---
language:
  - en
  - zh
multilinguality:
  - multilingual
task_categories:
  - video-text-to-text
  - question-answering
tags:
  - video-understanding
  - drama
  - long-video
  - reasoning
  - sft
  - rl
size_categories:
  - 10K<n<100K
license: cc-by-nc-4.0
---

# MDrama: Drama Video Understanding Dataset

## Dataset Summary

- **32,361 QA annotations** over **8,102 short-drama video clips** (~52 GB, videos NOT included in this repo).
- Each clip has ~4 annotations: 1 caption/summary + 3 QA (multiple-choice or open-ended).
- Source videos are YouTube short dramas; use the `url`, `start_time`, `end_time` fields to retrieve each clip yourself.

## Fields

| Field | Description |
|---|---|
| `qid` | Unique question id (`train_0` .. `train_32360`). |
| `video_id` | Safe video identifier (`video_00000` .. `video_08101`). |
| `question` | Question text (options excluded). |
| `candidates` | Options list for MC questions (`A. xxx` prefixed); `null` for open-ended. |
| `answer` | MC: option letter; caption/summary: reference summary text. |
| `thinking` | Reference chain-of-thought reasoning. |
| `caption` | Chronological shot-by-shot caption of the clip. |
| `task_type` | Task category (e.g., `caption`, `Character-related-Character Identification`, ...). |
| `q_type` | `MC` / `OE` / `summary`. |
| `url` / `start_time` / `end_time` | YouTube source of the clip. |
| `stage` | Training stage this sample is used in: `SFT` (all 32,361 samples) or `SFT+RL` (the 14,135 samples additionally used for RL). |

## Test Set

- **`test` split**: 2,075 QA annotations over **1,036 unique clips** (no overlap with train).
- `qid`: `test_0` .. `test_2074`; `video_id`: `test_video_00000` .. `test_video_01035`.
- QA only (1,562 MC + 513 OE); `thinking` is `null` and `stage` is `"test"`.
- `url` / `start_time` / `end_time` were **manually verified and aligned** (as of 2026-09): every clip was checked against its YouTube source, and mis-aligned or dead links were corrected with replacement sources where available. Note that short-drama uploads are frequently taken down: ~9% of the URLs may be dead again at any given time — use the `url` + timestamps to locate re-uploads when a link fails.

## Ground-Truth Scene Graphs

`gt_graphs.json` (~40 MB) provides a ground-truth scene graph for every **train** clip, keyed by `video_id` (`video_00000` .. `video_08101`, 8,102 entries):

```json
{
  "video_00000": {
    "nodes": [{"id": "n1", "type": "Scene", "name": "Chinese-style corridor"}, ...],
    "edges": [{"source": "n2", "relation": "AGENT_OF", "target": "n4"}, ...]
  }
}
```

Graphs were parsed from the reference `caption` of each clip by Qwen3-14B with the SAGA graph-construction prompt: nodes typed `Character` / `Event` / `Prop` / `Scene`, semantic edges plus `NEXT_EVENT` temporal edges. They are the alignment target of SAGA's graph-alignment reward (`R_graph = 0.725 * F1_semantic + 0.275 * F1_structural`). The test split has no graphs (they are only needed as training reward targets).

## Usage

```python
import json
from datasets import load_dataset
from huggingface_hub import hf_hub_download

ds = load_dataset("yixin1121/M-Drama")
train = ds["train"]
test = ds["test"]

rl_subset = train.filter(lambda x: x["stage"] == "SFT+RL")

gt_graphs = json.load(open(hf_hub_download(
    "yixin1121/M-Drama", "gt_graphs.json", repo_type="dataset")))
graph = gt_graphs[train[0]["video_id"]]
```

## License

The annotations are released under the [Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/).

The dataset only contains annotations (questions, answers, reasoning, and captions). The source videos belong to their respective copyright holders and are NOT distributed with this dataset.