Add dataset card

README.md

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+---
+language:
+  - en
+license: apache-2.0
+tags:
+  - information-extraction
+  - json
+  - rag
+  - structured-data
+  - synthetic
+  - legacy-database-modernization
+task_categories:
+  - text-generation
+  - feature-extraction
+size_categories:
+  - 1B<n<10B
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: "data/train-*.parquet"
+---
+
+# Helios Nano JSON Data
+
+Large-scale synthetic dataset for training small language models (SLMs) on
+**structured information extraction** — converting unstructured text into JSON.
+
+## Purpose
+
+Designed for fine-tuning a 400M-parameter extraction engine that:
+- Reads unstructured business documents (invoices, medical records, contracts, etc.)
+- Follows a provided JSON schema
+- Outputs clean, structured JSON
+
+Ideal for **legacy database modernization** and **RAG pipelines**.
+
+## Dataset Structure
+
+Each row contains:
+
+| Column | Type | Description |
+|---|---|---|
+| `industry` | string | Source industry (finance, healthcare, hr, legal, …) |
+| `doc_type` | string | Document type (invoice, prescription, contract, …) |
+| `schema_json` | string | JSON schema the model should extract |
+| `raw_text` | string | Unstructured source document |
+| `extracted_json` | string | Gold-standard structured extraction |
+
+## Coverage
+
+**16 industries**, **41 document types**, including:
+- Finance: invoices, receipts, payroll, wire transfers, tax summaries, bank transactions
+- Healthcare: patient records, prescriptions, lab results, referrals
+- HR: employee records, job postings, performance reviews
+- Legal: contract summaries
+- Real Estate: property listings, lease agreements
+- Logistics: shipping notices, purchase orders, inventory, customs declarations
+- Retail: orders, returns
+- Insurance: claims
+- Education: enrollment, scholarships
+- Manufacturing: quality inspections, maintenance logs
+- Government: business licenses, building permits
+- And more…
+
+## Format Diversity
+
+Text fields use randomized formatting for dates (`Sept 29` / `09-29-2024` /
+`2024-09-29`), currency (`$1,234.56` / `USD 1234.56`), phone numbers, IDs,
+and document layout (formal headers vs. narrative prose vs. email style).
+
+## Stats
+
+- **Shards**: 26
+- **Disk size**: 12.2 GB (Snappy-compressed Parquet)
+- **Target**: 10B tokens (BPE, vocab 32768)
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("respinosamena/Helios-Nano-JSON-Data", split="train")
+print(ds[0])
+```
+
+## Training Prompt Format
+
+```
+<|schema|>{schema_json}<|end_turn|>
+<|document|>{raw_text}<|end_turn|>
+<|extract|>{extracted_json}<|end_turn|>
+```
+
+## License
+
+Apache 2.0