A pragmatic AI-powered data labeling library.
Project description
Auto-Labeler
An AI-powered data labeling library for Python.
Key Features
- 🔍 Discovery: Automatically suggest labels/taxonomy using Iterative or Embedding-based discovery.
- 🏷️ Assignment: Labels your dataset using LLMs (Gemini, OpenAI, Anthropic, etc.).
- ⚡ Batching & Async: High-throughput processing for large datasets.
- 💾 Disk Caching: Save costs and time with local persistence.
- 💰 cost Tracking: Real-time USD cost estimation for every run.
- 🛡️ Validation: Pydantic-powered fail-fast checks for your data.
Installation
pip install auto-labeler-ai
Quick Start
from auto_labeler import AutoLabeler
import pandas as pd
labeler = AutoLabeler(model_name="gemini/gemini-2.5-flash")
results = labeler.label_dataset(
pd.read_csv("data.csv"),
labels=["Urgent", "Billing", "General"],
context="Customer support tickets"
)
print(results[["text", "label"]]) # output column is always 'label'
print(labeler.get_usage())
Documentation
For full guides on Advanced discovery, Caching, and API reference, visit our: 👉 Documentation Site
Testing
pytest
License
MIT
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