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README.md
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---
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# Human Behavior Atlas Resources
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This repository hosts the official models and datasets released as part of the Human Behavior Atlas (HBA) project — a benchmark and foundation model ecosystem for unified multimodal social behavior understanding and reasoning.
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The resources include:
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- Unified behavioral foundation models
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- Specialized downstream behavioral adapters
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- Supervised fine-tuning checkpoints
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- The Human Behavior Atlas benchmark datasets
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---
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# Models
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## [OmniSapiens 2.0](https://huggingface.co/HumanBehaviorAtlas/OmniSapiens2.0)
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**Repository:** `HumanBehaviorAtlas/OmniSapiens2.0`
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**OmniSapiens 2.0** is the latest and strongest OmniSapiens foundation model for multimodal social behavior reasoning. The model is trained using **Heterogeneity-Aware Relative Policy Optimization (HARPO)** on the Human Behavior Atlas benchmark, enabling more balanced and consistent learning across diverse behavioral tasks and domains.
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The model supports unified reasoning over:
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- Emotion understanding
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- Humor detection
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- Sarcasm understanding
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- Sentiment analysis
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- Social reasoning
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- Behavioral interpretation
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This model represents the current state-of-the-art OmniSapiens release.
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---
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## [OmniSapiens SFT](https://huggingface.co/HumanBehaviorAtlas/omnisapiens_sft)
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**Repository:** `HumanBehaviorAtlas/omnisapiens_sft`
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**OmniSapiens SFT** is the supervised fine-tuned version of OmniSapiens trained on Human Behavior Atlas tasks using classification and QA-style supervision.
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This model serves as:
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- The supervised baseline prior to reasoning-RL training
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- The initialization model for subsequent RL-based OmniSapiens variants
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- A unified multimodal behavioral understanding model
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---
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## [BAM — Humour Detection Adapter](https://huggingface.co/HumanBehaviorAtlas/omnisapiens_bam_humour_detection)
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**Repository:** `HumanBehaviorAtlas/omnisapiens_bam_humour_detection`
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A specialized **Behavioral Adapter Module (BAM)** trained for multimodal humor detection tasks.
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This adapter is designed for downstream humor understanding applications while retaining the broader OmniSapiens behavioral representations.
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---
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## [BAM — Sarcasm Detection Adapter](https://huggingface.co/HumanBehaviorAtlas/omnisapiens_bam_sarcasm_detection)
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**Repository:** `HumanBehaviorAtlas/omnisapiens_bam_sarcasm_detection`
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A specialized **Behavioral Adapter Module (BAM)** optimized for sarcasm detection and sarcastic intent understanding across multimodal behavioral inputs.
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---
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## [BAM — Sentiment Polarity (MOSEI) Adapter](https://huggingface.co/HumanBehaviorAtlas/omnisapiens_bam_sentiment_polarity_mosei)
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**Repository:** `HumanBehaviorAtlas/omnisapiens_bam_sentiment_polarity_mosei`
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A specialized **Behavioral Adapter Module (BAM)** trained for sentiment polarity prediction on the MOSEI benchmark.
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The adapter supports multimodal sentiment understanding using behavioral and expressive cues.
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---
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# Datasets
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## [Human Behavior Atlas (Parquet Version)](https://huggingface.co/datasets/HumanBehaviorAtlas/human_behavior_atlas)
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**Repository:** `HumanBehaviorAtlas/human_behavior_atlas`
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The primary release of the **Human Behavior Atlas (HBA)** benchmark.
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This version is distributed in **Parquet format**, which is generally easier and faster to use with the Hugging Face `datasets` library and most downstream training pipelines.
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The benchmark contains diverse multimodal behavioral tasks spanning:
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- Emotion recognition
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- Humor understanding
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- Sarcasm detection
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- Sentiment analysis
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- Intent reasoning
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- Social reasoning
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- Mental health related behavioral understanding
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---
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## [Human Behavior Atlas (TAR Version)](https://huggingface.co/datasets/HumanBehaviorAtlas/human_behavior_atlas_tar)
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**Repository:** `HumanBehaviorAtlas/human_behavior_atlas_tar`
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An alternative release of the Human Behavior Atlas benchmark distributed in **TAR file format**.
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This version may be useful for:
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- Custom preprocessing pipelines
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- Streaming-based workflows
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- Manual media extraction
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- Large-scale storage systems
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Most users may find the Parquet release (`human_behavior_atlas`) easier to download and integrate directly into training workflows.
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