Duplicate from Azzindani/IDX_Financial_Statements

Co-authored-by: Azzindani <Azzindani@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- table-question-answering
+- document-question-answering
+- feature-extraction
+- text-generation
+- translation
+- summarization
+language:
+- id
+- en
+tags:
+- finance
+- indonesia
+- idx
+- stock-market
+- financial-statements
+- xbrl
+- ocr
+- multimodal-rag
+- accounting
+- corporate-reporting
+---
+
+# IDX_Financial_Statements: The Multimodal Indonesian Financial Dataset
+
+**IDX_Financial_Statements** is a centralized repository providing the most complete set of financial disclosures for public companies listed on the **Indonesia Stock Exchange (IDX)**. This dataset is designed for advanced financial research, spanning from raw document archival to structured data extraction.
+
+## Dataset Overview
+
+This is a multimodal dataset that captures the full lifecycle of financial reporting. It includes:
+
+1.  **PDF Reports**: Original Annual Reports (Laporan Tahunan) and Financial Statements as submitted by issuers. Ideal for Document AI, layout analysis, and OCR tasks.
+2.  **Excel Sheets**: Semi-structured spreadsheets containing balance sheets, income statements, and cash flow details.
+3.  **XBRL Instances**: High-precision, machine-readable XML-based data using the IDX Taxonomy. This is the gold standard for quantitative data integrity.
+4.  **Metadata**: Associated tickers, industry sectors, reporting periods (Q1, Q2, Q3, FY), and submission timestamps.
+
+## Key Features
+
+- **Format Diversity**: Supports a wide range of technical workflows, from computer vision (PDF parsing) to direct database ingestion (XBRL).
+- **Historical Depth**: Comprehensive coverage of the Indonesian capital market over multiple fiscal years.
+- **Bilingual Context**: Documents often contain side-by-side Indonesian and English text, making this a valuable resource for financial-domain NMT (Neural Machine Translation).
+- **Audit-Ready**: Includes original source documents, allowing for the verification of extracted data against official corporate filings.
+
+## Use Cases
+
+- **Multimodal RAG**: Build AI agents that can "read" a 200-page PDF annual report and cross-reference it with numerical Excel data.
+- **XBRL Analysis**: Utilize the standardized taxonomy for high-speed fundamental screening without the need for traditional scraping.
+- **Document AI Training**: Fine-tune models for table detection, key-value pair extraction, and financial entity recognition.
+- **Market Intelligence**: Analyze corporate governance statements and management discussion & analysis (MD&A) sections for sentiment and strategic shifts.
+
+## Data Structure
+
+The repository is organized by `Ticker` and `Period`. A typical directory structure looks like:
+- `[TICKER]/[YEAR]/[PERIOD]/`
+    - `FinancialStatement-202X-I-Ticker.pdf`
+    - `FinancialStatement-202X-I-Ticker.xlsx`
+    - `FinancialStatement-202X-I-Ticker.zip` (XBRL)
+
+## License
+
+This dataset is licensed under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). You are free to share and adapt the material for any purpose, including commercial use, provided you give appropriate credit.
+
+## Disclaimer
+
+This dataset aggregates publicly available information for research and analytical purposes. Users are encouraged to cross-reference data with the [IDX official disclosures](https://www.idx.co.id) for formal investment or legal decisions.
+
+---