license: odc-by
language:
- en
size_categories:
- 100M<n<1B
configs:
- config_name: default
data_files:
- split: train
path: data/nn_*.parquet
Dataset Card for E-MM1-100M
Dataset Summary
E-MM1-100M is a large-scale multimodal dataset of 100M+ data groups, pairing data from five modalities: audio, image, video, point cloud, and text.
Each pair is a 5-tuple of a caption and an item from one of the four other modalities.
The data and captions are sourced from public data sources.
The dataset was created to advance work on joint embeddings for multimodal applications like cross-modal retrieval.
To visually explore the dataset, please visit our E-MM1 Explorer.
Dataset Structure
The data is structured in nearest-neighbor (NN) shards. The data directory contains the ith nearest neighbors of each caption in the data in ./data/nn_{i:02d}.parquet.
For example, the third nearest neighbor to all captions is in ./data/nn_03.parquet.
You can use this fact to, e.g., only load the nearest neighbor to each caption like this:
from datasets import load_dataset
# Full dataset (it's 15GB so perhaps best to stream)
ds = load_dataset("encord-team/E-MM1-100M", split="train", streaming=True)
# Load just the nearest neighbor
ds = load_dataset("encord-team/E-MM1-100M", split="train", data_files=["data/nn_01.parquet"])
The raw underlying data for this dataset can be downloaded by following the instructions on our GitHub page.
Dataset Splits
We provide two data splits:
- (this dataset) E-MM1-100M (automated): very large, built via nearest-neighbour retrieval for pre-training applications.
- E-MM1-1M (annotated): validated with high quality, human-verified annotations for post-training applications.
The E-MM1-100M split contains the large-scale dataset built with nearest-neighbour retrieval. For each of ~6.7M captions, we retrieved the top-16 nearest neighbours across all modalities, resulting in roughly 1B multimodal connections or 100M groups.
Data Schema
{
"caption": "String: Caption of the audio, image, points, text, or video.",
"dataset_license_audio": "(String) The license of the dataset that the audio belongs to. !! This is not the license of the audio file !!",
"dataset_license_image": "(String) The license of the dataset that the image belongs to. !! This is not the license of the image file !!",
"dataset_license_points": "(String) The license of the dataset that the points belongs to.",
"dataset_license_text": "(String) The license of the dataset that the text belongs to.",
"dataset_license_video": "(String) The license of the dataset that the video belongs to. !! This is not the license of the video file !!",
"encord_audio_id": "(Int64) Unique ID of the audio file (and segment).",
"encord_image_id": "(Int64) Unique ID of the image file.",
"encord_points_id": "(Int64) Unique ID of the points file.",
"encord_text_id": "(Int64) Unique ID of the text file.",
"encord_video_id": "(Int64) Unique ID of the video file (and segment).",
"end_time_audio": "(Int64) End time of the audio segment in seconds.",
"end_time_video": "(Int64) End time of the video segment in seconds.",
"file_id_audio": "(String) Audio identifier from the source dataset.",
"file_id_image": "(String) Image identifier from the source dataset.",
"file_id_points": "(String) 3D object identifier from the source dataset.",
"file_id_video": "(String) Video identifier from the source dataset.",
"file_name_audio": "(String) Filename of the audio file if downloaded with download script.",
"file_name_image": "(String) Filename of the image file if downloaded with download script.",
"file_name_points": "(String) Filename of the points file if downloaded with download script.",
"file_name_video": "(String) Filename of the video file if downloaded with download script.",
"nn_index": "(Int64) all items in the row arethe `nn_index` nearest neighbors to the caption.",
"save_folder_audio": "(String) Folder name of the audio file if downloaded with download script.",
"save_folder_image": "(String) Folder name of the image file if downloaded with download script.",
"save_folder_points": "(String) Folder name of the points file if downloaded with download script.",
"save_folder_video": "(String) Folder name of the video file if downloaded with download script.",
"source_dataset_audio": "(String) Source dataset of the audio file.",
"source_dataset_image": "(String) Source dataset of the image file.",
"source_dataset_points": "(String) Source dataset of the points file.",
"source_dataset_text": "(String) Source dataset of the text file.",
"source_dataset_video": "(String) Source dataset of the video file.",
"start_time_audio": "(Int64) Start time of the audio segment in seconds.",
"start_time_video": "(Int64) Start time of the video segment in seconds.",
"youtube_id_audio": "(String) Youtube ID of the audio file.",
"youtube_id_video": "(String) Youtube ID of the video file."
}
Additional Information
Usage Documentation
Please find more detailed usage instructions on Github. We have also created an interactive demonstration in Encord for visual exploration of a subset of the dataset, where you can find dataset tutorials as well.
Contact
For any questions about the creation of and applications for the dataset, please contact our team at [email protected].
Citation Information
@misc{broadbent2025ebindpracticalapproachspace,
title={{EBind}: a practical approach to space binding},
author={Jim Broadbent and Felix Cohen and Frederik Hvilshøj and Eric Landau and Eren Sasoglu},
year={2025},
eprint={2511.14229},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2511.14229},
}