yixin1121/M-Drama

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):

{
  "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

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).

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.