Update README.md

README.md

@@ -1,4 +1,367 @@
 ---
 license: mit
+task_categories:
+- zero-shot-classification
+- text-classification
+tags:
+- uv-script
+- classification
+- zero-shot
+- structured-outputs
 ---
-# Hugging Face Dataset 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](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/create_classification_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/create_classification_dataset.py \
+  --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:
+
+```bash
+uv run examples/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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/create_classification_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.