README.md

---
license: cc-by-nc-nd-4.0
library_name: transformers
pipeline_tag: video-text-to-text
tags:
- dense-video-captioning
- causal-reasoning
- video-understanding
---

# CausalVLM

**Dense Causal Captioning (DCC): joint dense event captioning + causal-graph prediction for video.**

CausalVLM produces timestamped dense event descriptions for a video and predicts a causal (prerequisite) graph over those events.

## Usage

```python
from huggingface_hub import snapshot_download
d = snapshot_download("causalvlm/CausalVLM")
import sys; sys.path.insert(0, d)
from modeling_causalvlm import CausalVLM

model = CausalVLM(d, device="cuda")
out = model.run("my_video.mp4")
for t, desc in out["events"]:
    print(f"[{t:.1f}s] {desc}")
print("causal edges:", out["causal_edges"])
```

## Example output (illustrative)

```
[t1s] <event 1 description>
[t2s] <event 2 description>
[t3s] <event 3 description>
causal edges: [(1, 2), (2, 3)]
```

Each `(i, j)` edge means event *i* is a prerequisite of event *j*.

## License
CC BY-NC-ND 4.0.

## Citation
```
(anonymous — under review)
```