Datasets:

stindardlogic
/

data-extraction-sft-100k

language:
  - en
license: apache-2.0
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
pretty_name: Data Extraction SFT (100K)
tags:
  - information-extraction
  - structured-extraction
  - nlp
  - enterprise
  - document-parsing
  - json-extraction
  - table-extraction
  - ner
  - sft
  - supervised-fine-tuning
  - synthetic
configs:
  - config_name: default
    data_files:
      - split: train
        path: data-extraction-sft-100k.jsonl

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