Enhance dataset card: Add comprehensive metadata and usage examples for KC-MMBench

README.md

@@ -1,14 +1,32 @@
 ---
-license: cc-by-sa-4.0
 language:
 - zh
+- en
+license: cc-by-sa-4.0
+task_categories:
+- video-text-to-text
+tags:
+- multimodal
+- video-understanding
+- short-video
+- benchmark
+- e-commerce
+- vqa
+library_name:
+- transformers
 ---
-<font size=3><div align='center' >  [[🍎 Home Page](https://kwai-keye.github.io/)] [[📖 Technical Report](https://huggingface.co/papers/2507.01949)] [[📊 Models](https://huggingface.co/Kwai-Keye)] [[🚀 Demo](https://huggingface.co/spaces/Kwai-Keye/Keye-VL-8B-Preview)] </div></font>
 
+<font size=3><div align='center' >  [[🍎 Home Page](https://kwai-keye.github.io/)] [[📖 Technical Report](https://huggingface.co/papers/2507.01949)] [[\ud83d\udcca Models](https://huggingface.co/Kwai-Keye)] [[\ud83d\ude80 Demo](https://huggingface.co/spaces/Kwai-Keye/Keye-VL-8B-Preview)] </div></font>
+
+This repository contains **KC-MMBench**, a new benchmark dataset meticulously tailored for real-world short-video scenarios, as presented in the paper "[Kwai Keye-VL Technical Report](https://huggingface.co/papers/2507.01949)". Constructed from [Kuaishou](https://www.kuaishou.com/) short video data, KC-MMBench comprises 6 distinct datasets designed to evaluate the performance of Vision-Language Models (VLMs) like [**Kwai Keye-VL-8B**](https://huggingface.co/Kwai-Keye/Keye-VL-8B-Preview), Qwen2.5-VL, and InternVL in comprehending dynamic, information-dense short-form videos.
 
-Based on the [Kuaishou](https://www.kuaishou.com/) short video data, we constructed 6 datasets for Vision-Language Models (VLMs) like [**Kwai Keye-VL-8B**](https://huggingface.co/Kwai-Keye/Keye-VL-8B-Preview), **Qwen2.5-VL** and **InternVL** to evaluate performance.
+For the associated code, detailed documentation, and evaluation scripts, please refer to the official [Kwai Keye-VL GitHub repository](https://github.com/Kwai-Keye/Kwai-Keye-VL).
+
+If you want to use KC-MMbench, please download with:
+```bash
+git clone https://huggingface.co/datasets/Kwai-Keye/KC-MMbench
+```
 
-If you want to use KC-MMbench, please download with: git clone https://huggingface.co/datasets/Kwai-Keye/KC-MMbench 
 ## Tasks
 | Task           | Description                                                                 |
 | -------------- | --------------------------------------------------------------------------- |
@@ -19,7 +37,6 @@ If you want to use KC-MMbench, please download with: git clone https://huggingfa
 | High_Like      | A binary classification task to determine the rate of likes of a short video. |
 | SPU            | The task of determining whether two items are the same product in e-commerce. |
 
-
 ## Performance 
 | Task           | Qwen2.5-VL-3B | Qwen2.5-VL-7B | InternVL-3-8B | MiMo-VL-7B | Kwai Keye-VL-8B |
 | -------------- | ------------- | ------------- | ------------- | ------- | ---- |
@@ -30,13 +47,63 @@ If you want to use KC-MMbench, please download with: git clone https://huggingfa
 | High_Like      | 48.85         | 47.94         | 47.03         | 51.14   | 55.25 |
 | SPU            | 74.09         | 81.34         | 75.64         | 81.86   | 87.05 |
 
-## Example of Evaluation
+## Usage
+
+This section provides a quick guide on how to interact with models using the `keye-vl-utils` library, which is essential for processing and integrating visual language information with Keye Series Models like Kwai Keye-VL-8B.
+
+### Install `keye-vl-utils`
+
+First, install the necessary utility library:
+```bash
+pip install keye-vl-utils
+```
+
+### Keye-VL Inference Example
+
+Here's an example of performing inference with a Kwai Keye-VL model, demonstrating how to prepare inputs for both image and video scenarios.
 
-Here is an example of an evaluation using VLMs on our datasets. The following configuration needs to be added to the config file.
 ```python
-{
+from transformers import AutoModel, AutoProcessor
+from keye_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model_path = "Kwai-Keye/Keye-VL-8B-Preview"
+
+model = AutoModel.from_pretrained(
+    model_path, torch_dtype="auto", device_map="auto", attn_implementation="flash_attention_2", trust_remote_code=True,
+).to('cuda')
+
+# Example messages demonstrating various input types (image, video)
+messages = [
+    # Image Input Examples
+    [{"role": "user", "content": [{"type": "image", "image": "file:///path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}]}],
+    [{"role": "user", "content": [{"type": "image", "image": "http://path/to/your/image.jpg"}, {"type": "text", "text": "Describe this image."}]}],
+    [{"role": "user", "content": [{"type": "image", "image": "data:image;base64,/9j/..."}, {"type": "text", "text": "Describe this image."}]}],
+    
+    # Video Input Examples (most relevant for KC-MMBench)
+    [{"role": "user", "content": [{"type": "video", "video": "file:///path/to/video1.mp4"}, {"type": "text", "text": "Describe this video."}]}],
+    [{"role": "user", "content": [{"type": "video", "video": ["file:///path/to/extracted_frame1.jpg", "file:///path/to/extracted_frame2.jpg", "file:///path/to/extracted_frame3.jpg"],}, {"type": "text", "text": "Describe this video."},],}],
+    [{"role": "user", "content": [{"type": "video", "video": "file:///path/to/video1.mp4", "fps": 2.0, "resized_height": 280, "resized_width": 280}, {"type": "text", "text": "Describe this video."}]}],
+]
+
+processor = AutoProcessor.from_pretrained(model_path)
+# Note: model loaded above already
+text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+images, videos, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+inputs = processor(text=text, images=images, videos=videos, padding=True, return_tensors="pt", **video_kwargs).to("cuda")
+generated_ids = model.generate(**inputs)
+print(generated_ids)
+```
 
-    "model":'...'
+### Evaluation
+
+For detailed instructions on how to evaluate models using the KC-MMBench datasets, including setup and running evaluation scripts, please refer to the `evaluation/KC-MMBench/README.md` file in the official [Kwai Keye-VL GitHub repository](https://github.com/Kwai-Keye/Kwai-Keye-VL/tree/main/evaluation/KC-MMBench).
+
+Below is the example configuration for evaluation using VLMs on our datasets:
+
+```python
+{
+    "model": "...", # Specify your model
     "data": {
         "CPV": {
             "class": "KwaiVQADataset",
@@ -64,3 +131,4 @@ Here is an example of an evaluation using VLMs on our datasets. The following co
         }
     }
 }
+```