datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TABLET-Large
TABLET-Large
This is the Large sized train set of the TABLET dataset. It contains all train examples for all TABLET tasks, resulting in a total of 3,505,311 training examples across 17 tasks.This dataset is self-contained, each example includes a table image, its HTML representation, and the associated task data.However, if you're interested in downloading just the TABLET tables, check out TABLET-tables.
All TABLET Subsets:
(train) TABLET-Small: The smallest TABLET subset… See the full description on the dataset page: https://huggingface.co/datasets/alonsoapp/TABLET-Large.table_spill_cleanup_bimanual
Exylos Bimanual Spill Cleanup — Rich-Modality 50-Episode Sample
50 episodes of a bimanual Franka Panda wiping a liquid spill off a tabletop. Synthetic, VR-teleop demonstrations retargeted to two 7-DoF arms — 6 RGB views (3 with depth + segmentation), 6-DoF object poses, and a ground-truth dirty_fraction cleanliness signal, packaged in LeRobot v2.1.
Release note: this rich-modality v2 release replaces the original public 50-episode preview in place. The previous dataset… See the full description on the dataset page: https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual.evaluation-tables
[!CAUTION]
This dataset will not be updated. It corresponds to the last available public snapshot of the data, retrieved on July 28th, 2025.
elements_annotated_tables_4500_docs
Dataset
🚀 Progress
Last update (UTC): 2025-11-11 15:40:21Z
Documents processed: 4500 / 500058
Batches completed: 30
Total pages/rows uploaded: 89882
Latest batch summary
Batch index: 30
Docs in batch: 150
Pages/rows added: 1487
bird-dev-tablesArXiv-tables
Arxiv-tables Dataset
Dataset Summary
The Arxiv-tables dataset is a collection of tables extracted from scientific papers published on arXiv, primarily focused on ML papers. It includes both the LaTeX source of the tables and their corresponding rendered images from the PDF versions of the papers.
Supported Tasks
This dataset can support several tasks, including but not limited to:
Table structure recognition
LaTeX to image generation for tables
Image-to-LaTeX… See the full description on the dataset page: https://huggingface.co/datasets/staghado/ArXiv-tables.tpch_tables_scale_1
polars-tpch
This repo contains the code used for performance evaluation of polars. The benchmarks are TPC-standardised queries and data designed to test the performance of "real" workflows.
From the TPC website:
TPC-H is a decision support benchmark. It consists of a suite of business-oriented ad hoc queries and concurrent data modifications. The queries and the data populating the database have been chosen to have broad industry-wide relevance. This benchmark illustrates… See the full description on the dataset page: https://huggingface.co/datasets/kunishou/tpch_tables_scale_1.TABLET-Medium
TABLET-Medium
This is the Medium sized train set of the TABLET dataset. It contains the train examples for all TABLET tasks.Each task is capped at 140,000 examples, resulting in a total of 1,117,217 training examples across 17 tasks.This dataset is self-contained, each example includes a table image, its HTML representation, and the associated task data.However, if you're interested in downloading just the TABLET tables, check out TABLET-tables.
All TABLET Subsets:
(train)… See the full description on the dataset page: https://huggingface.co/datasets/alonsoapp/TABLET-Medium.table-vqa
Dataset description
The table-vqa Dataset integrates images of tables from the dataset AFTdb (Arxiv Figure Table Database) curated by cmarkea.
This dataset consists of pairs of table images and corresponding LaTeX source code, with each image linked to an average of ten questions and answers. Half of the Q&A pairs are in English and the other half in French. These questions and answers were generated using Gemini 1.5 Pro and Claude 3.5 sonnet, making the dataset well-suited for… See the full description on the dataset page: https://huggingface.co/datasets/cmarkea/table-vqa.tabfactbeaver-table
Dataset Card for beaver-table
Homepage and leaderboard |
Github repository |
Paper
Beaver is a holistic framework for evaluating performance on complex, private‑enterprise text‑to‑SQL tasks.
This repository includes the full collection of tables. Each table contains:
db: ID of the database the table belongs to
table_name: name of the table in the database
column_names: names of the columns in the table
column_types: data types of the columns in the table
example_rows: example rows… See the full description on the dataset page: https://huggingface.co/datasets/beaverbench/beaver-table.dclm-14b-c4-rewrriten-table-prompt-14b-retokbimanual-table-cleanup-cross-embodiment-rich-modality-sample
Cross-Embodiment Bimanual Table Cleanup — Rich-Modality 10-Episode Inspection Sample
10 full-modality cross-embodiment bimanual table-cleanup episodes: 5 Franka Panda + 5 WidowXAI, 21,267 frames, 6 RGB views per robot, task-camera depth and segmentation, native robot state/action, end-effector trajectories, 6-DoF object poses, and QA annotations.
✅ Use it / ❌ Skip it
Use it for
Inspecting loaders, schemas, camera coverage, depth, segmentation, object poses… See the full description on the dataset page: https://huggingface.co/datasets/ExylosAi/bimanual-table-cleanup-cross-embodiment-rich-modality-sample.dclm-6.7b-c4-rewrriten-table-prompt-6.7b-retokspider-tableQA
Dataset Card for "spider-tableQA"
Usage
import pandas as pd
from datasets import load_dataset
spider_tableQA = load_dataset("vaishali/spider-tableQA")
for sample in spider_tableQA['train']:
question = sample['question']
sql_query = sample['query']
input_table_names = sample["table_names"]
input_tables = [pd.read_json(table, orient='split') for table in sample['tables']]
answer = pd.read_json(sample['answer'], orient='split')
# flattened input/output… See the full description on the dataset page: https://huggingface.co/datasets/vaishali/spider-tableQA.TableVQA-Bench
Dataset Card for "TableVQA-Bench"
More Information needed
dclm-14b-c4-rewrriten-table-prompt-14b-dpoed-retokdclm-6.7b-c4-rewrriten-table-prompt-6.7b-dpoed-retokVisual-TableQA
🧠 Visual-TableQA: Open-Domain Benchmark for Reasoning over Table Images
Welcome to Visual-TableQA, a project designed to generate high-quality synthetic question-answer datasets associated to images of tables. This resource is ideal for training and evaluating models on visually-grounded table understanding tasks such as document QA, table parsing, and multimodal reasoning.
🚀 Latest Update
We have refreshed the dataset with newly generated QA pairs created by… See the full description on the dataset page: https://huggingface.co/datasets/AI-4-Everyone/Visual-TableQA.TABLET-Small
TABLET-Small
This is the Small sized train set of the TABLET dataset. It contains the train examples for 14 TABLET tasks.Each task is capped at 140,000 examples, resulting in a total of 776,602 training examples across 14 tasks.This dataset is self-contained, each example includes a table image, its HTML representation, and the associated task data.However, if you're interested in downloading just the TABLET tables, check out TABLET-tables.
All TABLET Subsets:
(train)… See the full description on the dataset page: https://huggingface.co/datasets/alonsoapp/TABLET-Small.tottotable_spill_cleanup_bimanual_rgbd_segmentation_poses
Exylos Bimanual Table Spill Cleanup Rich-Modality Sample
A compact, rich-modality bimanual robot manipulation dataset for tabletop spill cleanup.
Each episode combines synchronized dual-arm Panda state/action trajectories, 7 RGB camera streams, per-frame depth maps, per-frame segmentation masks, object pose streams, phase annotations, and an objective cleanup success metric based on the remaining spill fraction.
This dataset is a rich-modality inspection sample for the Exylos… See the full description on the dataset page: https://huggingface.co/datasets/ExylosAi/table_spill_cleanup_bimanual_rgbd_segmentation_poses.text_table_md_v0table-image-html-pairsnutrition-table-detection
Open Food Facts Nutrition table detection dataset
This dataset was used to train the nutrition table object detection model running in production at Open Food Facts.
Images were collected from the Open Food Facts database and labeled manually.
Just like the original images, the images in this dataset are licensed under the Creative Commons Attribution Share Alike license (CC-BY-SA 3.0).
Fields
image_id: Unique identifier for the image, generated from the barcode and… See the full description on the dataset page: https://huggingface.co/datasets/openfoodfacts/nutrition-table-detection.table-vqa_beirThis is a copy of https://huggingface.co/datasets/jinaai/table-vqa reformatted into the BEIR format. For any further information like license, please refer to the original dataset.
Disclaimer
This dataset may contain publicly available images or text data. All data is provided for research and educational purposes only. If you are the rights holder of any content and have concerns regarding intellectual property or copyright, please contact us at "support-data (at) jina.ai" for… See the full description on the dataset page: https://huggingface.co/datasets/jinaai/table-vqa_beir.language_table_simagibot-sim-clear-table-in-the-restaurantThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "a2d",
"total_episodes": 102,
"total_frames": 84901,
"total_tasks": 1,
"total_videos": 306,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30.0,
"splits": {
"train": "0:102"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/bot-pi/agibot-sim-clear-table-in-the-restaurant.TABLET-teststeerable-scene-generation-dimsum-table