Add dataset card

README.md

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+---
+license: cc-by-nc-4.0
+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
+pretty_name: Human Behavior Atlas v2
+size_categories:
+  - 10K<n<100K
+configs:
+  - 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
+  splits:
+    - name: train
+      num_examples: 74449
+    - name: validation
+      num_examples: 7646
+    - name: test
+      num_examples: 18204
+---
+
+# Human Behavior Atlas v2
+
+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.
+
+## Dataset Summary
+
+| Property | Value |
+|---|---|
+| Total samples | 100,299 |
+| Train split | 74,449 |
+| Validation split | 7,646 |
+| Test split | 18,204 |
+| Source datasets | 16 |
+| Modalities | Text, Audio (.wav bytes), Video (.mp4 bytes), OpenSmile features (.pt bytes), Pose features (.pt bytes) — all embedded in parquet |
+| Languages | English, Chinese (CHSIMSv2) |
+| License | CC BY-NC 4.0 |
+
+## Modality Distribution
+
+| Modality Signature | Samples | Percentage |
+|---|---|---|
+| text_video_audio | 87,318 | 87.1% |
+| text_audio | 10,431 | 10.4% |
+| text | 2,550 | 2.5% |
+
+## Source Datasets
+
+| Dataset | Samples | Task | Modality | Description |
+|---|---|---|---|---|
+| **mosei_senti** | 22,740 | Sentiment classification | text_video_audio | CMU-MOSEI sentiment analysis (negative/neutral/positive) |
+| **intentqa** | 14,158 | Video QA | text_video_audio | Intent-driven video question answering |
+| **meld_senti** | 13,518 | Sentiment classification | text_video_audio | MELD multimodal sentiment (from Friends TV series) |
+| **meld_emotion** | 13,350 | Emotion classification | text_video_audio | MELD multimodal emotion recognition (7 classes) |
+| **mosei_emotion** | 8,545 | Emotion classification | text_video_audio | CMU-MOSEI emotion recognition (6 classes) |
+| **cremad** | 7,442 | Emotion classification | text_audio | CREMA-D acted emotional speech recognition |
+| **siq2** | 6,394 | Video QA | text_video_audio | Social IQ 2.0 social intelligence QA |
+| **chsimsv2** | 4,384 | Sentiment classification | text_video_audio | CH-SIMS v2 Chinese multimodal sentiment |
+| **tess** | 2,800 | Emotion classification | text_audio | Toronto Emotional Speech Set |
+| **urfunny** | 2,113 | Humor classification | text_video_audio | UR-Funny multimodal humor detection |
+| **mmpsy_depression** | 1,275 | Depression screening | text_video_audio | Multimodal depression assessment |
+| **mmpsy_anxiety** | 1,275 | Anxiety screening | text_video_audio | Multimodal anxiety assessment |
+| **mimeqa** | 801 | Video QA | text_video_audio | MIME gesture-based QA |
+| **mmsd** | 687 | Humor classification | text | Multimodal sarcasm detection (text only) |
+| **ptsd_in_the_wild** | 628 | PTSD detection | text_video_audio | PTSD detection from video interviews |
+| **daicwoz** | 189 | Depression screening | text_video_audio | DAIC-WOZ clinical depression interviews |
+
+## Task Types
+
+| Task ID | Description | Datasets |
+|---|---|---|
+| `emotion_cls` | Emotion classification | mosei_emotion, meld_emotion, cremad, tess |
+| `sentiment_cls` | Sentiment classification / regression | mosei_senti, meld_senti, chsimsv2 |
+| `humor_cls` | Humor and sarcasm detection | urfunny, mmsd |
+| `depression` | Depression screening | mmpsy_depression, daicwoz |
+| `anxiety` | Anxiety screening | mmpsy_anxiety |
+| `ptsd` | PTSD detection | ptsd_in_the_wild |
+| `video_qa` | Video question answering | intentqa, siq2, mimeqa |
+
+## Schema
+
+Each row in the Parquet files contains the following columns:
+
+| Column | Type | Description |
+|---|---|---|
+| `problem` | string | Prompt text with modality markers (`<audio>`, `<video>`) |
+| `answer` | string | Ground truth answer |
+| `audios` | list[bytes] | Raw .wav audio bytes (embedded) |
+| `videos` | list[bytes] | Raw .mp4 video bytes (embedded) |
+| `images` | list[bytes] | Image bytes (currently unused) |
+| `dataset` | string | Source dataset name |
+| `modality_signature` | string | Modality combination: `text_video_audio`, `text_audio`, or `text` |
+| `ext_video_feats` | list[bytes] | Pose estimation feature tensors (.pt bytes, embedded) |
+| `ext_audio_feats` | list[bytes] | OpenSmile audio feature tensors (.pt bytes, embedded) |
+| `task` | string | Task type identifier |
+| `class_label` | string | Classification label |
+
+## Repository Structure
+
+```
+sboughorbel/human_behavior_atlas_v2/
+  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
+
+```python
+from datasets import load_dataset
+
+# Stream without downloading everything
+ds = load_dataset("sboughorbel/human_behavior_atlas_v2", split="train", streaming=True)
+sample = next(iter(ds))
+
+# Load a subset
+ds_100 = load_dataset("sboughorbel/human_behavior_atlas_v2", split="train[:100]")
+
+# Filter by task or modality
+emotion_ds = ds_100.filter(lambda x: x["task"] == "emotion_cls")
+```
+
+### Accessing Embedded Media
+
+```python
+import io
+import soundfile as sf
+
+sample = ds_100[0]
+
+# Audio is raw bytes — decode with soundfile or torchaudio
+if sample["audios"]:
+    audio_data, sr = sf.read(io.BytesIO(sample["audios"][0]))
+
+# 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 sboughorbel/human_behavior_atlas_v2 \
+    --repo-type dataset --local-dir /path/to/data
+
+# Or download specific splits only
+huggingface-cli download sboughorbel/human_behavior_atlas_v2 \
+    --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' \
+    ...
+```
+
+## Citation
+
+If you use this dataset, please cite the original source datasets as appropriate. Key references include:
+
+- 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
+
+## 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.