Upload Text2SQL dataset with 34 SQLite databases and metadata files

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+finch_dataset.json filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/debit_card_specializing.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/financial.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/regional_sales.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/retails.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/sales.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bird/sales_in_weather.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/book_sql/accounting.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bull/ccks_fund/ccks_fund.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bull/ccks_macro/ccks_macro.sqlite filter=lfs diff=lfs merge=lfs -text
+text2sql-db/text2sql/bull/ccks_stock/ccks_stock.sqlite filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- text2sql
+- text-to-sql
+- semantic-parsing
+language:
+- en
+tags:
+- sql
+- database
+- finch
+- financial
+- text2sql
+- semantic-parsing
+- nlp
+- sqlite
+- database-schemas
+- finance
+pretty_name: FINCH - Financial Intelligence using Natural language for Contextualized SQL Handling
+size_categories:
+- 10K<n<100K
+---
+
+# Dataset Card for FINCH - Financial Intelligence using Natural language for Contextualized SQL Handling
+
+A comprehensive collection of SQLite databases from the FINCH benchmark, containing **33 databases** with **292 tables** and **75,725 natural language-SQL pairs** across diverse financial domains for Text-to-SQL research and development.
+
+## Dataset Details
+
+### Dataset Description
+
+**Curated by:** [Domyn](https://www.domyn.com/)  
+**Authors:** Avinash Kumar Singh, Bhaskarjit Sarmah, Stefano Pasquali  
+**Language(s):** English  
+**License:** CC-BY-NC-4.0
+
+FINCH (Financial Intelligence using Natural language for Contextualized SQL Handling) provides SQLite database files from a carefully curated financial Text-to-SQL benchmark that consolidates and extends existing resources into a unified, finance-specific dataset. Each database preserves original schema structure, relationships, and data while focusing specifically on financial domains and applications.
+
+This dataset addresses a critical gap in Text-to-SQL research: despite significant progress in general-domain benchmarks, financial applications remain especially challenging due to complex schemas, domain-specific terminology, and high stakes of error. FINCH provides the first large-scale, finance-oriented Text-to-SQL benchmark suitable for both evaluation and fine-tuning.
+
+### Dataset Sources
+
+**Paper:** FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling *(coming soon)*  
+
+
+## Key Features
+
+- **33 SQLite databases** specifically curated for financial applications
+- **292 tables** with **2,233 columns** and **177 relations**
+- **75,725 NL-SQL pairs** for comprehensive training and evaluation
+- **Financial domain focus** including retail, banking, insurance, e-commerce, funds, stocks, and accounting
+- **Direct SQLite format** - ready for SQL queries and analysis
+- **Preserved relationships** - foreign keys and indexes intact
+- **Multi-difficulty coverage** with easy, medium, and hard query complexity levels
+
+## Dataset Structure
+
+The dataset is organized by financial domain with meaningful database names:
+
+### File Organization
+```
+finch/
+├── spider/           # 22 SQLite files (financial subset from Spider)
+├── bird/             # 7 SQLite files (financial subset from BIRD)
+├── bull/             # 3 SQLite files (BULL/CCKS financial data)
+└── book_sql/         # 1 SQLite file (BookSQL accounting data)
+```
+
+### Financial Domains Covered
+
+#### Retail & E-commerce
+- **customers_and_invoices**: E-commerce customer and billing systems
+- **e_commerce**: Online retail transactions and order management  
+- **department_store**: Retail chain operations and inventory management
+- **shop_membership**: Customer loyalty and membership programs
+
+#### Banking & Financial Services
+- **financial**: Czech bank transactions and loan portfolios (1M+ records)
+- **small_bank**: Banking account management systems
+- **loan_1**: Loan processing and customer account data
+
+#### Insurance & Risk Management
+- **insurance_policies**: Insurance claims and policy management
+- **insurance_and_eClaims**: Electronic claims processing systems
+- **insurance_fnol**: First notification of loss handling
+
+#### Investment & Trading
+- **ccks_fund**: Mutual fund management and performance data
+- **ccks_stock**: Stock market data and trading information
+- **tracking_share_transactions**: Investment portfolio tracking
+
+#### Sales & Marketing
+- **sales**: Large-scale sales transactions (6M+ records)
+- **sales_in_weather**: Sales data correlated with external factors
+- **customers_campaigns_ecommerce**: Marketing campaign effectiveness
+
+#### Accounting & Financial Reporting
+- **accounting**: Complete accounting system with 185+ tables covering transactions, customers, vendors, and financial reporting
+- **school_finance**: Educational institution financial management
+
+## Dataset Format & Examples
+
+### Data Files Structure
+- **`finch_dataset.json`**: Main dataset file with 75,725 NL-SQL pairs (appears in HF dataset viewer)
+- **`tables.json`**: Database schema metadata for all 33 databases (auxiliary file)
+- **`text2sql-db/`**: SQLite database files organized by source (auxiliary files)
+
+### Sample Data from finch_dataset.json
+
+```json
+[
+    {
+        "question_id": 1,
+        "db_id": "financial",
+        "db_name": "bird",
+        "question": "How many accounts who choose issuance after transaction are staying in East Bohemia region?",
+        "partition": "dev",
+        "difficulty": "medium",
+        "SQL": "SELECT COUNT(t2.account_id) FROM district AS t1 INNER JOIN account AS t2 ON t1.district_id = t2.district_id WHERE t1.a3 = 'east bohemia' AND t2.frequency = 'poplatek po obratu'"
+    },
+    {
+        "question_id": 2,
+        "db_id": "financial", 
+        "db_name": "bird",
+        "question": "How many accounts who have region in Prague are eligible for loans?",
+        "partition": "dev",
+        "difficulty": "easy",
+        "SQL": "SELECT COUNT(t1.account_id) FROM account AS t1 INNER JOIN loan AS t2 ON t1.account_id = t2.account_id INNER JOIN district AS t3 ON t1.district_id = t3.district_id WHERE t3.a3 = 'prague'"
+    },
+    {
+        "question_id": 3,
+        "db_id": "financial",
+        "db_name": "bird", 
+        "question": "The average unemployment ratio of 1995 and 1996, which one has higher percentage?",
+        "partition": "dev",
+        "difficulty": "easy",
+        "SQL": "SELECT DISTINCT IIF(AVG(a13) > AVG(a12), '1996', '1995') FROM district"
+    }
+]
+```
+
+### Schema Information (tables.json)
+
+The `tables.json` file contains comprehensive schema metadata for all databases:
+
+```json
+{
+  "financial": {
+    "db_id": "financial",
+    "table_names_original": ["account", "card", "client", "disp", "district", "loan", "order", "trans"],
+    "table_names": ["account", "card", "client", "disposition", "district", "loan", "order", "transaction"],
+    "column_names_original": [
+      [-1, "*"],
+      [0, "account_id"],
+      [0, "district_id"], 
+      [0, "frequency"],
+      [0, "date"]
+    ],
+    "column_types": ["text", "number", "number", "text", "text"],
+    "foreign_keys": [[2, 1], [4, 2]],
+    "primary_keys": [1]
+  }
+}
+```
+
+## Example Usage
+
+### Loading with Python
+
+### Primary Method: Using datasets library (Recommended)
+
+```python
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+import sqlite3
+
+# Load the main dataset using HuggingFace datasets library
+dataset = load_dataset("domyn/finch")
+print(f"Dataset: {dataset}")
+print(f"Number of examples: {len(dataset['train'])}")
+
+# Access individual examples
+sample = dataset['train'][0]
+print(f"Question: {sample['question']}")
+print(f"SQL: {sample['SQL']}")
+print(f"Database: {sample['db_id']}")
+print(f"Difficulty: {sample['difficulty']}")
+
+# Load schema information for the database
+schema_path = hf_hub_download(repo_id="domyn/finch", filename="tables.json")
+import json
+with open(schema_path, 'r') as f:
+    schemas = json.load(f)
+
+# Download the corresponding SQLite database
+db_path = hf_hub_download(
+    repo_id="domyn/finch", 
+    filename=f"text2sql-db/text2sql/bird/{sample['db_id']}.sqlite"
+)
+
+# Execute the SQL query on the actual database
+conn = sqlite3.connect(db_path)
+cursor = conn.cursor()
+cursor.execute(sample['SQL'])
+results = cursor.fetchall()
+print(f"Query Results: {results}")
+```
+
+### Alternative Method: Direct file download
+
+```python
+import json
+import sqlite3
+from huggingface_hub import hf_hub_download
+
+# Alternative: Load dataset JSON file directly
+samples_path = hf_hub_download(repo_id="domyn/finch", filename="finch_dataset.json")
+with open(samples_path, 'r') as f:
+    dataset = json.load(f)
+
+sample = dataset[0]  # First sample
+print(f"Question: {sample['question']}")
+print(f"SQL: {sample['SQL']}")
+```
+
+### Financial Query Examples
+
+```python
+# Analyze banking transactions
+cursor.execute("""
+    SELECT account_id, SUM(amount) as total_balance 
+    FROM transactions 
+    WHERE transaction_date >= '2023-01-01' 
+    GROUP BY account_id 
+    ORDER BY total_balance DESC
+""")
+
+# Insurance claims analysis
+cursor.execute("""
+    SELECT policy_type, COUNT(*) as claim_count, AVG(claim_amount)
+    FROM claims c
+    JOIN policies p ON c.policy_id = p.policy_id
+    WHERE claim_status = 'approved'
+    GROUP BY policy_type
+""")
+```
+
+### Schema Exploration
+
+```python
+# Get all tables
+cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
+tables = cursor.fetchall()
+print("Available tables:", tables)
+
+# Get detailed schema information
+cursor.execute("PRAGMA table_info(transactions)")
+schema = cursor.fetchall()
+for column in schema:
+    print(f"Column: {column[1]}, Type: {column[2]}")
+```
+
+## Data Quality & Statistics
+
+### Database Statistics
+
+**📊 TOTAL DATABASES: 33**  
+**📅 FINANCIAL DOMAINS: 8+ specialized areas**  
+**🏢 TABLES: 292 across all databases**  
+**🔗 RELATIONS: 177 foreign key relationships**  
+**💼 NL-SQL PAIRS: 75,725 total examples**
+
+| Source | Database Count | Table Count | NL-SQL Pairs | Domain Focus |
+|--------|---------------|-------------|--------------|--------------|
+| Spider (financial) | 22 | 145 | 1,100 | Cross-domain financial |
+| BIRD (financial) | 7 | 48 | 1,139 | Large-scale realistic |
+| BULL/CCKS | 3 | 99 | 4,966 | Chinese financial markets |
+| BookSQL | 1 | 185 | 68,907 | Accounting systems |
+| **TOTAL** | **33** | **292** | **75,725** | **Financial** |
+
+### Difficulty Distribution
+
+- **Easy queries**: 9,358 examples (12.4%)
+- **Medium queries**: 33,780 examples (44.6%) 
+- **Hard queries**: 32,587 examples (43.0%)
+
+### Quality Assurance
+
+The dataset has undergone extensive validation and cleaning:
+- ✅ **SQL execution verified** for all 75,725 queries
+- ✅ **Schema consistency** maintained across all databases
+- ✅ **Error correction** performed on original datasets:
+  - BIRD: 327 queries fixed (column names, table references)
+  - BULL: 60 queries corrected (syntax errors, invalid references)
+  - BookSQL: 9,526 queries repaired (column names, table references, syntax)
+- ✅ **Financial domain relevance** verified for all included databases
+
+## Applications
+
+This dataset is specifically designed for:
+
+### Financial Research Applications
+- **Financial Text-to-SQL Systems**: Train models specifically for financial database querying
+- **Domain Adaptation Studies**: Research cross-domain transfer from general to financial SQL
+- **Financial Schema Understanding**: Develop models that understand complex financial relationships
+- **Regulatory Compliance**: Build systems for automated financial reporting and compliance checking
+- **Risk Analysis Automation**: Create tools for automated risk assessment query generation
+
+### Industry Applications  
+- **Financial Analytics Platforms**: Natural language interfaces for financial data analysis
+- **Banking Query Systems**: Customer service and internal analyst tools
+- **Investment Research**: Automated portfolio analysis and market research
+- **Regulatory Reporting**: Compliance and audit report generation
+- **Insurance Processing**: Claims analysis and policy management systems
+
+### Educational Applications
+- **Financial SQL Training**: Teach SQL with realistic financial datasets
+- **Business Intelligence Education**: Train on real-world financial database structures
+- **Fintech Development**: Build and test financial technology applications
+
+## FINCH Evaluation Metric
+
+The dataset introduces the **FINCH Score**, a specialized evaluation metric for financial Text-to-SQL that addresses limitations of traditional exact-match and execution accuracy metrics:
+
+### Key Features of FINCH Score
+- **Component-wise Scoring**: Weighted evaluation of SQL clauses (SELECT, WHERE, JOIN, etc.)
+- **Financial Clause Priority**: Higher weights for business-critical clauses (WHERE, JOIN, GROUP BY)
+- **Execution Tolerance**: Materiality-aware tolerance for floating-point differences
+- **Structural Fidelity**: Emphasis on semantic correctness over syntactic matching
+
+### Mathematical Formulation
+```
+FINCH Score = S(q̂,q*)^β × (δ + (1-δ)e(q̂,q*))
+```
+Where:
+- S(q̂,q*): Weighted component similarity score
+- e(q̂,q*): Execution accuracy with tolerance τ
+- β: Structural fidelity parameter
+- δ: Execution failure penalty parameter
+
+## Benchmark Results
+
+Initial benchmarking on FINCH reveals detailed performance across multiple state-of-the-art models:
+
+### Model Performance Table
+
+| Model | Exact Match | Execution Accuracy | Component Match | FINCH Score |
+|-------|-------------|-------------------|-----------------|-------------|
+| **GPT-OSS-120B** | 1.8% | 27.8% | 16.6% | **11.6%** |
+| **Arctic-Text2SQL-R1-7B** | 0.6% | 2.3% | 3.7% | **1.5%** |
+| **Qwen3-235B-A22B** | 0.7% | 2.5% | 2.8% | **1.2%** |
+| **Qwen3-8B** | 0.5% | 0.8% | 3.5% | 1.2% |
+| **GPT-OSS-20B** | 0.3% | 7.5% | 5.2% | 3.0% |
+| **Phi-4-mini-reasoning** | 0.0% | 0.2% | 1.0% | 0.4% |
+
+### SQL Clause-Level Performance
+
+Analysis of errors by SQL clause reveals systematic challenges:
+
+| Model | SELECT | FROM | WHERE | GROUP BY | HAVING | ORDER BY | LIMIT |
+|-------|--------|------|-------|----------|--------|----------|--------|
+| **GPT-OSS-120B** | 4.7% | **27.3%** | 6.9% | 7.5% | 6.3% | 6.3% | **73.8%** |
+| **Arctic-Text2SQL-R1-7B** | 2.5% | 3.6% | 0.7% | 4.7% | 1.0% | 1.3% | 42.7% |
+| **GPT-OSS-20B** | 1.4% | 6.2% | 1.5% | 8.4% | 3.7% | 1.5% | 65.2% |
+
+### Model Performance Hierarchy
+1. **GPT-OSS-120B**: Strongest overall performance (11.6% FINCH Score)
+2. **Arctic-Text2SQL-R1-7B**: Best domain-adapted model despite smaller size (1.5% FINCH Score)
+3. **GPT-OSS-20B**: Solid medium-scale performance (3.0% FINCH Score)
+
+### Key Research Findings
+- **Domain adaptation** outperforms scale alone - Arctic-Text2SQL-R1-7B (7B params) rivals much larger models
+- **Schema-sensitive clauses** (SELECT, FROM, WHERE) remain the primary bottleneck
+- **Query difficulty** shows steep performance degradation: easy queries achieve ~26.5% vs hard queries at ~4.5%
+- **Financial complexity** significantly impacts all models, with even SOTA systems achieving modest absolute performance
+- **FINCH Score correlation**: Provides more nuanced assessment than traditional exact-match metrics
+
+
+## Data Source & Methodology
+
+FINCH consolidates financial databases from multiple sources:
+
+1. **Careful Domain Selection**: Only financial-relevant databases retained
+2. **Comprehensive Validation**: All SQL queries tested for execution
+3. **Error Correction**: Systematic fixing of syntax and schema errors
+4. **Difficulty Annotation**: Query complexity labeled following established guidelines
+5. **Schema Normalization**: All databases converted to SQLite for consistency
+
+The curation process prioritized financial domain relevance while maintaining the diversity and complexity necessary for robust model evaluation.
+
+## Ethical Considerations
+
+- **Public Domain Data**: All source databases are from publicly available benchmarks
+- **Financial Privacy**: No real customer or proprietary financial data included
+- **Synthetic Data**: Financial amounts and transactions are synthetic or anonymized
+- **Research Purpose**: Intended primarily for academic and research applications
+- **Domain Compliance**: Respects financial data handling best practices
+
+## Citation
+
+If you use the FINCH dataset in your research, please cite:
+
+```bibtex
+@inproceedings{singh2025finch,
+  title={FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling},
+  author={Singh, Avinash Kumar and Sarmah, Bhaskarjit and Pasquali, Stefano},
+  booktitle={Proceedings of Advances in Financial AI: Innovations, Risk, and Responsibility in the Era of LLMs (CIKM 2025)},
+  year={2025},
+  organization={ACM}
+}
+```
+
+## Dataset Card Contact
+
+For questions about the FINCH dataset, please contact the research team at Domyn.
+
+**Research Team:**
+- Avinash Kumar Singh (avinash.kumarsingh@domyn.com)
+- Bhaskarjit Sarmah (bhaskarjit.sarmah@domyn.com)  
+- Stefano Pasquali (stefano.pasquali@domyn.com)

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