hugging-face-datasets

# Overview This skill provides tools to manage datasets on the Hugging Face Hub with a focus on creation, configuration, content management, and SQL-based data manipulation. It is designed to complement the existing Hugging Face MCP server by providing dataset editing and querying capabilities. ## Integration with HF MCP Server - **Use HF MCP Server for**: Dataset discovery, search, and metadata retrieval - **Use This Skill for**: Dataset creation, content editing, SQL queries, data transformation, and structured data formatting # Version 2.1.0 # Dependencies # This skill uses PEP 723 scripts with inline dependency management # Scripts auto-install requirements when run with: uv run scripts/<script>.py - uv (Python package manager) - Getting Started: See "Usage Instructions" below for PEP 723 usage # Core Capabilities ## 1. Dataset Lifecycle Management - **Initialize**: Create new dataset repositories with proper structure - **Configure**: Store detailed configuration including system prompts and metadata - **Stream Updates**: Add rows efficiently without downloading entire datasets ## 2. SQL-Based Dataset Querying (NEW) Query any Hugging Face dataset using DuckDB SQL via `scripts/sql_manager.py`: - **Direct Queries**: Run SQL on datasets using the `hf://` protocol - **Schema Discovery**: Describe dataset structure and column types - **Data Sampling**: Get random samples for exploration - **Aggregations**: Count, histogram, unique values analysis - **Transformations**: Filter, join, reshape data with SQL - **Export & Push**: Save results locally or push to new Hub repos ## 3. Multi-Format Dataset Support Supports diverse dataset types through template system: - **Chat/Conversational**: Chat templating, multi-turn dialogues, tool usage examples - **Text Classification**: Sentiment analysis, intent detection, topic classification - **Question-Answering**: Reading comprehension, factual QA, knowledge bases - **Text Completion**: Language modeling, code completion, creative writing - **Tabular Data**: Structured data for regression/classification tasks - **Custom Formats**: Flexible schema definition for specialized needs ## 4. Quality Assurance Features - **JSON Validation**: Ensures data integrity during uploads - **Batch Processing**: Efficient handling of large datasets - **Error Recovery**: Graceful handling of upload failures and conflicts # Usage Instructions The skill includes two Python scripts that use PEP 723 inline dependency management: > **All paths are relative to the directory containing this SKILL.md file.** > Scripts are run with: `uv run scripts/<script>.py [arguments]` - `scripts/dataset_manager.py` - Dataset creation and management - `scripts/sql_manager.py` - SQL-based dataset querying and transformation ### Prerequisites - `uv` package manager installed - `HF_TOKEN` environment variable must be set with a Write-access token --- New repositories created by `dataset_manager.py init` are private by default. Pass `--public` to publish a new repository; `--private` remains accepted. `add_rows` and `quick_setup` return a nonzero exit status if validation or an upload fails, so automation should check their exit status before proceeding. # SQL Dataset Querying (sql_manager.py) Query, transform, and push Hugging Face datasets using DuckDB SQL. The `hf://` protocol provides direct access to any public dataset (or private with token). ## Credential Setup HuggingFace token is auto-injected by openscience when connected via the dashboard. ```bash # Verify credentials [ -n "$HF_TOKEN" ] && echo "HF_TOKEN set" || echo "NOT SET" ``` If not set: add your Hugging Face token in Customize → Tools or export `HF_TOKEN` locally. ## Quick Start ```bash # Query a dataset uv run scripts/sql_manager.py query \ --dataset "cais/mmlu" \ --sql "SELECT * FROM data WHERE subject='nutrition' LIMIT 10" # Get dataset schema uv run scripts/sql_manager.py describe --dataset "cais/mmlu" # Sample random rows uv run scripts/sql_manager.py sample --dataset "cais/mmlu" --n 5 # Count rows with filter uv run scripts/sql_manager.py count --dataset "cais/mmlu" --where "subject='nutrition'" ``` ## SQL Query Syntax Use `data` as the table name in your SQL - it gets replaced with the actual `hf://` path: ```sql -- Basic select SELECT * FROM data LIMIT 10 -- Filtering SELECT * FROM data WHERE subject='nutrition' -- Aggregations SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject ORDER BY cnt DESC -- Column selection and transformation SELECT question, choices[answer] AS correct_answer FROM data -- Regex matching SELECT * FROM data WHERE regexp_matches(question, 'nutrition|diet') -- String functions SELECT regexp_replace(question, '\n', '') AS cleaned FROM data ``` ## Common Operations ### 1. Explore Dataset Structure ```bash # Get schema uv run scripts/sql_manager.py describe --dataset "cais/mmlu" # Get unique values in column uv run scripts/sql_manager.py unique --dataset "cais/mmlu" --column "subject" # Get value distribution uv run scripts/sql_manager.py histogram --dataset "cais/mmlu" --column "subject" --bins 20 ``` ### 2. Filter and Transform ```bash # Complex filtering with SQL uv run scripts/sql_manager.py query \ --dataset "cais/mmlu" \ --sql "SELECT subject, COUNT(*) as cnt FROM data GROUP BY subject HAVING cnt > 100" # Using transform command uv run scripts/sql_manager.py transform \ --dataset "cais/mmlu" \ --select "subject, COUNT(*) as cnt" \ --group-by "subject" \ --order-by "cnt DESC" \ --limit 10 ``` ### 3. Create Subsets and Push to Hub ```bash # Query and push to new dataset uv run scripts/sql_manager.py query \ --dataset "cais/mmlu" \ --sql "SELECT * FROM data WHERE subject='nutrition'" \ --push-to "username/mmlu-nutrition-subset" \ --private # Transform and push uv run scripts/sql_manager.py transform \ --dataset "ibm/duorc" \ --config "ParaphraseRC" \ --select "question, answers" \ --where "LENGTH(question) > 50" \ --push-to "username/duorc-long-questions" ``` ### 4. Export to Local Files ```bash # Export to Parquet uv run scripts/sql_manager.py export \ --dataset "cais/mmlu" \ --sql "SELECT * FROM data WHERE subject='nutrition'" \ --output "nutrition.parquet" \ --format parquet # Export to JSONL uv run scripts/sql_manager.py export \ --dataset "cais/mmlu" \ --sql "SELECT * FROM data LIMIT 100" \ --output "sample.jsonl" \ --format jsonl ``` ### 5. Working with Dataset Configs/Splits ```bash # Specify config (subset) uv run scripts/sql_manager.py query \ --dataset "ibm/duorc" \ --config "ParaphraseRC" \ --sql "SELECT * FROM data LIMIT 5" # Specify split uv run scripts/sql_manager.py query \ --dataset "cais/mmlu" \ --split "test" \ --sql "SELECT COUNT(*) FROM data" # Query all splits uv run scripts/sql_manager.py query \ --dataset "cais/mmlu" \ --split "*" \ --sql "SELECT * FROM data LIMIT 10" ``` ### 6. Raw SQL with Full Paths For complex queries or joining datasets: ```bash uv run scripts/sql_manager.py raw --sql " SELECT a.*, b.* FROM 'hf://datasets/dataset1@~parquet/default/train/*.parquet' a JOIN 'hf://datasets/dataset2@~parquet/default/train/*.parquet' b ON a.id = b.id LIMIT 100 " ``` ## Python API Usage ```python from sql_manager import HFDatasetSQL sql = HFDatasetSQL() # Query results = sql.query("cais/mmlu", "SELECT * FROM data WHERE subject='nutrition' LIMIT 10") # Get schema schema = sql.describe("cais/mmlu") # Sample samples = sql.sample("cais/mmlu", n=5, seed=42) # Count count = sql.count("cais/mmlu", where="subject='nutrition'") # Histogram dist = sql.histogram("cais/mmlu", "subject") # Filter and transform results = sql.filter_and_transform( "cais/mmlu", select="subject, COUNT(*) as cnt", group_by="subject", order_by="cnt DESC", limit=10 ) # Push to Hub url = sql.push_to_hub( "cais/mmlu", "username/nutrition-subset", sql="SELECT * FROM data WHERE subject='nutrition'", private=True ) # Export locally sql.export_to_parquet("cais/mmlu", "output.parquet", sql="SELECT * FROM data LIMIT 100") sql.close() ``` ## HF Path Format DuckDB uses the `hf://` protocol to access datasets: ``` hf://datasets/{dataset_id}@{revision}/{config}/{split}/*.parquet ``` Examples: - `hf://datasets/cais/mmlu@~parquet/default/train/*.parquet` - `hf://datasets/ibm/duorc@~parquet/ParaphraseRC/test/*.parquet` The `@~parquet` revision provides auto-converted Parquet files for any dataset format. ## Useful DuckDB SQL Functions ```sql -- String functions LENGTH(column) -- String length regexp_replace(col, '\n', '') -- Regex replace regexp_matches(col, 'pattern') -- Regex match LOWER(col), UPPER(col) -- Case conversion -- Array functions choices[0] -- Array indexing (0-based) array_length(choices) -- Array length unnest(choices) -- Expand array to rows -- Aggregations COUNT(*), SUM(col), AVG(col) GROUP BY col HAVING condition -- Sampling USING SAMPLE 10 -- Random sample USING SAMPLE 10 (RESERVOIR, 42) -- Reproducible sample -- Window functions ROW_NUMBER() OVER (PARTITION BY col ORDER BY col2) ``` --- # Dataset Creation (dataset_manager.py) ### Recommended Workflow **1. Discovery (Use HF MCP Server):** ```python # Use HF MCP tools to find existing datasets search_datasets("conversational AI training") get_dataset_details("username/dataset-name") ``` **2. Creation (Use This Skill):** ```bash # Initialize new dataset uv run scripts/dataset_manager.py init --repo_id "your-username/dataset-name" [--private | --public] # Configure with detailed system prompt uv run scripts/dataset_manager.py config --repo_id "your-username/dataset-name" --system_prompt "$(cat system_prompt.txt)" ``` **3. Content Management (Use This Skill):** ```bash # Quick setup with any template uv run scripts/dataset_manager.py quick_setup \ --repo_id "your-username/dataset-name" \ --template classification # Add data with template validation uv run scripts/dataset_manager.py add_rows \ --repo_id "your-username/dataset-name" \ --template qa \ --rows_json "$(cat your_qa_data.json)" ``` ### Template-Based Data Structures **1. Chat Template (`--template chat`)** ```json { "messages": [ {"role": "user", "content": "Natural user request"}, {"role": "assistant", "content": "Response with tool usage"}, {"role": "tool", "content": "Tool response", "tool_call_id": "call_123"} ], "scenario": "Description of use case", "complexity": "simple|intermediate|advanced" } ``` **2. Classification Template (`--template classification`)** ```json { "text": "Input text to be classified", "label": "classification_label", "confidence": 0.95, "metadata": {"domain": "technology", "language": "en"} } ``` **3. QA Template (`--template qa`)** ```json { "question": "What is the question being asked?", "answer": "The complete answer", "context": "Additional context if needed", "answer_type": "factual|explanatory|opinion", "difficulty": "easy|medium|hard" } ``` **4. Completion Template (`--template completion`)** ```json { "prompt": "The beginning text or context", "completion": "The expected continuation", "domain": "code|creative|technical|conversational", "style": "description of writing style" } ``` **5. Tabular Template (`--template tabular`)** ```json { "columns": [ {"name": "feature1", "type": "numeric", "description": "First feature"}, {"name": "target", "type": "categorical", "description": "Target variable"} ], "data": [