Update README

README.md

@@ -1,9 +1,14 @@
 ---
 license: odc-by
 language:
-- en
+  - en
 size_categories:
-- 100M<n<1B
+  - 100M<n<1B
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "data/nn_*.csv"
 ---
 
 # Dataset Card for E-MM1-100M
@@ -12,7 +17,7 @@ size_categories:
 
 ## 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. 
+**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](https://github.com/encord-team/E-MM1/blob/main/SOURCE_DATASETS.md).
 The dataset was created to advance work on joint embeddings for multimodal applications like cross-modal retrieval. <br>
@@ -24,29 +29,61 @@ To visually explore the dataset, please visit our [E-MM1 Explorer](https://data.
 ## 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](https://huggingface.co/datasets/encord-team/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. 
+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
 
-| Column   | Type    |   Description | 
-| -------- | ------- |   -------     |
-| `encord_{modality}_id`  | Integer  | Unique ID for a specific file in that (dataset,modality) combination |
-| `save_folder`           | String   | Relative folder under your chosen root where the asset is stored. |
-| `file_name`             | String   | Filename of the asset |
-| `encord_text_id`        | Integer  | ID of the caption row |
-| `caption`               | String   | The caption text |
-
+```json
+{
+  "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](https://github.com/encord-team/E-MM1).
-We have also created an [interactive demonstration](data.encord.com) in Encord for visual exploration of the dataset, where you can find tutorials about the dataset.
+We have also created an [interactive demonstration](data.encord.com) in Encord for visual exploration of a subset of the dataset, where you can find dataset tutorials as well.
 
 ### Contact
 
@@ -60,4 +97,4 @@ title={EBind: A Practical Approach To Space Binding},
 author={Broadbent, Jim and Cohen, Felix and Hvilshøj, Frederik and Landau, Eric and Sasoglu, Eren}
 year={2025}
 }
-```
+```