mantisnlp/classification-with-sieves

Hugging Face Dataset Classification With Sieves

GPU-accelerated text classification for Hugging Face datasets with guaranteed valid outputs through structured generation with Sieves, Outlines and Hugging Face zero-shot pipelines.

This is a modified version of https://huggingface.co/datasets/uv-scripts/classification.

🚀 Quick Start

# Classify IMDB reviews
uv run classify-dataset.py classify \
  --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

uv run classify-dataset.py classify \
  --input-dataset <dataset-id> \
  --column <text-column> \
  --labels <comma-separated-labels> \
  --model <model-id> \
  --output-dataset <output-id>

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:

uv run classify-dataset.py classify \
  --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:

uv run classify-dataset.py classify \
  --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

uv run classify-dataset.py classify \
  --input-dataset stanfordnlp/imdb \
  --column text \
  --labels "positive,ambivalent,negative" \
  --model HuggingFaceTB/SmolLM-360M-Instruct \
  --output-dataset user/imdb-sentiment

Support Ticket Classification

uv run classify-dataset.py classify \
  --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

uv run classify-dataset.py classify \
  --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

uv run classify-dataset.py classify \
  --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:

# Fast classification with random sampling
uv run classify-dataset.py classify \
  --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 classify-dataset.py classify \
  --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:

# Local execution with your GPU
uv run classify-dataset.py classify \
  --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:

# Get 50 random reviews instead of the first 50
uv run classify-dataset.py classify \
  --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:

# Lightweight model for fast classification
uv run classify-dataset.py classify \
  --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 classify-dataset.py classify \
  --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 classify-dataset.py classify \
  --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:

uv run classify-dataset.py classify \
  --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:

# Step 1: Test with small sample
uv run classify-dataset.py classify \
  --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 classify-dataset.py classify \
  --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 project.