Enhance dataset card: Add paper, code links, task categories, and comprehensive usage

This PR significantly improves the `MAC-SLU` dataset card by adding:
- The Hugging Face `paper` link: https://huggingface.co/papers/2512.01603
- The `code` (GitHub) repository link: https://github.com/Gatsby-web/MAC_SLU
- The `task_categories: ['audio-text-to-text']`, `language: ['en']` and `tags: ['spoken-language-understanding', 'automotive', 'multi-intent']` to improve discoverability on the Hub.
- An introductory overview of the dataset.
- A comprehensive `Usage` section, including code snippets for In-Context Learning (ICL) and Supervised Fine-Tuning (SFT), directly from the GitHub README. This helps users quickly get started with the dataset and its associated models.

README.md

@@ -1,3 +1,100 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- audio-text-to-text
+language:
+- en
+tags:
+- spoken-language-understanding
+- automotive
+- multi-intent
+---
+
+# MAC-SLU: A Benchmark for Multi-Intent Spoken Language Understanding in Automotive Cabins
+
+[Paper](https://huggingface.co/papers/2512.01603) | [Code](https://github.com/Gatsby-web/MAC_SLU)
+
+This repository hosts the **MAC-SLU** dataset, a novel Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark. MAC-SLU is designed to evaluate Spoken Language Understanding (SLU) systems on complex, multi-intent user commands within an automotive environment, addressing the limitations of existing SLU datasets in terms of diversity and complexity. It features authentic and complex multi-intent data, suitable for benchmarking both Large Language Models (LLMs) and Large Audio Language Models (LALMs).
+
+## 🚀 Getting Started
+
+### 1\. Download the Dataset
+
+The complete MAC-SLU dataset is hosted on the Hugging Face Hub.
+
+  * **Dataset Link:** [Gatsby1984/MAC\_SLU](https://huggingface.co/datasets/Gatsby1984/MAC_SLU)
+
+### 2\. Prepare the Environment
+
+Our experiments are divided into two main approaches: **In-Context Learning (ICL)** and **Supervised Fine-Tuning (SFT)**. Please set up the appropriate environment for the method you wish to use.
+
+## 🛠️ Usage
+
+### In-Context Learning (ICL)
+
+#### Environment Setup
+
+Our ICL code relies on `vLLM`. The required version depends on the model you are using. All experiments were conducted with **Python 3.10**.
+
+  * For **Qwen3** experiments: `pip install vllm==0.9.2`
+  * For **Qwen2.5-Omni** experiments: `pip install vllm==0.8.5.post1`
+
+#### Running ICL Experiments
+
+**Step 1: Deploy the Model with vLLM**
+(This step is not required if you are using a commercial API.)
+
+Open a terminal and run the following command to start the vLLM server. This example is for `Qwen2.5-Omni-7B`.
+
+```bash
+
+export CUDA_VISIBLE_DEVICES=0
+
+vllm serve /path/to/your/Qwen2.5-Omni-7B \
+  --served-model-name Qwen2.5-Omni-7B \
+  --tensor-parallel-size 1 \
+  --gpu-memory-utilization 0.9 \
+  --host 0.0.0.0 \
+  --port 12355 \
+  --uvicorn-log-level warning \
+  --disable-log-requests \
+  --max-model-len 32768
+```
+
+**Step 2: Run Inference**
+
+Once the server is running, open a new terminal and execute the inference script.
+
+```bash
+python slu_icl.py \
+    --provider local \
+    --input-file /path/to/test_set.jsonl \
+    --audio-dir /path/to/audio_test_directory \
+    --output-file /path/to/prediction.jsonl \
+    --model-name Qwen2.5-Omni-7B \
+    --api-base http://0.0.0.0:12355/v1
+```
+
+  * **Note:** For other models, you may need to change `--model-name` and the model path in the `vllm serve` command. To use a commercial API, change `--provider` to the appropriate name and configure the necessary API keys.
+
+**Step 3: Evaluation**
+
+```bash
+python metrics.py prediction.jsonl icl_label.jsonl
+```
+-----
+
+### Supervised Fine-Tuning (SFT)
+
+#### Environment Setup
+
+For SFT experiments, we use the efficient **LLaMA-Factory** framework. Please follow the official instructions to install and set up the environment.
+
+  * **Framework:** [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+
+#### Training Instructions
+
+We recommend using a **LoRA-SFT** approach for fine-tuning.
+
+1.  **Prepare your dataset** using the format required by LLaMA-Factory.
+2.  **Configure your training run** by selecting a model, dataset, and setting the LoRA hyperparameters.