Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- n<1K
+---
+
+
+# Dataset Card for MeowBench
+
+MeowBench is a high-fidelity, expert-verified quad-modal benchmark designed to evaluate Multimodal Large Language Models (MLLMs) on feline intention decoding. It is the official evaluation suite for the **Meow-Omni 1** model.
+
+
+### Dataset Summary
+MeowBench is designed to solve the challenge of **"semantic aliasing"** in animal behaviour. It provides a rigorous testing ground for models to determine if they can move beyond superficial pattern matching to genuine latent state reasoning by correlating external observational data (video/audio) with internal biological markers (ECG/EEG/IMU).
+
+## Uses
+
+### Direct Use
+- Benchmarking Multimodal Large Language Models on animal behaviour interpretation.
+- Evaluating a model's ability to ingest and reason over video, audio, and high-frequency biological time-series data.
+
+### Out-of-Scope Use
+- Real-world veterinary diagnosis without human oversight.
+- Direct application to non-feline species (unless testing for zero-shot transfer capabilities).
+
+## Dataset Structure
+
+Each sample in MeowBench is structured as a **Multiple Choice Question (MCQ)**:
+- **Input:** A synchronized (or intent-matched) triplet of Video, Audio, and Time-Series data.
+- **Question:** A natural language prompt asking for the animal's underlying intent (e.g., "Based on the provided biometrics and visual cues, is the subject exhibiting play-aggression or predatory intent?").
+- **Options:** One ground-truth intention label and three expert-curated distractors sampled from the broader intention collection.
+
+### Construction & Verification
+1. **Next-Behaviour Prediction (NBP) Logic:** Intent labels are derived from the behaviour immediately following a temporal transition point in the raw data.
+2. **Intent-Matched Synthesis:** Due to the scarcity of naturally synchronized quad-modal data, samples were synthesized by matching unimodal data sharing the same intent. 
+3. **Ethologist Audit:** To ensure biological and biomechanical plausibility, **eight Professional Feline Ethologists** manually reviewed every sample. Only samples where the biometric acceleration and heart-rate patterns were consistent with the audio-visual displays were retained.
+
+# 🔗 The Meow-Omni Ecosystem
+
+To facilitate reproducibility and further research in computational ethology, we have released the following components:
+
+* **Main Model:** [Meow-Omni 1](https://huggingface.co/smgjch/Meow-Omni-1) — The full fine-tuned quad-modal MLLM.
+* **Base Model:** [Meow-Omni 1-Base](https://huggingface.co/smgjch/Meow-Omni-1-Base) — The model weights prior to specific intent-alignment.
+* **Training Dataset:** [Meow-10K](https://huggingface.co/datasets/smgjch/meow-10k) — The synchronized 10k sample dataset used for training.
+
+## Citation
+
+Coming Soon.
+
+
+