Create README.md

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.