--- license: mit viewer: false task_categories: - zero-shot-classification - text-classification tags: - uv-script - classification - structured-outputs - zero-shot --- # Hugging Face Dataset Classification With Sieves GPU-accelerated text classification for Hugging Face datasets with guaranteed valid outputs through structured generation with [Sieves](https://github.com/MantisAI/sieves/), [Outlines](https://github.com/dottxt-ai/outlines) and Hugging Face zero-shot pipelines. This is a modified version of https://huggingface.co/datasets/uv-scripts/classification. ## 🚀 Quick Start ```bash # Classify IMDB reviews uv run examples/classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/imdb-classified ``` That's it! No installation, no setup - just `uv run`. ## 📋 Requirements - **GPU Recommended**: Uses GPU-accelerated inference (CPU fallback available but slow) - Python 3.12+ - UV (will handle all dependencies automatically) **Python Package Dependencies** (automatically installed via UV): - `sieves` with engines support (>= 0.17.4) - `typer` (>= 0.12) - `datasets` - `huggingface-hub` ## 🎯 Features - **Guaranteed valid outputs** using structured generation with Outlines guided decoding - **Zero-shot classification** without training data required - **GPU-optimized** for maximum throughput and efficiency - **Multi-label support** for documents with multiple applicable labels - **Flexible model selection** - works with any instruction-tuned transformer model - **Robust text handling** with preprocessing and validation - **Automatic progress tracking** and detailed statistics - **Direct Hub integration** - read and write datasets seamlessly - **Label descriptions** support for providing context to improve accuracy - **Optimized batching** with Sieves' automatic batch processing - **Multiple guided backends** - supports `outlines` to handle any general language model on Hugging Face, and fast Hugging Face zero-shot classification pipelines ## 💻 Usage ### Basic Classification ```bash uv run examples/classify-dataset.py \ --input-dataset \ --column \ --labels \ --model \ --output-dataset ``` ### Arguments **Required:** - `--input-dataset`: Hugging Face dataset ID (e.g., `stanfordnlp/imdb`, `user/my-dataset`) - `--column`: Name of the text column to classify - `--labels`: Comma-separated classification labels (e.g., `"spam,ham"`) - `--model`: Model to use (e.g., `HuggingFaceTB/SmolLM-360M-Instruct`) - `--output-dataset`: Where to save the classified dataset **Optional:** - `--label-descriptions`: Provide descriptions for each label to improve classification accuracy - `--multi-label`: Enable multi-label classification mode (creates multi-hot encoded labels) - `--split`: Dataset split to process (default: `train`) - `--max-samples`: Limit samples for testing - `--shuffle`: Shuffle dataset before selecting samples (useful for random sampling) - `--shuffle-seed`: Random seed for shuffling - `--batch-size`: Batch size for inference (default: 64) - `--max-tokens`: Maximum tokens to generate per sample (default: 200) - `--hf-token`: Hugging Face token (or use `HF_TOKEN` env var) ### Label Descriptions Provide context for your labels to improve classification accuracy: ```bash uv run examples/classify-dataset.py \ --input-dataset user/support-tickets \ --column content \ --labels "bug,feature,question,other" \ --label-descriptions "bug:something is broken,feature:request for new functionality,question:asking for help,other:anything else" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/tickets-classified ``` The model uses these descriptions to better understand what each label represents, leading to more accurate classifications. ### Multi-Label Classification Enable multi-label mode for documents that can have multiple applicable labels: ```bash uv run examples/classify-dataset.py \ --input-dataset ag_news \ --column text \ --labels "world,sports,business,science" \ --multi-label \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/ag-news-multilabel ``` ## 📊 Examples ### Sentiment Analysis ```bash uv run examples/classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,ambivalent,negative" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/imdb-sentiment ``` ### Support Ticket Classification ```bash uv run examples/classify-dataset.py \ --input-dataset user/support-tickets \ --column content \ --labels "bug,feature_request,question,other" \ --label-descriptions "bug:code or product not working as expected,feature_request:asking for new functionality,question:seeking help or clarification,other:general comments or feedback" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/tickets-classified ``` ### News Categorization ```bash uv run examples/classify-dataset.py \ --input-dataset ag_news \ --column text \ --labels "world,sports,business,tech" \ --model HuggingFaceTB/SmolLM-1.7B-Instruct \ --output-dataset user/ag-news-categorized ``` ### Multi-Label News Classification ```bash uv run examples/classify-dataset.py \ --input-dataset ag_news \ --column text \ --labels "world,sports,business,tech" \ --multi-label \ --label-descriptions "world:global and international events,sports:sports and athletics,business:business and finance,tech:technology and innovation" \ --model HuggingFaceTB/SmolLM-1.7B-Instruct \ --output-dataset user/ag-news-multilabel ``` This combines label descriptions with multi-label mode for comprehensive categorization of news articles. ### ArXiv ML Research Classification Classify academic papers into machine learning research areas: ```bash # Fast classification with random sampling uv run examples/classify-dataset.py \ --input-dataset librarian-bots/arxiv-metadata-snapshot \ --column abstract \ --labels "llm,computer_vision,reinforcement_learning,optimization,theory,other" \ --label-descriptions "llm:language models and NLP,computer_vision:image and video processing,reinforcement_learning:RL and decision making,optimization:training and efficiency,theory:theoretical ML foundations,other:other ML topics" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/arxiv-ml-classified \ --split "train" \ --max-samples 100 \ --shuffle # Multi-label for nuanced classification uv run examples/classify-dataset.py \ --input-dataset librarian-bots/arxiv-metadata-snapshot \ --column abstract \ --labels "multimodal,agents,reasoning,safety,efficiency" \ --label-descriptions "multimodal:vision-language and cross-modal models,agents:autonomous agents and tool use,reasoning:reasoning and planning systems,safety:alignment and safety research,efficiency:model optimization and deployment" \ --multi-label \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/arxiv-frontier-research \ --split "train[:1000]" \ --max-samples 50 ``` Multi-label mode is particularly valuable for academic abstracts where papers often span multiple topics and require careful analysis to determine all relevant research areas. ## 🚀 Running Locally vs Cloud This script is optimized to run locally on GPU-equipped machines: ```bash # Local execution with your GPU uv run examples/classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/imdb-classified ``` For cloud deployment, you can use Hugging Face Spaces or other GPU services by adapting the command to your environment. ## 🔧 Advanced Usage ### Random Sampling When working with ordered datasets, use `--shuffle` with `--max-samples` to get a representative sample: ```bash # Get 50 random reviews instead of the first 50 uv run examples/classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/imdb-sample \ --max-samples 50 \ --shuffle \ --shuffle-seed 123 # For reproducibility ``` ### Using Different Models By default, this script works with any instruction-tuned model. Here are some recommended options: ```bash # Lightweight model for fast classification uv run examples/classify-dataset.py \ --input-dataset user/my-dataset \ --column text \ --labels "A,B,C" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/classified # Larger model for complex classification uv run examples/classify-dataset.py \ --input-dataset user/legal-docs \ --column text \ --labels "contract,patent,brief,memo,other" \ --model HuggingFaceTB/SmolLM3-3B-Instruct \ --output-dataset user/legal-classified # Specialized zero-shot classifier uv run examples/classify-dataset.py \ --input-dataset user/my-dataset \ --column text \ --labels "A,B,C" \ --model MoritzLaurer/deberta-v3-large-zeroshot-v2.0 \ --output-dataset user/classified ``` ### Large Datasets Configure `--batch-size` for more effective batch processing with large datasets: ```bash uv run examples/classify-dataset.py \ --input-dataset user/huge-dataset \ --column text \ --labels "A,B,C" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/huge-classified \ --batch-size 128 ``` ## 🤝 How It Works 1. **Sieves**: Provides a zero-shot task pipeline system for structured NLP workflows 2. **Outlines**: Provides guided decoding to guarantee valid label outputs 3. **UV**: Handles all dependencies automatically The script loads your dataset, preprocesses texts, classifies each one with guaranteed valid outputs using Sieves' `Classification` task, then saves the results as a new column in the output dataset. ## 🐛 Troubleshooting ### GPU Not Available This script works best with a GPU but can run on CPU (much slower). To use GPU: - Run on a machine with NVIDIA GPU - Use cloud GPU instances (AWS, GCP, Azure, etc.) - Use Hugging Face Spaces with GPU ### Out of Memory - Use a smaller model (e.g., SmolLM-360M instead of 3B) - Reduce `--batch-size` (try 32, 16, or 8) - Reduce `--max-tokens` for shorter generations ### Invalid/Skipped Texts - Texts shorter than 3 characters are skipped - Empty or None values are marked as invalid - Very long texts are truncated to 4000 characters ### Classification Quality - With Outlines guided decoding, outputs are guaranteed to be valid labels - For better results, use clear and distinct label names - Try `--label-descriptions` to provide context - Use a larger model for nuanced tasks - In multi-label mode, adjust the confidence threshold (defaults to 0.5) ### Authentication Issues If you see authentication errors: - Run `huggingface-cli login` to cache your token - Or set `export HF_TOKEN=your_token_here` - Verify your token has read/write permissions on the Hub ## 🔬 Advanced Workflows ### Full Pipeline Workflow Start with small tests, then run on the full dataset: ```bash # Step 1: Test with small sample uv run examples/classify-dataset.py \ --input-dataset your-dataset \ --column text \ --labels "label1,label2,label3" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/test-classification \ --max-samples 100 # Step 2: If results look good, run on full dataset uv run examples/classify-dataset.py \ --input-dataset your-dataset \ --column text \ --labels "label1,label2,label3" \ --label-descriptions "label1:description,label2:description,label3:description" \ --model HuggingFaceTB/SmolLM-360M-Instruct \ --output-dataset user/final-classification \ --batch-size 64 ``` ## 📝 License This example is provided as part of the [Sieves](https://github.com/MantisAI/sieves/) project.