Improve dataset card: Add library_name, license, benchmark tag, GitHub link, and sample usage

This PR improves the dataset card for the LoopServe Multi-Turn Dialogue Benchmark by:
- Adding `library_name: datasets` and `license: cc-by-4.0` to the metadata section for better discoverability and usage clarity.
- Adding the `benchmark` tag to reflect the dataset's nature as an evaluation benchmark.
- Including a direct link to the associated GitHub repository (`https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management`), which serves as a central hub for KV Cache Management research and explicitly links to this dataset.
- Providing a clear `Sample Usage` section demonstrating how to load and access the dataset using the `datasets` library.
- Removing the redundant shell tree file information from the content section, as this is typically handled by the Hub's file browser and `configs` metadata.

The dataset's primary associated paper, "LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues" (arXiv:2507.13681), remains the main reference in the dataset card.

README.md

@@ -1,15 +1,17 @@
 ---
+language:
+- en
 task_categories:
 - question-answering
 - summarization
 - text-generation
-language:
-- en
+pretty_name: LoopServe Multi-Turn Dialogue Benchmark
 tags:
 - llm
 - kv_cache
-pretty_name: LoopServe Multi-Turn Dialogue Benchmark
-
+- benchmark
+library_name: datasets
+license: cc-by-4.0
 configs:
 - config_name: multi-turn_FS
   data_files: multi_turn/few_shot_learning/*.jsonl
@@ -35,21 +37,7 @@ Arxiv: https://www.arxiv.org/abs/2507.13681
 
 Huggingface: https://huggingface.co/papers/2507.13681
 
-``` shell
-.
-├── README.md
-├── conversations.jsonl
-├── multi_turn
-│   ├── few_shot_learning
-│   ├── needle_in_haystack
-│   ├── question_answering
-│   └── summarization
-└── single_turn
-    ├── few_shot_learning
-    ├── needle_in_haystack
-    ├── question_answering
-    └── summarization
-```
+Code: https://github.com/TreeAI-Lab/Awesome-KV-Cache-Management
 
 # Introduction
 
@@ -65,6 +53,25 @@ The benchmark captures the dynamic dependencies and unpredictable patterns chara
 
 For more details, please refer to our paper.
 
+# Sample Usage
+
+The dataset can be easily loaded using the `load_dataset` function from the 🤗 Datasets library.
+
+```python
+from datasets import load_dataset
+
+# Load a specific configuration, for example, the multi-turn question answering data
+dataset = load_dataset("TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs", "multi-turn_QA")
+
+# Access the training split
+print(dataset["train"])
+
+# Iterate through an example
+for example in dataset["train"]:
+    print(example)
+    break
+```
+
 # Citation
 ``` bibtex
 @misc{li2025loopserveadaptivedualphasellm,
@@ -76,4 +83,4 @@ For more details, please refer to our paper.
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2507.13681}, 
 }
-```
+```