Improve dataset card: Add metadata, links, and sample usage

This pull request significantly improves the dataset card for the `lil-lab/respect` dataset.

Key changes include:
- **Metadata Update**: Added `task_categories` (`conversational`, `text-generation`) and `tags` (`reinforcement-learning-from-human-feedback`, `dialogue`, `conversational-ai`, `preference-alignment`) to enhance discoverability and provide better context for the dataset.
- **Content Overhaul**: The existing minimal content has been replaced with a comprehensive overview:
- A descriptive title for the dataset.
- An introductory paragraph summarizing the paper's core contributions and the dataset's purpose.
- Updated the paper link to the official Hugging Face Papers page: https://huggingface.co/papers/2509.25137.
- Added links to the project page: https://lil-lab.github.io/respect.
- Added links to the GitHub repository: https://github.com/lil-lab/respect.
- Included a "Sample Usage" section with Python code snippets, directly sourced from the GitHub README, demonstrating how to load both the dataset and associated model checkpoints, thereby facilitating easier access and utilization for researchers.

These additions make the dataset card much more informative, discoverable, and user-friendly for the Hugging Face community.

README.md

@@ -2,6 +2,14 @@
 language:
 - en
 license: apache-2.0
+task_categories:
+- conversational
+- text-generation
+tags:
+- reinforcement-learning-from-human-feedback
+- dialogue
+- conversational-ai
+- preference-alignment
 dataset_info:
 - config_name: feedback_decoder_binary
   features:
@@ -172,4 +180,35 @@ configs:
     path: turn/train-*
 ---
 
-<https://arxiv.org/abs/2410.13852>
+# Retrospective Learning from Interactions (Respect) Dataset
+
+This dataset supports **Retrospective Learning from Interactions (Respect)**, a paradigm introduced in the paper [The Era of Real-World Human Interaction: RL from User Conversations](https://huggingface.co/papers/2509.25137).
+
+The paper introduces Reinforcement Learning from Human Interaction (RLHI), a novel approach that learns directly from in-the-wild user conversations. This enables continual model improvement and multifaceted alignment of conversational models, moving beyond traditional pre-annotated, expert-generated human feedback. The dataset facilitates two complementary methods: RLHI with User-Guided Rewrites and RLHI with User-Based Rewards, linking long-term user personas to turn-level preferences.
+
+*   **Paper**: [The Era of Real-World Human Interaction: RL from User Conversations](https://huggingface.co/papers/2509.25137)
+*   **Project Page**: [https://lil-lab.github.io/respect](https://lil-lab.github.io/respect)
+*   **GitHub Repository**: [https://github.com/lil-lab/respect](https://github.com/lil-lab/respect)
+
+## Sample Usage
+
+You can load the data and associated checkpoints as follows:
+
+```python
+from datasets import load_dataset
+from transformers import Idefics2ForConditionalGeneration
+from peft import PeftModel
+import torch # Ensure torch is imported
+
+# Download data
+ds = load_dataset("lil-lab/respect", name="turn", split="train")
+
+# Download checkpoints
+checkpoint = "HuggingFaceM4/idefics2-8b"
+model_id = 'lil-lab/respect'
+
+model = Idefics2ForConditionalGeneration.from_pretrained(
+    checkpoint, torch_dtype=torch.bfloat16)
+peft_model = PeftModel.from_pretrained(
+    model, model_id, adapter_name="r6_bp", revision="r6_bp")
+```