaco, kinova3, kuka_iiwa, sawyer, ur5e, widowX, xarm7
FPS: 15
Episodes: 3,242
Frames: 213,972
Splits:
train: 0:3242
Data Layout
data_path : data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
video_path: videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4
Features
Feature
dtype
shape
description… See the full description on the dataset page: https://huggingface.co/datasets/oxe-auge/taco_play_train_augmented.TACOThis is the dataset from http://tacodataset.org , cleaned up and repackaged.
The original is the COCO_format.zip, the Torch_format.zip is the same dataset transformed into torch format.
Both can be plugged directly into YOLO for fine tuning.
CVPR25-OSGNet-TACoS
TACoS Feature for OSGNet
This repository provides the TACoS features for Object-Shot Enhanced Grounding Network (OSGNet).
For installation, dataset preparation, training, and evaluation, please refer to the main repository:
GitHub: https://github.com/iLearn-Lab/CVPR25-OSGNet
Paper: https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Object-Shot_Enhanced_Grounding_Network_for_Egocentric_Video_CVPR_2025_paper.html
Checkpoints: https://huggingface.co/iLearn-Lab/CVPR25-OSGNet
TACO-Cobalt
TACO-Cobalt
Paper | GitHub
TACO-Cobalt is a cleaned version of that TACO code generation dataset crawled from programming competition websites.
Due to noise in the crawled data, we start from the TACO-verified subset and apply further cleaning to obtain 6,103 valid tasks, each with at least 8 test cases.
For each task, we sort the test cases’ difficulty by the possibility of Qwen2.5-Coder-7B-Instruct passing them out of 16 attempts.
We select the four easiest test cases as the… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/TACO-Cobalt.taco_play_test_augmented
taco_play_test_augmented
Overview
Codebase version: v3.0
Robots: google_robot, images, jaco, kinova3, kuka_iiwa, sawyer, ur5e, widowX, xarm7
FPS: 15
Episodes: 361
Frames: 23,826
Splits:
train: 0:361
Data Layout
data_path : data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
video_path: videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4
Features
Feature
dtype
shape
description
observation.images.google_robot… See the full description on the dataset page: https://huggingface.co/datasets/oxe-auge/taco_play_test_augmented.TACO
TACO Dataset
TACO is a benchmark for code generation with 26443 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications.
Key Update:
We remove and modified some test cases in test set. Please update to use the newest version.
Languages
The dataset contains questions in English and code solutions in Python.
Dataset Structure
from datasets import load_dataset
load_dataset("BAAI/TACO")… See the full description on the dataset page: https://huggingface.co/datasets/DONG19/TACO.alpaca_swedish_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_swedish_taco.alpaca_afrikaans_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_afrikaans_taco.testalpaca_sanskrit_tacoThis repo consists of the datasets used for the TaCo paper. There are four datasets:
Multilingual Alpaca-52K GPT-4 dataset
Multilingual Dolly-15K GPT-4 dataset
TaCo dataset
Multilingual Vicuna Benchmark dataset
We translated the first three datasets using Google Cloud Translation.
The TaCo dataset is created by using the TaCo approach as described in our paper, combining the Alpaca-52K and Dolly-15K datasets.
If you would like to create the TaCo dataset for a specific language, you can… See the full description on the dataset page: https://huggingface.co/datasets/saillab/testalpaca_sanskrit_taco.alpaca_hungarian_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_hungarian_taco.tacodemoalpaca_sesotho_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_sesotho_taco.TACO-Cobalt-PTB
TACO-Cobalt-PTB
Paper | GitHub
TACO-Cobalt is the perturbed version of the validation set in TACO-Cobalt to analyze in-context reward hacking behaviors of LLMs in code generation contexts.
For each coding problem, we randomly select two public tests (x_1, y_1) and (x_2, y_2) with distinct outputs (y_1 != y_2).
Then, we exchange their expected outputs and turn them into two perturbed tests (x_1, y_2) and (x_2, y_1), which are impossible for any correct program to pass.
If all public… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/TACO-Cobalt-PTB.taco_play_inpainting
taco_play robot-removal inpainting dataset
This dataset contains robot-removal inpainting results for taco_play. Each episode provides:
inpainting.mp4: the robot visually removed via inpainting
mask.mp4: the robot mask video used for inpainting
original_episode.mp4: the original (unmodified) episode video
language_instructions_{split}_all.txt: tab-separated mapping from episode_id to instruction
Relation to OXE-AugE
This release is produced as part of OXE-AugE… See the full description on the dataset page: https://huggingface.co/datasets/oxe-auge/taco_play_inpainting.Maya4alpaca_uyghur_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_uyghur_taco.BAAI-TACO-reuploadalpaca_french_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_french_taco.alpaca_dogri_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_dogri_taco.alpaca_shona_tacoThis repository contains the dataset used for the TaCo paper.
The dataset follows the style outlined in the TaCo paper, as follows:
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
Please refer to the paper for more details: OpenReview
If you have used our dataset, please cite it as follows:
Citation… See the full description on the dataset page: https://huggingface.co/datasets/saillab/alpaca_shona_taco.B_taco_clean_v1TACO_solutuon