Simplify dataset card and link to GitHub usage documentation

README.md

@@ -282,8 +282,6 @@ Dependencies are checked for necessity and non-leakage, and answers must be visu
 </table>
 </div>
 
-Main results from **Table 2** of the manuscript. All model scores use 128 fixed frames and sequential self-rollout. Scores are percentages; higher is better.
-
 ### Evaluation with fewer frames
 
 13 open-weight models evaluated with 64 or 32 fixed frames per video.
@@ -542,153 +540,7 @@ Main results from **Table 2** of the manuscript. All model scores use 128 fixed
 
 **Table 4** · Fixed frames and sequential self-rollout. Scores are percentages. [Compare frame counts on the project page →](https://videomembench.github.io/#frame-results)
 
-## Download and load
-
-The repository contains one evaluation split, `test`:
-
-```text
-annotations.jsonl        # 326 video-level dialogue records
-videos/
-  videomembench-1.mp4
-  ...
-  videomembench-326.mp4
-```
-
-### Load annotations
-
-```bash
-pip install huggingface_hub
-```
-
-```python
-import json
-from huggingface_hub import hf_hub_download
-
-annotation_path = hf_hub_download(
-    repo_id="VideoMemBench/data",
-    repo_type="dataset",
-    filename="annotations.jsonl",
-)
-with open(annotation_path, encoding="utf-8") as source:
-    dialogues = [json.loads(line) for line in source if line.strip()]
-
-print(len(dialogues))  # 326
-print(dialogues[0]["video_path"])
-print(dialogues[0]["dialogue"][0]["question"])
-```
-
-### Download videos for evaluation
-
-The full video collection is approximately **146.5 GB** (136.4 GiB). The following downloads the annotations and videos together:
-
-```python
-from huggingface_hub import snapshot_download
-
-data_dir = snapshot_download(
-    repo_id="VideoMemBench/data",
-    repo_type="dataset",
-    local_dir="data",
-    allow_patterns=["annotations.jsonl", "videos/*.mp4"],
-)
-```
-
-Downloads use your local Hugging Face authentication when access is required.
-
-## Data format
-
-`annotations.jsonl` is UTF-8 JSONL: each line contains one video and its ordered dialogue.
-
-| Field | Description |
-| --- | --- |
-| `video_id` | Unique video identifier, such as `videomembench-1`. |
-| `video_name` | Video filename, such as `videomembench-1.mp4`. |
-| `video_path` | Path relative to the repository root, such as `videos/videomembench-1.mp4`. |
-| `dialogue` | Questions in their original conversation order. |
-| `dialogue[].turn_id` | Turn identifier within the dialogue (`T1`, `T2`, ...). |
-| `dialogue[].type` | `open_structured`, `multi_choice_question`, or `true_or_false_question`. |
-| `dialogue[].question` | Question text. |
-| `dialogue[].options` | Lettered options for multiple-choice questions; `null` otherwise. |
-| `dialogue[].answer` | Option key, a True/False reference, or a free-form reference answer. |
-| `dialogue[].dependency` | `null` for an independent turn; otherwise the parent IDs and dependency types. |
-
-A dependency object contains `depends_on`, a list of earlier turn IDs, and `dep_type`, which maps each parent ID to `episodic` or `answer`. Preserve turn order during evaluation. Reference answers and dependency labels are evaluation metadata and must not be supplied to the answering model.
-
-<details>
-<summary><strong>Example record structure</strong></summary>
-
-The placeholder text below illustrates the structure and is not a benchmark question. JSONL stores each record on one line; it is expanded here for readability.
-
-```json
-{
-  "video_id": "videomembench-1",
-  "video_name": "videomembench-1.mp4",
-  "video_path": "videos/videomembench-1.mp4",
-  "dialogue": [
-    {
-      "turn_id": "T1",
-      "type": "open_structured",
-      "dependency": null,
-      "question": "<independent question>",
-      "options": null,
-      "answer": "<reference answer>"
-    },
-    {
-      "turn_id": "T2",
-      "type": "multi_choice_question",
-      "dependency": {
-        "depends_on": ["T1"],
-        "dep_type": {"T1": "episodic"}
-      },
-      "question": "<dependent question>",
-      "options": {"A": "<option A>", "B": "<option B>", "C": "<option C>", "D": "<option D>"},
-      "answer": "C"
-    }
-  ]
-}
-```
-
-</details>
-
-## Evaluation
-
-Use the [official evaluation code](https://github.com/Oliiveralien/VideoMemBench) with Python 3.11 or newer:
-
-```bash
-git clone https://github.com/Oliiveralien/VideoMemBench.git
-cd VideoMemBench
-pip install -e .
-```
-
-Place the downloaded files under `data/`. The Hub records include `video_path` for locating video files. The evaluation code resolves files from `video_name` and `--video-root`, and its strict validator does not accept the extra `video_path` field. Create an evaluation copy as follows:
-
-```python
-import json
-from pathlib import Path
-
-data_dir = Path("data")
-with (data_dir / "annotations.jsonl").open(encoding="utf-8") as source, \
-     (data_dir / "annotations.eval.jsonl").open("w", encoding="utf-8") as target:
-    for line in source:
-        if not line.strip():
-            continue
-        record = json.loads(line)
-        record.pop("video_path", None)
-        target.write(json.dumps(record, ensure_ascii=False) + "\n")
-```
-
-Validate the evaluation copy and prepare the shared 128-frame cache:
-
-```bash
-python -m videomembench.data data/annotations.eval.jsonl
-python -m videomembench.video \
-  --annotations data/annotations.eval.jsonl \
-  --video-root data/videos \
-  --frame-cache cache/f128
-```
-
-Set the annotation path to `data/annotations.eval.jsonl`, configure the model and judge endpoints in [`configs/evaluation.toml`](https://github.com/Oliiveralien/VideoMemBench/blob/main/configs/evaluation.toml), and follow the repository's inference and scoring instructions.
-
-The main evaluation uses 128 uniformly sampled, timestamped frames with a maximum side length of 512 pixels. Models answer turns sequentially while retaining their own full response history. Reference answers, dependencies, audio, and subtitles are not provided to the answering model. Open answers are graded by GPT-5.4 mini. See the [evaluation protocol](https://github.com/Oliiveralien/VideoMemBench/blob/main/docs/PROTOCOL.md) for complete details.
+For download and evaluation instructions, see the [GitHub documentation](https://github.com/Oliiveralien/VideoMemBench#readme).
 
 ## License and terms of use