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--- |
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license: apache-2.0 |
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task_categories: |
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- video-classification |
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- zero-shot-classification |
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language: |
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- en |
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tags: |
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- social-signals |
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- behavioral-analysis |
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- multimodal |
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- video |
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pretty_name: Wave1 Test Set - Social Signal Detection |
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size_categories: |
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- n<1K |
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--- |
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# Wave1 Test Set - Social Signal Detection |
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## Dataset Description |
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This dataset contains video clips annotated with social and affective signals for behavioral analysis tasks. Each sample includes: |
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- **System Prompt**: Instructions for behavioral analysis |
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- **Video URL**: Link to the video clip |
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- **Multi-labels**: Detected social signals from 12 categories |
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- **Video ID**: Unique identifier for the video |
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## Social Signal Categories |
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The dataset includes annotations for 12 social signals: |
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1. **Agreement**: Nods, verbal affirmations, affiliative smiles, posture mirroring |
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2. **Disagreement**: Opposition, interruptions, head shakes, eye rolls |
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3. **Engagement**: Sustained orientation, eye contact, forward-leaning posture |
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4. **Disengagement**: Gaze aversion, fidgeting, low-energy speech |
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5. **Confusion**: Puzzled facial display, increased blinking, clarification questions |
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6. **Hesitation**: Long gaps before speaking, filled pauses, restarts |
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7. **Uncertainty**: Hedged answers, rising final contour, shoulder shrugs |
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8. **Skepticism**: Questioning stance, delayed responses, furrowed brows |
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9. **Confidence**: Upright posture, sustained eye contact, clear speech |
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10. **Frustration**: Tension cues, higher pitch, sighs, complaints |
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11. **Interest**: Attentional engagement, widened eyes, faster speech |
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12. **Stress**: Self-touch, repetitive fidgeting, rapid blinking, vocal instability |
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## Data Format |
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The dataset is provided as a JSONL file where each line contains: |
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```json |
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{ |
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"messages": [ |
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{ |
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"content": "System prompt with task instructions...", |
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"role": "system" |
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}, |
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{ |
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"content": "<video>", |
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"role": "user" |
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}, |
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{ |
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"content": "", |
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"role": "assistant" |
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} |
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], |
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"videos": ["https://..."], |
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"multilabels": ["Signal1", "Signal2", ...], |
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"video_id": "unique_video_identifier.mp4" |
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} |
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``` |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load dataset |
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dataset = load_dataset("Interhuman/wave1-test-set", split="train") |
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# Or load the raw JSONL file |
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import json |
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with open("configs_wave1-test-set-dataset.jsonl", "r") as f: |
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data = [json.loads(line) for line in f] |
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# Access first sample |
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sample = data[0] |
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print(sample['video_id']) |
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print(sample['multilabels']) |
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print(sample['videos'][0]) |
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``` |
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## Dataset Statistics |
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- **Total Samples**: 50 |
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- **Multi-label Classification**: Each video can have multiple social signals |
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- **Signal Distribution**: Varies across samples |
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## Citation |
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If you use this dataset, please cite: |
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```bibtex |
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@dataset{interhuman_wave1_test, |
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title={Wave1 Test Set - Social Signal Detection}, |
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author={Interhuman}, |
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year={2024}, |
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publisher={Hugging Face} |
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} |
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``` |
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## License |
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Apache 2.0 |
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