update dataset card

README.md

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 license: mit
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+# AVGen-Bench Generated Videos Data Card
+
+## Overview
+
+This data card describes the generated audio-video outputs organized under `generated_videos/` in AVGen-Bench.
+
+The collection is intended for **benchmarking and qualitative/quantitative evaluation** of text-to-audio-video (T2AV) systems. It is not a training dataset. Each item is a model-generated video produced from a prompt defined in `prompts/*.json`.
+
+Code repository: https://github.com/microsoft/AVGen-Bench
+
+## What This Dataset Contains
+
+The dataset is organized by:
+
+1. Model directory
+2. Video category
+3. Generated `.mp4` files
+
+A typical top-level structure is:
+
+```text
+AVGen-Bench/
+├── generated_videos/
+│   ├── Kling_2.6/
+│   ├── LTX-2/
+│   ├── LTX-2.3/
+│   ├── MOVA_360p_Emu3.5/
+│   ├── MOVA_360p_NanoBanana_2/
+│   ├── Ovi_11/
+│   ├── Seedance_1.5_pro/
+│   ├── Sora_2/
+│   ├── Veo_3.1_fast/
+│   ├── Veo_3.1_quality/
+│   ├── Wan_2.2_HunyuanVideo-Foley/
+│   └── Wan_2.6/
+├── prompts/
+└── generated_images/   # optional, depending on generation pipeline
+```
+
+Within each model directory, videos are grouped by category, for example:
+
+```text
+generated_videos/veo3.1_fast/
+├── ads/
+├── animals/
+├── asmr/
+├── chemical_reaction/      
+├── cooking/
+├── gameplays/
+├── movie_trailer/
+├── musical_instrument_tutorial/
+├── news/
+├── physical_experiment/
+└── sports/
+```
+
+## Prompt Coverage
+
+Prompt definitions are stored in `prompts/*.json`.
+
+The current prompt set contains **235 prompts** across **11 categories**:
+
+| Category | Prompt count |
+|---|---:|
+| `ads` | 20 |
+| `animals` | 20 |
+| `asmr` | 20 |
+| `chemical_reaction` | 20 |
+| `cooking` | 20 |
+| `gameplays` | 20 |
+| `movie_trailer` | 20 |
+| `musical_instrument_tutorial` | 35 |
+| `news` | 20 |
+| `physical_experiment` | 20 |
+| `sports` | 20 |
+
+Prompt JSON entries typically contain:
+
+- `content`: a short content descriptor used for naming or indexing
+- `prompt`: the full generation prompt
+
+
+## Data Instance Format
+
+Each generated item is typically:
+
+- A single `.mp4` file
+- Containing model-generated video and, when supported by the model/pipeline, synthesized audio
+- Stored under `generated_videos/<model>/<category>/`
+
+The filename is usually derived from prompt content after sanitization. Exact naming may vary by generation script or provider wrapper.
+In the standard export pipeline, the filename is derived from the prompt's `content` field using the following logic:
+
+```python
+def safe_filename(name: str, max_len: int = 180) -> str:
+    name = str(name).strip()
+    name = re.sub(r"[/\\:*?\"<>|\\n\\r\\t]", "_", name)
+    name = re.sub(r"\\s+", " ", name).strip()
+    if not name:
+        name = "untitled"
+    if len(name) > max_len:
+        name = name[:max_len].rstrip()
+    return name
+```
+
+So the expected output path pattern is:
+
+```text
+generated_videos/<model>/<category>/<safe_filename(content)>.mp4
+```
+
+## How The Data Was Produced
+
+The videos were generated by running different T2AV systems on a shared benchmark prompt set.
+
+Important properties:
+
+- All systems are evaluated against the same category structure
+- Outputs are model-generated rather than human-recorded
+- Different models may expose different generation settings, resolutions, or conditioning mechanisms
+- Some pipelines may additionally use first-frame or reference-image inputs, depending on the underlying model
+
+## Intended Uses
+
+This dataset is intended for:
+
+- Benchmarking T2AV generation systems
+- Running AVGen-Bench evaluation scripts
+- Comparing failure modes across models
+- Qualitative demo curation
+- Error analysis by category or prompt type
+
+## Out-of-Scope Uses
+
+This dataset is not intended for:
+
+- Training a general-purpose video generation model
+- Treating model outputs as factual evidence of real-world events
+- Safety certification of a model without additional testing
+- Any claim that benchmark performance fully captures downstream deployment quality
+
+## Known Limitations
+
+- Outputs are synthetic and inherit the biases and failure modes of the generating models
+- Some categories emphasize benchmark stress-testing rather than natural real-world frequency
+- File availability may vary across models if a generation job failed, timed out, or was filtered
+- Different model providers enforce different safety and moderation policies; some prompts may be rejected during provider-side review, which can lead to missing videos for specific models even when the prompt exists in the benchmark
+
+
+## Risks and Responsible Use
+
+Because these are generated videos:
+
+- Visual realism does not imply factual correctness
+- Audio may contain artifacts, intelligibility failures, or misleading synchronization
+- Generated content may reflect stereotypes, implausible causal structure, or unsafe outputs inherited from upstream models
+
+Anyone redistributing results should clearly label them as synthetic model outputs.
+
+
+