Improve FinSearchComp dataset card: Add paper link, project page, and comprehensive metadata

This PR significantly improves the FinSearchComp dataset card by:
- Adding `task_categories: ['question-answering']` to the metadata for better categorization.
- Including relevant `tags: ['finance', 'financial-analysis', 'reasoning', 'search']` to enhance discoverability on the Hub.
- Providing a link to the official paper: https://huggingface.co/papers/2509.13160.
- Providing a link to the project page: https://randomtutu.github.io/FinSearchComp/.
- Adding a descriptive overview of the dataset, highlighting its purpose and the three key tasks it comprises, drawn from the paper's abstract.

These additions ensure the dataset card is more informative and discoverable for researchers and users.

README.md

@@ -1,3 +1,21 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- question-answering
+tags:
+- finance
+- financial-analysis
+- reasoning
+- search
+---
+
+This repository contains the FinSearchComp dataset, a benchmark for evaluating financial search and reasoning capabilities of LLM-based agents, as presented in the paper [FinSearchComp: Towards a Realistic, Expert-Level Evaluation of Financial Search and Reasoning](https://huggingface.co/papers/2509.13160).
+
+**Project Page:** [https://randomtutu.github.io/FinSearchComp/](https://randomtutu.github.io/FinSearchComp/)
+
+FinSearchComp is the first fully open-source agent benchmark designed for realistic, open-domain financial search and reasoning. It comprises three tasks that closely reproduce real-world financial analyst workflows:
+- Time-Sensitive Data Fetching
+- Simple Historical Lookup
+- Complex Historical Investigation
+
+The benchmark includes 635 questions spanning global and Greater China markets, meticulously annotated by 70 professional financial experts.