Add task category, tags, and sample usage section

This PR enhances the dataset card by adding:
- `task_categories: - video-text-to-text` to the YAML metadata for accurate categorization.
- Relevant `tags` (`video-understanding`, `large-video-language-models`, `lvlm`, `positional-bias`, `benchmark`, `evaluation`) to improve discoverability.
- A detailed "🚀 Sample Usage" section directly from the project's GitHub README, including steps for dataset preparation, running inference with various models (InternVL3, MiMo-VL, GLM-4.5V), and metric calculation. This provides actionable code snippets for users to quickly get started with the dataset.

README.md

@@ -1,11 +1,23 @@
 ---
+language:
+- en
 license: cc-by-nc-sa-4.0
-extra_gated_prompt: >-
-  You acknowledge and understand that: This dataset is provided solely for
-  academic research purposes. It is not intended for commercial use or any other
-  non-research activities. All copyrights, trademarks, and other intellectual
-  property rights related to the videos in the dataset remain the exclusive
-  property of their respective owners. 
+size_categories:
+- 1K<n<10K
+task_categories:
+- video-text-to-text
+tags:
+- video-understanding
+- large-video-language-models
+- lvlm
+- positional-bias
+- benchmark
+- evaluation
+extra_gated_prompt: 'You acknowledge and understand that: This dataset is provided
+  solely for academic research purposes. It is not intended for commercial use or
+  any other non-research activities. All copyrights, trademarks, and other intellectual
+  property rights related to the videos in the dataset remain the exclusive property
+  of their respective owners. '
 configs:
 - config_name: default
   data_files:
@@ -39,10 +51,6 @@ dataset_info:
     num_examples: 1177
   download_size: 224148
   dataset_size: 490082
-language:
-- en
-size_categories:
-- 1K<n<10K
 ---
 
 <h1 align="center">Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models</h1>
@@ -93,6 +101,61 @@ Video-LevelGauge encompasses six categories of structured video understanding ta
 ## 🔍 Dataset
 The annotation file and the raw videos are readily accessible via this [HF Link](https://huggingface.co/datasets/Cola-any/Video-LevelGauge) 🤗. Note that this dataset is for research purposes only and you must strictly comply with the above License.
 
+## 🚀 Sample Usage
+
+To quickly get started with running inference and evaluating models on Video-LevelGauge, follow these steps. For more detailed instructions and examples, please refer to the [GitHub repository](https://github.com/Cola-any/Video-LevelGauge).
+
+### ✨ Clone and Prepare Dataset
+First, please clone this repository and download [our dataset](https://huggingface.co/datasets/Cola-any/Video-LevelGauge/tree/main/LevelGauge) into `./LevelGauge`, organizing it as follows:
+```
+Video-LevelGauge
+├── asset
+├── evaluation
+├── LevelGauge
+│   ├── json
+│   └── videos
+├── metric
+├── output
+├── preprocess
+```
+### ✨ Running Inference
+We take three models as examples to demonstrate how to use our benchmark for positional bias evaluation:
+- **InternVL3** – inference with `transformers`.
+- **MiMo-VL** – inference with `vLLM API`, using **video input**.  
+   (If you plan to call the commercial API for testing, this is a good reference.)
+- **GLM-4.5V** – inference with `vLLM API`, using **multi-image input**.
+
+For InternVL3, please follow the [official project](https://github.com/OpenGVLab/InternVL) to set up the environment. Run inference as follow:
+```bash
+bash ./evaluation/transformer/eval_intervl3.sh
+```
+The accuracy at each position will be computed and saved to  `acc_dir: ./output/internvl_acc`.
+
+For MiMo-VL, please first follow the [official project](https://github.com/XiaomiMiMo/MiMo-VL/tree/main) to deploy the model with vLLM. Run inference as follow:
+```bash
+bash ./evaluation/vllm/eval_mimovl.sh
+```
+The accuracy at each position will be computed and saved to `acc_dir: ./output/mimovl_acc`.
+
+For GLM-4.5V, please first follow the [official project](https://github.com/zai-org/GLM-V/) to deploy the model with vLLM. Run inference as follow:
+```bash
+bash ./evaluation/vllm/eval_glm45v.sh
+```
+The accuracy at each position will be computed and saved to `acc_dir: ./output/glm45v_acc`.
+
+📌 In addition, we provide preprocessing scripts, including:
+*frame extraction* and *concatenating probe and background videos into a single video*. See the `./preprocess` folder. 
+You can choose the input method based on your model. Concatenating probe and background videos into a single video is recommended as it is applicable to all models.
+
+📌 For precise investigation, in our paper, we evaluate models on the full set of our 1,177 samples, which requires tens of thousands of inferences across 10 positions. We provide a subset of [300 samples](https://huggingface.co/datasets/Cola-any/Video-LevelGauge/blob/main/LevelGauge/json/Pos_MCQA_300_final.json) for quick testing 🚀.
+
+### ✨ Metric Calculation
+Once positional accuracies are saved to `acc_dir`, you can compute all metrics in one command 😄, including *Pran*, *Pvar*, *Pmean*, *MR*, etc. We use the provided files in `./output/example_acc` as an example:
+```bash
+python ./metric/metric.py --acc_dir ./output/example_acc
+```
+Finally, we provide a script for visualizing positional bias. See [bias_plot.py](https://github.com/Cola-any/Video-LevelGauge/blob/main/metric/bias_plot.py) for details.
+
 ## 🔮 Evaluation PipLine
 Please refer to our 🍎 [project](https://github.com/Cola-any/Video-LevelGauge) and 📖[arXiv Paper](https://arxiv.org/abs/2508.19650) for more details.