stindardlogic/data-extraction-sft-100k

Data Extraction SFT (100K)

100,000 ShareGPT conversations demonstrating structured information extraction from unstructured text. Each example takes a real-world document (invoice, contract, resume, research abstract, meeting notes, log files) and extracts the relevant information into JSON, markdown tables, or other structured formats.

Motivation

Information extraction is one of the highest-value NLP tasks in enterprise settings. Common model failures include:

• Format drift: Asked for JSON, produces a mix of prose and JSON
• Missed fields: Extracts some fields but silently omits others
• Hallucinated data: Infers or invents values not present in the source
• Wrong output format: Produces a table when JSON was requested, or vice versa
• Poor handling of ambiguity: Doesn't flag when source data is incomplete or contradictory
• Inconsistent structure: JSON with inconsistent key naming, tables with misaligned columns

This dataset trains models to extract accurately, completely, and in the exact format requested.

Dataset Description

100,000 conversations across 23 extraction types:

Extraction Type Distribution

Extraction Type	Source Format	Output Format
contact_information	Email signature	JSON
financial_data	Earnings report	Table
job_posting	Job description	JSON
dates_events	Project timeline	Table
contract_terms	Legal clause	Bullet list
product_specifications	Product description	Table
action_items	Meeting notes	Table
named_entities	News article	Categorized list
invoice_parsing	Invoice	JSON
prescription_data	Prescription	JSON
network_entities	Server log	Categorized list
requirements_classification	Requirements doc	Classified list
clinical_trial_data	Research abstract	JSON
sentiment_analysis	Customer review	Analysis
recipe_ingredients	Recipe	JSON
meeting_decision	Meeting transcript	Structured analysis
api_error_codes	API documentation	Table
business_metrics	KPI dashboard	JSON
resume_parsing	Resume	JSON
compliance_violations	Audit report	Table
real_estate_listing	Property listing	JSON
performance_review	HR document	Structured summary
mathematical_formulas	Scientific text	Structured analysis

Format

{
  "conversations": [
    {
      "from": "human",
      "value": "Extract all contact information from the following email signature and return it as JSON:

---
Dr. Sarah Mitchell...
---"
    },
    {
      "from": "gpt",
      "value": "```json
{"name": "Dr. Sarah Mitchell", "title": "Chief Research Officer"...}
```"
    }
  ],
  "metadata": {
    "extraction_type": "contact_information",
    "source_format": "email_signature",
    "output_format": "json"
  },
  "id": "abc123"
}

Key Properties of Responses

1. Format fidelity: JSON when JSON is requested, markdown tables when tables are requested. Code blocks used correctly. Keys are consistently named.

2. Complete extraction: All entities in the source are extracted. Nothing is silently skipped. When the source is ambiguous, the extraction notes the ambiguity.

3. No hallucination: Only information present in the source is extracted. Missing values are represented as null or omitted, not invented.

4. Appropriate structure: Nested JSON for hierarchical data, flat tables for tabular data, categorized lists for multi-type entity extraction.

5. Relevant metadata: Extracted data includes context that makes it actionable (e.g., units for measurements, calculation details for formulas, severity indicators for compliance violations).

6. Real-world document types: Source documents represent actual professional formats — invoices with realistic line items, resumes with realistic career progressions, API docs with standard error code patterns.

Use Cases

• SFT fine-tuning for document processing pipelines
• Training AI for enterprise automation (invoice processing, contract review, HR document parsing)
• Building RAG pre-processing models that extract structured facts from documents
• Improving model performance on information extraction benchmarks
• Training models for legal tech, HR tech, and fintech applications
• Building AI assistants for analysts who work with unstructured data

License

Apache 2.0