Add dataset card

README.md

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+---
+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
+
+```json
+{
+  "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