datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.counterfactual-pendulum-multilingual
📌 Dataset Summary
When a Vision-Language Model (VLM) is given an image along with a text prompt containing contradictory or misleading information, how does it react? Does it rely on the visual evidence, succumb to textual bias, or honestly abstain when faced with unresolvable conflict?
This dataset adapts the Counterfactual Pendulum scenario across two visual conflict dimensions:
Angular (Angle): Conflict in the pendulum's angle of inclination.
Light: Conflict in the light… See the full description on the dataset page: https://huggingface.co/datasets/akanshjain37/counterfactual-pendulum-multilingual.code-conflict
Code Conflict Dataset
A dataset of 100 visual Python code conflict samples designed to evaluate Vision-Language Models (VLMs) under cross-modal conflicts (discrepancy between code screenshots and caption text).
Dataset Statistics
Total Rows: 100 samples
Language: English (english)
Categories: 5 distinct Python code conflict_types (20 samples per category):
operator_substitution (Rows 1–20): Swapping math or logic operators (e.g., + to -, == to !=, or to and).… See the full description on the dataset page: https://huggingface.co/datasets/akanshjain37/code-conflict.