datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TACOTACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.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.TACO-Benchmark
TACO-Benchmark
TACO (Text-to-SQL with Ambiguous and Cross-database Open-domain queries) is a benchmark for real-world data-lake Text-to-SQL.
📢 News (2026): TACO has been accepted to VLDB 2026! 🎉📄 Paper: arXiv:2606.14201
GitHub (code & evaluation): Akanezora0/TACO-Benchmark
Google Drive mirror: TACO-Benchmark.zip
Overview
Unlike Spider or BIRD — where the target database is known and schemas are clean — TACO evaluates systems on three challenges common in… See the full description on the dataset page: https://huggingface.co/datasets/Akanezora/TACO-Benchmark.TACOTACO is a benchmark for Python code generation, it includes 25443 problems and 1000 problems for train and test splits.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.errored_pythonThis is a subset of the python dataset provided but Ailurophile on Kaggle.
Important:Errors were introduced on purpose to try to test a sort of "specialized masking" in a realistic way.
Goal:The goal is to create a specialized agent, and add it to a chain with at least one other agent that generates code, and can hopefully "catch" any errors.
Inspiration:When working to generate datasets with other models, I found that even after multiple "passes" errors where still missed.
Out of curiosity… See the full description on the dataset page: https://huggingface.co/datasets/TacoPrime/errored_python.codegen_kodcode_lc2k_taco_merged
Dataset Card for Dataset Name
Merged likaixin/TACO-verified, Leon-Leee/LeetCodeDataset_rectified, and kodCode/KodCode-Light-RL-10K
Dataset Details
Dataset Description
Curated by: Leon (Me)
Funded by [optional]: AIGCode/Koting Intelligence
Language(s) (NLP): English
License: MIT (following GURU-92K)
Dataset Sources [optional]
Repository: stay tuned
Paper [optional]: stay tuned
Uses
Direct Use
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Leon-Leee/codegen_kodcode_lc2k_taco_merged.Taco