Update README.md

README.md

@@ -30,9 +30,149 @@ language:
 tags:
 - finance
 - multilingual
-pretty_name: PolyFiQA Easy
+pretty_name: PolyFiQA-Easy
 size_categories:
 - n<1K
 task_categories:
 - question-answering
 ---
+
+# Dataset Card for PolyFiQA-Easy
+
+## Table of Contents
+
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://huggingface.co/collections/TheFinAI/multifinben-6826f6fc4bc13d8af4fab223
+- **Repository:** https://huggingface.co/datasets/TheFinAI/polyfiqa-easy
+- **Paper:** MultiFinBen: A Multilingual, Multimodal, and Difficulty-Aware Benchmark for Financial LLM Evaluation
+- **Leaderboard:** https://huggingface.co/spaces/TheFinAI/Open-FinLLM-Leaderboard
+
+### Dataset Summary
+
+**PolyFiQA Easy** is a multilingual financial question-answering dataset designed to evaluate financial reasoning in a simplified setting. Each instance consists of a task identifier, a query prompt, an associated financial question, and the correct answer. The Easy split focuses on queries that can be answered with minimal document retrieval, making it ideal for low-latency or resource-constrained systems.
+
+### Supported Tasks and Leaderboards
+
+- **Tasks:**
+  - question-answering
+- **Evaluation Metrics:**
+  - ROUGE-1
+
+### Languages
+
+- English
+- Chinese
+- Japanese
+- Spanish
+- Greek
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance in the dataset contains:
+
+- `task_id`: A unique identifier for the query-task pair.
+- `query`: A brief query statement from the financial domain.
+- `question`: The full question posed based on the query context.
+- `answer`: The correct answer string.
+
+### Data Fields
+
+| Field     | Type   | Description                                  |
+|-----------|--------|----------------------------------------------|
+| task_id   | string | Unique ID per task                           |
+| query     | string | Financial query (short form)                 |
+| question  | string | Full natural-language financial question     |
+| answer    | string | Ground-truth answer to the question          |
+
+### Data Splits
+
+| Split | # Examples | Size (bytes) |
+|-------|------------|--------------|
+| test  | 76         | 5,175,349    |
+
+## Dataset Creation
+
+### Curation Rationale
+
+PolyFiQA Easy was curated to provide a lightweight yet robust benchmark for financial question answering with minimal retrieval burden. It aims to evaluate models’ reasoning on self-contained or short-context questions in finance.
+
+### Source Data
+
+#### Initial Data Collection
+
+Questions were derived from real-world financial scenarios and manually adapted to fit a concise QA format.
+
+#### Source Producers
+
+Data was created by researchers and annotators with backgrounds in finance, NLP, and data curation.
+
+### Annotations
+
+#### Annotation Process
+
+Questions and answers were authored and verified through a multi-step validation pipeline involving domain experts.
+
+#### Annotators
+
+A team of finance researchers and data scientists.
+
+### Personal and Sensitive Information
+
+The dataset contains no personal or sensitive information. All content is synthetic or anonymized for safe usage.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+PolyFiQA Easy contributes to research in financial NLP by enabling multilingual evaluation under constrained settings.
+
+### Discussion of Biases
+
+- May over-represent English financial contexts.
+- Questions emphasize clarity and answerability over real-world ambiguity.
+
+### Other Known Limitations
+
+- Limited size (76 examples).
+- Focused on easy questions; may not generalize to complex reasoning tasks.
+
+## Additional Information
+
+### Dataset Curators
+
+- The FinAI Team
+
+### Licensing Information
+
+- **License:** Apache License 2.0
+
+### Citation Information
+
+If you use this dataset, please cite:
+
+```bibtex