Link OmniSapiens paper, update task categories and improve metadata

README.md

@@ -1,78 +1,85 @@
 ---
+language:
+- en
+- zh
 license: cc-by-nc-4.0
+size_categories:
+- 10K<n<100K
 task_categories:
-  - video-classification
-  - audio-classification
-  - text-classification
-  - question-answering
-  - visual-question-answering
-language:
-  - en
-  - zh
-tags:
-  - multimodal
-  - emotion-recognition
-  - sentiment-analysis
-  - humor-detection
-  - mental-health
-  - video-qa
-  - reinforcement-learning
-  - verl
-  - rl-training
-  - qwen2.5-omni
-  - audio
-  - video
-  - pose-estimation
-  - opensmile
+- video-classification
+- audio-classification
+- text-classification
+- question-answering
+- visual-question-answering
+- any-to-any
 pretty_name: Human Behavior Atlas v2
-arxiv: 2510.04899
-size_categories:
-  - 10K<n<100K
+tags:
+- multimodal
+- emotion-recognition
+- sentiment-analysis
+- humor-detection
+- mental-health
+- video-qa
+- reinforcement-learning
+- verl
+- rl-training
+- qwen2.5-omni
+- audio
+- video
+- pose-estimation
+- opensmile
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: train-*.parquet
-      - split: validation
-        path: validation-*.parquet
-      - split: test
-        path: test-*.parquet
+- config_name: default
+  data_files:
+  - split: train
+    path: train-*.parquet
+  - split: validation
+    path: validation-*.parquet
+  - split: test
+    path: test-*.parquet
 dataset_info:
   features:
-    - name: problem
-      dtype: string
-    - name: answer
-      dtype: string
-    - name: images
-      sequence: binary
-    - name: videos
-      sequence: binary
-    - name: audios
-      sequence: binary
-    - name: dataset
-      dtype: string
-    - name: modality_signature
-      dtype: string
-    - name: ext_video_feats
-      sequence: binary
-    - name: ext_audio_feats
-      sequence: binary
-    - name: task
-      dtype: string
-    - name: class_label
-      dtype: string
+  - name: problem
+    dtype: string
+  - name: answer
+    dtype: string
+  - name: images
+    sequence: binary
+  - name: videos
+    sequence: binary
+  - name: audios
+    sequence: binary
+  - name: dataset
+    dtype: string
+  - name: modality_signature
+    dtype: string
+  - name: ext_video_feats
+    sequence: binary
+  - name: ext_audio_feats
+    sequence: binary
+  - name: task
+    dtype: string
+  - name: class_label
+    dtype: string
   splits:
-    - name: train
-      num_examples: 74449
-    - name: validation
-      num_examples: 7646
-    - name: test
-      num_examples: 18204
+  - name: train
+    num_examples: 74449
+  - name: validation
+    num_examples: 7646
+  - name: test
+    num_examples: 18204
 ---
 
 # Human Behavior Atlas
 
-A large-scale multimodal dataset for human behavior understanding, spanning emotion recognition, sentiment analysis, humor detection, mental health screening, and video question answering. The dataset integrates 16 source datasets into a unified schema with audio, video, and pre-extracted features, designed for reinforcement learning training with the [verl](https://github.com/volcengine/verl) framework and multimodal language models such as Qwen2.5-Omni-7B.
+A large-scale multimodal dataset for human behavior understanding, spanning emotion recognition, sentiment analysis, humor detection, mental health screening, and video question answering. The dataset integrates 16 source datasets into a unified schema with audio, video, and pre-extracted features.
+
+This dataset was used to train **OmniSapiens**, a foundation model for social behavior processing.
+
+- **Papers:** 
+  - [Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding (ICLR 2026)](https://arxiv.org/abs/2510.04899)
+  - [OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization (ICML 2026)](https://huggingface.co/papers/2602.10635)
+- **Repository:** [https://github.com/MIT-MI/human_behavior_atlas](https://github.com/MIT-MI/human_behavior_atlas)
 
 ## Dataset Summary
 
@@ -146,19 +153,6 @@ Each row in the Parquet files contains the following columns:
 | `task` | string | Task type identifier |
 | `class_label` | string | Classification label |
 
-## Repository Structure
-
-```
-HumanBehaviorAtlas/human_behavior_atlas/
-  train-00000-of-XXXXX.parquet    # Sharded parquet with embedded audio/video
-  train-00001-of-XXXXX.parquet
-  ...
-  validation-*.parquet
-  test-*.parquet
-```
-
-All data — including audio, video, and pre-extracted features — is fully embedded in the parquet files. No separate downloads or extraction needed.
-
 ## Usage
 
 ### Loading with HuggingFace Datasets
@@ -172,9 +166,6 @@ sample = next(iter(ds))
 
 # Load a subset
 ds_100 = load_dataset("HumanBehaviorAtlas/human_behavior_atlas", split="train[:100]")
-
-# Filter by task or modality
-emotion_ds = ds_100.filter(lambda x: x["task"] == "emotion_cls")
 ```
 
 ### Accessing Embedded Media
@@ -192,46 +183,28 @@ if sample["audios"]:
 # Video is raw bytes — decode with decord, opencv, or write to temp file
 if sample["videos"]:
     video_bytes = sample["videos"][0]
-    # e.g., with decord:
-    # from decord import VideoReader
-    # vr = VideoReader(io.BytesIO(video_bytes))
 ```
 
-### Download and Setup
-
-```bash
-# Download full dataset
-huggingface-cli download HumanBehaviorAtlas/human_behavior_atlas \
-    --repo-type dataset --local-dir /path/to/data
-
-# Or download specific splits only
-huggingface-cli download HumanBehaviorAtlas/human_behavior_atlas \
-    --repo-type dataset --local-dir /path/to/data \
-    --include "train-*.parquet"
-```
-
-### Integration with verl RL Training
-
-This dataset is designed for RL training with [verl](https://github.com/volcengine/verl) using Qwen2.5-Omni-7B. The `problem` field contains structured prompts with `<audio>` and `<video>` modality markers. Audio and video bytes are loaded directly from parquet — no path resolution needed.
-
-All data including feature tensors is embedded directly in the parquet files.
-
-```bash
-# verl training config
-python3 -m verl.trainer.main_ppo \
-    data.train_files=/path/to/data/train-*.parquet \
-    data.val_files=/path/to/data/validation-*.parquet \
-    data.prompt_key=problem \
-    data.image_key=images \
-    data.video_key=videos \
-    data.modalities='audio,videos' \
-    ...
+### Example Entry
+
+```json
+{
+  "problem": "<audio>
+Don't forget a jacket.
+The above is a speech recording along with the transcript from a clinical context. What emotion is the speaker expressing? Answer with one word from the following: anger, disgust, fear, happy, neutral, sad",
+  "answer": "sad",
+  "images": [],
+  "videos": [],
+  "audios": ["..."],
+  "dataset": "cremad",
+  "modality_signature": "text_audio",
+  "task": "emotion_cls",
+  "class_label": "sad"
+}
 ```
 
 ## Citation
 
-If you use this dataset, please cite the following paper:
-
 ```bibtex
 @inproceedings{
 ong2026human,
@@ -241,18 +214,15 @@ booktitle={The Fourteenth International Conference on Learning Representations},
 year={2026},
 url={https://openreview.net/forum?id=ZKE23BBvlQ}
 }
-```
-
-> Keane Ong, Wei Dai, Carol Li, Dewei Feng, Hengzhi Li, Jingyao Wu, Jiaee Cheong, Rui Mao, Gianmarco Mengaldo, Erik Cambria, Paul Pu Liang. "Human Behavior Atlas: Benchmarking Unified Psychological and Social Behavior Understanding." ICLR 2026. [Paper](https://openreview.net/pdf?id=ZKE23BBvlQ)
 
-Please also cite the individual source datasets as appropriate:
-
-- CMU-MOSEI: Zadeh et al., "Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph", ACL 2018
-- MELD: Poria et al., "MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations", ACL 2019
-- CREMA-D: Cao et al., "CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset", IEEE TAC 2014
-- DAIC-WOZ: Gratch et al., "The Distress Analysis Interview Corpus of Human and Computer Interviews", LREC 2014
-- CH-SIMS v2: Liu et al., "Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module", ICMI 2022
+@article{ong2026omnisapiens,
+  title={Omnisapiens: A foundation model for social behavior processing via heterogeneity-aware relative policy optimization},
+  author={Ong, Keane and Boughorbel, Sabri and Xiao, Luwei and Ekbote, Chanakya and Dai, Wei and Qu, Ao and Wu, Jingyao and Mao, Rui and Hoque, Ehsan and Cambria, Erik and others},
+  journal={arXiv preprint arXiv:2602.10635},
+  year={2026}
+}
+```
 
 ## License
 
-This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license. Individual source datasets may have their own licensing terms; please consult the original dataset publications for details.
+This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license. Individual source datasets may have their own licensing terms.