README.md

---
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.