Add task category and sample usage, fix internal paper link

This pull request improves the HiKE dataset card by:

* Adding the `task_categories: ['automatic-speech-recognition']` to the YAML metadata for better discoverability on the Hugging Face Hub.
* Fixing a broken internal link `[our paper]()` in the "Introduction" section to `[our paper](https://arxiv.org/abs/2509.24613)`, pointing to the arXiv paper.
* Adding a "Sample Usage" section with installation instructions and code snippets for running evaluations and evaluating custom models, directly sourced from the project's GitHub repository.

README.md

@@ -1,4 +1,10 @@
 ---
+language:
+- ko
+- en
+license: apache-2.0
+task_categories:
+- automatic-speech-recognition
 dataset_info:
   features:
   - name: audio
@@ -30,10 +36,6 @@ configs:
   data_files:
   - split: test
     path: data/test-*
-license: apache-2.0
-language:
-- ko
-- en
 tags:
 - speech
 - recognition
@@ -51,7 +53,7 @@ HiKE is the first Korean-English Code-Switching (CS) Automatic Speech Recognitio
 
 Experimental results show that all multilingual ASR models exhibit significantly higher error rates on code-switching data, and that their CS-ASR capabilities can be improved through fine-tuning.
 
-For further details, please refer to [our paper]().
+For further details, please refer to [our paper](https://arxiv.org/abs/2509.24613).
 
 [1] Ugan et al., [“PIER: A Novel Metric for Evaluating What Matters in Code-Switching”](https://arxiv.org/abs/2501.09512), ICASSP 2025 
 
@@ -65,6 +67,59 @@ To provide more fine-grained comparison of model performance on different forms
 ### Loanword Labels
 Loanwords are words adopted from a foreign language and adapted to the phonology and orthography of the new language. For example, the Korean loanword **'버스' [bəs]** and the English word **'bus' [bʌs]** are pronounced almost identically and can be used interchangeably in a CS context. To avoid this problem, we meticulously labeled all loanwords contained in our dataset.
 
+## Sample Usage
+### Install Dependencies
+```sh
+git clone --recurse-submodules https://github.com/ThetaOne-AI/HiKE
+cd HiKE
+pip install -r requirements.txt
+apt-get update && apt-get install -y ffmpeg  # install ffmpeg if needed
+```
+
+### Run Evaluation
+```sh
+bash scripts/evaluate_whisper.sh
+# or
+python src/main.py --model whisper --model_name openai/whisper-large --batch_size 8
+```
+
+The results will be saved in `./outputs`.
+
+### Evaluate Your Model
+- Implement a class that follows the `BaseASR` interface in `src/models/your_model.py`, and register it in `src/main.py`.
+
+Create `src/models/your_model.py`:
+
+```python
+from typing import List, Dict, Any
+from src.models import BaseASR
+
+
+class YourModel(BaseASR):
+    def __init__(self, model_name: str = "your/model-or-config"):
+        self.model_name = model_name
+        # TODO: load your model or client here
+
+    def generate(self, input, batch_size: int | None = None, **kwargs) -> List[Dict[str, Any]]:
+        if not isinstance(input, list):
+            input = [input]
+        return [{"text": your_transcribe_fn(x)} for x in input]
+```
+
+Register in `src/main.py`:
+
+```python
+elif model == "your_model":
+    from models.your_model import YourModel
+    asr = YourModel(model_name)
+```
+
+Run:
+
+```sh
+python src/main.py --model your_model --model_name your/model-or-name
+```
+
 ## Citation
 ```
 @misc{paik2025hike,