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Getting Started

  • What is Argilla?
  • 🚀 Quickstart
  • Deployments
    • Docker
    • Docker Quickstart
    • Docker-compose
    • Cloud Providers
    • Install from develop
    • Hugging Face Hub Spaces
    • Google Colab
  • Configurations
    • Elasticsearch
    • Server configuration
    • User Management
  • Terminology

Guides

  • 🍼 Basics
  • 🧑‍💻 Log, load, and prepare data
  • 🔎 Query datasets
  • 📊 Measure datasets with metrics
  • 🕵️ Explain predictions and bias
  • 👂 Schedule jobs using listeners
  • 👨🏽‍🏫 Use Active Learning
  • 👮 Programmatic labelling with rules
  • 🔫 Annotate with few-shot learning
  • 🔦 Label with semantic search

Tutorials

  • What are Tutorials?
  • MLOps Steps
    • 🏷 Labelling
    • 💪🏽 Training
    • 👨🏽‍💻 Deploying
    • 📊 Monitoring
  • NLP Tasks
    • 📕📗 Text Classification
    • 👨🏽💬 Text Generation
    • 🈴🈯️ Token Classification
  • Libraries
    • FastAPI
    • BentoML
    • DVC
    • Google Colab
    • spaCy
    • Stanza
    • Hugging Face Transformers
    • Hugging Face Disaggregators
    • Sentence Transformers
    • Flair
    • SetFit
    • Small-Text
    • modAL
    • classy-classification
    • OpenAI
    • Skweak
    • Snorkel
    • Transformers Interpret
    • Cleanlab
    • SHAP
    • OpenAI
  • Techniques
    • 🍼 Basics
    • 👨🏽‍🏫 Active Learning
    • 🔎 Explainability and bias
    • 🔫 Few-shot classification
    • 👮 Weak Supervision

Reference

  • Python
    • Client
    • Metrics
    • Labeling
    • Monitoring
    • Listeners
  • Argilla UI
    • Pages
    • Features
  • Data Model
  • Notebooks
    • 🔐 Backup and version Argilla Datasets using DVC
    • 🚀 Run Argilla with a Transformer in an active learning loop and a free GPU in your browser
    • 💾 Monitor FastAPI model endpoints
    • 🗺️ Add bias-equality features to datasets with disaggregators
    • 💡 Build and evaluate a zero-shot sentiment classifier with GPT-3
    • Bulk Labelling Multimodal Data
    • The Dataset: a ‘real world’ multimodal data
    • 📷 Zero Shot Image Classification
    • 📚 🙈 Zero-shot text classification
    • Consolidate our data with Argilla (Multi-modal) bulk labelling 📷 ⚓ 📚
    • Fewshot Classification of bulk labelled data
    • 💨 Label data with semantic search and Sentence Transformers
    • 🧱 Augment weak supervision rules with Sentence Transformers
    • 🔫 Zero-shot and few-shot classification with SetFit
    • 🗂 Multi-label text classification with weak supervision
    • 📰 Train a text classifier with weak supervision
    • 🔫 Evaluate a zero-shot NER with Flair
    • 🐭 Train a NER model with skweak
    • 💫 Explore and analyze spaCy NER predictions
    • 🧐 Find label errors with cleanlab
    • Text Classification Model Comparison
    • 🕵️‍♀️ Analize predictions with explainability methods
    • 🧼 Clean labels using your model’s loss
    • INSERT TITLE
    • Text classification active learning with classy-classification
    • 🤔 Text Classification active learning with ModAL
    • 🤯 Few-shot classification with SetFit
    • 🤗 Train a sentiment classifier with SetFit
    • 👂 Text Classification active learning with small-text
    • 🏷️ Fine-tune a sentiment classifier with your own data
    • 🕸️ Train a summarization model with Unstructured and Transformers
  • Telemetry

Community

  • Slack
  • Github
  • Discussion forum
  • Developer documentation
  • Contributor Documentation
  • Migration from Rubrix
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modAL#

These tutorials show you how Argilla can be used in combination with modAL.
modAL is an active learning framework for Python3, designed with modularity, flexibility and extensibility in mind.

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🔁 Using modAL for Active Learning

MLOps Steps: Training
NLP Tasks: TextClassification
Libraries: modAL
Techniques: Active Learning

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