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ReadMe.md

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----
-pretty_name: Human Behavior Atlas (HBA)
-language:
-- en
-license: other
-task_categories:
-- text-classification
-- audio-classification
-- image-classification
-- video-classification
-- multimodal
-tags:
-- human-behavior
-- social-intelligence
-- multimodal
-- benchmarking
-- psychology
-- affective-computing
-- emotion-recognition
-- intent-recognition
-- sarcasm-detection
-- depression-detection
-- anxiety-detection
-- ptsd
-- nonverbal-behavior
-- pose-estimation
-- opensmile
-size_categories:
-- 100K<n<1M
----
-
-# Human Behavior Atlas (HBA)
-
-Human Behavior Atlas (HBA) is a **unified benchmark for multimodal behavioral understanding**.  
-It aggregates and standardizes multiple behavioral datasets into a single training and evaluation framework, enabling consistent training and evaluation of foundation models on psychological and social behavior tasks (e.g., emotion, intent, sarcasm, mental health signals, nonverbal behavior).
-
-- **Dataset on Hugging Face:** https://huggingface.co/datasets/keentomato/human_behavior_atlas  
-- **Official GitHub:** https://github.com/MIT-MI/human_behavior_atlas/tree/main  
-- **Paper (ICLR 2026 Main Conference, accepted):** https://openreview.net/forum?id=ZKE23BBvlQ
-
----
-
-## What’s inside
-
-When you download the dataset, you will find:
-
-### 1) JSONL splits (centralized indices)
-These JSONL files sit at the **root level** and define all benchmark samples:
-- `final_v8_train_cleaned_2.jsonl` — training set  
-- `final_v8_val_cleaned.jsonl` — validation set  
-- `final_v8_test_cleaned.jsonl` — test set  
-
-Each line is a **self-contained sample** that provides:
-- the **prompt** (problem statement),
-- the **ground-truth** answer/label,
-- pointers to any **raw media** (video/audio/text/image),
-- pointers to any **pre-extracted behavioral features** (`.pt`).
-- miscellaneous information such as the dataset, task, class label, modalities present (i.e. modality signature)
-
-### 2) Raw media files
-Subdirectories contain the raw media referenced by each JSONL sample:
-- video / audio files
-- (optional) associated text files
-
-### 3) Behavioral feature files (`.pt`)
-Pre-extracted features for common behavioral signals, such as:
-- pose features (video)
-- OpenSMILE features (audio)
-- (and other feature types included in the release)
-
----
-
-## Data format
-
-HBA uses JSONL to provide a **unified sample schema** across heterogeneous source datasets.  
-All file paths are **relative to the dataset root** (the same directory as the JSONL files).
-
-### Example sample
-```json
-{
-  "problem": "<audio>\nDon't forget a jacket.\nThe 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": ["cremad_dataset_audio/1077_DFA_SAD_XX.wav"],
-  "dataset": "cremad",
-  "texts": [],
-  "modality_signature": "text_audio",
-  "ext_video_feats": ["pose/cremad_dataset_audio/1077_DFA_SAD_XX.pt"],
-  "ext_audio_feats": ["opensmile/cremad_dataset_audio/1077_DFA_SAD_XX.pt"],
-  "task": "emotion_cls",
-  "class_label": "sad"
-}
-```
-
----
-
-# Field-Level Explanation
-
-### Key fields:
-
-- **problem / answer** — the prompt and ground-truth label  
-- **images, videos, audios, texts** — relative paths to raw media files  
-- **ext_video_feats, ext_audio_feats** — relative paths to pre-extracted behavioral feature files (.pt)  
-- **modality_signature** — indicates which modalities are present (e.g., text_audio, video, text_video)  
-- **dataset** — source dataset name  
-- **task / class_label** — behavioral task type and label  
-
-### Detailed explanation:
-
-- `problem`: The full prompt presented to the model. May contain modality markers such as `<audio>` or `<video>` and includes the task instruction.
-- `answer`: The ground-truth label expected during evaluation.
-- `images`, `videos`, `audios`, `texts`: Lists of relative paths to raw media files stored in subdirectories under the dataset root. Empty lists indicate that the modality is not present.
-- `ext_video_feats`, `ext_audio_feats`: Lists of relative paths to pre-extracted behavioral feature tensors (`.pt` files), also stored in subdirectories under the dataset root.
-- `modality_signature`: A compact indicator of which modalities are present for the sample (e.g., `text_audio`, `video`, `text_video`).
-- `dataset`: The original source dataset name (e.g., `cremad`), enabling provenance tracking.
-- `task`: High-level behavioral task identifier (e.g., `emotion_cls`).
-- `class_label`: Canonical ground-truth class associated with the sample.
-
-The dataloader uses this JSONL as the centralized index to locate and load all raw media and feature files for each sample into the model.
-
----
-
-# Data Loading Workflow
-
-1. Read a JSONL entry.
-2. Parse `problem`, `task`, and `class_label`.
-3. Load raw media using relative paths.
-4. Optionally load `.pt` behavioral feature tensors.
-5. Construct the multimodal sample for training or evaluation.
-
-This unified indexing structure enables heterogeneous behavioral datasets to be standardized under a single multimodal evaluation framework.