--- viewer: false tags: [uv-script, classification, vllm, structured-outputs, gpu-required] --- # Dataset Classification Script GPU-accelerated text classification for Hugging Face datasets with guaranteed valid outputs through structured generation. ## 🚀 Quick Start ```bash # Classify IMDB reviews uv run classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --output-dataset user/imdb-classified ``` That's it! No installation, no setup - just `uv run`. ## 📋 Requirements - **GPU Required**: Uses GPU-accelerated inference - Python 3.10+ - UV (will handle all dependencies automatically) - vLLM >= 0.6.6 ## 🎯 Features - **Guaranteed valid outputs** using structured generation with guided decoding - **Zero-shot classification** without training data required - **GPU-optimized** for maximum throughput and efficiency - **Default model**: HuggingFaceTB/SmolLM3-3B (fast 3B model with thinking capabilities) - **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 - **Reasoning mode** for interpretable classifications with thinking traces - **JSON output parsing** for reliable extraction from reasoning mode - **Optimized batching** with vLLM's automatic batch processing - **Multiple guided backends** - supports outlines, xgrammar, and more ## 💻 Usage ### Basic Classification ```bash uv run classify-dataset.py \ --input-dataset \ --column \ --labels \ --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"`) - `--output-dataset`: Where to save the classified dataset **Optional:** - `--model`: Model to use (default: **`HuggingFaceTB/SmolLM3-3B`** - a fast 3B parameter model) - `--label-descriptions`: Provide descriptions for each label to improve classification accuracy - `--enable-reasoning`: Enable reasoning mode with thinking traces (adds reasoning column) - `--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 (default: 42) - `--temperature`: Generation temperature (default: 0.1) - `--guided-backend`: Backend for guided decoding (default: `outlines`) - `--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 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" \ --output-dataset user/tickets-classified ``` The model uses these descriptions to better understand what each label represents, leading to more accurate classifications. ### Reasoning Mode Enable thinking traces for interpretable classifications: ```bash uv run classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative,neutral" \ --enable-reasoning \ --output-dataset user/imdb-with-reasoning ``` When `--enable-reasoning` is used: - The model generates step-by-step reasoning using SmolLM3's thinking capabilities - Output includes three columns: `classification`, `reasoning`, and `parsing_success` - Final answer must be in JSON format: `{"label": "chosen_label"}` - Useful for understanding complex classification decisions - Trade-off: Slower but more interpretable ## 📊 Examples ### Sentiment Analysis ```bash uv run classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --output-dataset user/imdb-sentiment ``` ### Support Ticket Classification ```bash uv run 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" \ --output-dataset user/tickets-classified ``` ### News Categorization ```bash uv run classify-dataset.py \ --input-dataset ag_news \ --column text \ --labels "world,sports,business,tech" \ --output-dataset user/ag-news-categorized \ --model meta-llama/Llama-3.2-3B-Instruct ``` ### Complex Classification with Reasoning ```bash uv run classify-dataset.py \ --input-dataset user/customer-feedback \ --column text \ --labels "very_positive,positive,neutral,negative,very_negative" \ --label-descriptions "very_positive:extremely satisfied,positive:generally satisfied,neutral:mixed feelings,negative:dissatisfied,very_negative:extremely dissatisfied" \ --enable-reasoning \ --output-dataset user/feedback-analyzed ``` This combines label descriptions with reasoning mode for maximum interpretability. ### ArXiv ML Research Classification Classify academic papers into machine learning research areas: ```bash # Fast classification with random sampling uv run 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" \ --output-dataset user/arxiv-ml-classified \ --split "train[:10000]" \ --max-samples 100 \ --shuffle # With reasoning for nuanced classification uv run 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" \ --enable-reasoning \ --output-dataset user/arxiv-frontier-research \ --split "train[:1000]" \ --max-samples 50 ``` The reasoning mode is particularly valuable for academic abstracts where papers often span multiple topics and require careful analysis to determine the primary focus. ## 🚀 Running on HF Jobs Optimized for [Hugging Face Jobs](https://huggingface.co/docs/hub/spaces-gpu-jobs) (requires Pro subscription or Team/Enterprise organization): ```bash # Run on L4 GPU with vLLM image hf jobs uv run \ --flavor l4x1 \ --image vllm/vllm-openai:latest \ https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --output-dataset user/imdb-classified ``` ### GPU Flavors - `t4-small`: Budget option for smaller models - `l4x1`: Good balance for 7B models - `a10g-small`: Fast inference for 3B models - `a10g-large`: More memory for larger models - `a100-large`: Maximum performance ## 🔧 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 classify-dataset.py \ --input-dataset stanfordnlp/imdb \ --column text \ --labels "positive,negative" \ --output-dataset user/imdb-sample \ --max-samples 50 \ --shuffle \ --shuffle-seed 123 # For reproducibility ``` This is especially important for: - Chronologically ordered datasets (news, papers, social media) - Pre-sorted datasets (by rating, category, etc.) - Testing on diverse samples before processing the full dataset ### Using Different Models By default, this script uses **HuggingFaceTB/SmolLM3-3B** - a fast, efficient 3B parameter model that's perfect for most classification tasks. You can easily use any other instruction-tuned model: ```bash # Larger model for complex classification uv run classify-dataset.py \ --input-dataset user/legal-docs \ --column text \ --labels "contract,patent,brief,memo,other" \ --output-dataset user/legal-classified \ --model Qwen/Qwen2.5-7B-Instruct ``` ### Large Datasets vLLM automatically handles batching for optimal performance. For very large datasets, it will process efficiently without manual intervention: ```bash uv run classify-dataset.py \ --input-dataset user/huge-dataset \ --column text \ --labels "A,B,C" \ --output-dataset user/huge-classified ``` ## 📈 Performance - **SmolLM3-3B (default)**: ~50-100 texts/second on A10 - **7B models**: ~20-50 texts/second on A10 - vLLM automatically optimizes batching for best throughput - Performance scales with GPU memory and compute capability ## 🤝 How It Works 1. **vLLM**: Provides efficient GPU batch inference with automatic batching 2. **Guided Decoding**: Uses outlines backend to guarantee valid label outputs 3. **Structured Generation**: Constrains model outputs to exact label choices 4. **UV**: Handles all dependencies automatically The script loads your dataset, preprocesses texts, classifies each one with guaranteed valid outputs, then saves the results as a new column in the output dataset. ## 🐛 Troubleshooting ### CUDA Not Available This script requires a GPU. Run it on: - A machine with NVIDIA GPU - HF Jobs (recommended) - Cloud GPU instances ### Out of Memory - Use a smaller model - Use a larger GPU (e.g., a100-large) ### 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 guided decoding, outputs are guaranteed to be valid labels - For better results, use clear and distinct label names - Try the `reasoning` prompt style for complex classifications - Use a larger model for nuanced tasks ### vLLM Version Issues If you see `ImportError: cannot import name 'GuidedDecodingParams'`: - Your vLLM version is too old (requires >= 0.6.6) - The script specifies the correct version in its dependencies - UV should automatically install the correct version ## 🔬 Advanced Workflows For complex real-world workflows that integrate UV scripts with the Python HF Jobs API, see the [ArXiv ML Trends example](examples/arxiv-workflow/). This demonstrates: - **Multi-stage pipelines**: Data preparation → GPU classification → Analysis - **Python API orchestration**: Using `run_uv_job()` to manage GPU jobs programmatically - **Production patterns**: Error handling, parallel execution, and incremental updates - **Cost optimization**: Choosing appropriate compute resources for each task ```python # Example: Submit a classification job via Python API from huggingface_hub import run_uv_job job = run_uv_job( script="https://huggingface.co/datasets/uv-scripts/classification/raw/main/classify-dataset.py", args=["--input-dataset", "my/dataset", "--labels", "A,B,C"], flavor="l4x1", image="vllm/vllm-openai:latest" ) result = job.wait() ``` ## 📝 License This script is provided as-is for use with the UV Scripts organization.