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: Structured Output SFT (100K)
+tags:
+- structured-output
+- json
+- yaml
+- csv
+- xml
+- markdown
+- json-schema
+- openapi
+- formatting
+- sft
+- supervised-fine-tuning
+- synthetic
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: structured-output-sft-100k.jsonl
+---
+
+# Structured Output SFT (100K)
+
+100,000 ShareGPT conversations demonstrating correct generation of structured data formats: JSON, YAML, CSV, XML, Markdown tables, JSON Schema, and OpenAPI fragments. Each example pairs a natural language specification with a valid, well-formed output.
+
+## Motivation
+
+Structured output generation is among the most commercially critical LLM capabilities. Models fail in characteristic ways:
+
+- **Invalid JSON**: unclosed brackets, trailing commas, unquoted keys, mismatched types
+- **Schema violations**: required fields missing, wrong types, values outside enum constraints
+- **Nesting errors**: objects where arrays expected, flat structure instead of hierarchy
+- **Format inconsistency**: CSV with inconsistent quoting, XML without closing tags, YAML indentation errors
+- **Semantic errors**: technically valid JSON that violates the intended schema
+
+This dataset trains models to produce valid, semantically correct structured output from natural language specifications.
+
+## Dataset Description
+
+**100,000 conversations** across 7 output formats and 20 task types:
+
+### Format Distribution
+
+| Format | Count | % |
+|---|---|---|
+| JSON | ~36,400 | 36% |
+| YAML | ~18,200 | 18% |
+| Markdown Tables | ~18,200 | 18% |
+| CSV | ~13,600 | 14% |
+| JSON Schema | ~4,500 | 5% |
+| Markdown (docs) | ~4,500 | 5% |
+| XML | ~4,500 | 5% |
+
+### Task Categories
+
+| Category | Examples |
+|---|---|
+| Data extraction (text to JSON) | 4,546 |
+| Schema/config design | 13,637 |
+| API design (request/response/error formats) | 9,091 |
+| Infrastructure config (Docker, Kubernetes, CI) | 13,637 |
+| Data generation (sample/test data) | 9,091 |
+| Data formatting (tables, reports) | 9,090 |
+| Data modeling (trees, state machines) | 9,090 |
+| Technical documentation | 4,545 |
+| Geospatial (GeoJSON) | 4,545 |
+| ML results (confusion matrices) | 4,545 |
+| Time-series data | 4,545 |
+
+## Format Example
+
+```json
+{
+  "conversations": [
+    {
+      "from": "human",
+      "value": "Extract the following information into JSON: 'John Smith, 34 years old, works as a software engineer at Acme Corp...'"
+    },
+    {
+      "from": "gpt",
+      "value": "```json\n{\n  \"name\": \"John Smith\",\n  \"age\": 34,\n  ...\n}\n```"
+    }
+  ],
+  "metadata": {
+    "format": "json",
+    "topic": "user_profile_schema",
+    "output_format": "json",
+    "task": "extraction"
+  },
+  "id": "abc123"
+}
+```
+
+## Key Properties of Responses
+
+**1. Syntactic validity**: Every JSON/YAML/CSV/XML output is syntactically valid and would parse without errors.
+
+**2. Schema adherence**: When a schema is specified (required fields, type constraints, enum values, length limits), the output strictly follows it.
+
+**3. Semantic correctness**: Beyond syntax, outputs are semantically meaningful -- GeoJSON coordinates are [lon, lat] not [lat, lon]; polygon rings are closed; state machine terminal states have no transitions.
+
+**4. Format-specific conventions**: XML declarations, RSS version attributes, GeoJSON `FeatureCollection` wrapper, YAML `---` document separator, CSV quoting -- each format's conventions are followed.
+
+**5. Explanatory notes**: Where non-obvious choices are made (coordinate order, ring closure, terminal states), responses explain why.
+
+**6. Production-quality patterns**: Kubernetes Deployments use `readOnly`, Docker Compose uses named volumes, GitHub Actions use pinned action versions (`actions/checkout@v4`).
+
+## Real-World Use Cases Covered
+
+- **REST API design**: request bodies, response envelopes, error formats, pagination
+- **Infrastructure as Code**: Kubernetes Deployments, Docker Compose, GitHub Actions CI/CD
+- **Data schemas**: JSON Schema, OpenAPI component schemas, Pydantic-like definitions
+- **Data exchange**: webhook payloads, GeoJSON features, RSS feeds
+- **Analytics**: comparison tables, time-series, confusion matrices, cohort data
+- **Configuration**: server config with nested structure, environment-specific settings
+- **Documentation**: API endpoint docs with request/response examples
+
+## Use Cases for Training
+
+- SFT fine-tuning for structured output reliability
+- Training models for JSON mode and function calling
+- Improving model performance on schema-following benchmarks
+- Building AI data entry, extraction, and transformation pipelines
+- Training code assistants that generate config files correctly
+- Complementary to `json-structured-output-dpo-3k` (small DPO) -- this provides large-scale SFT
+
+## License
+
+Apache 2.0