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Getting Started

  • What is Argilla?
  • Quickstart
  • Setup and installation
    • Docker
    • Docker-compose
    • Elasticsearch Configuration
    • Server configuration
    • User Management
    • Cloud Providers
    • Install from develop
  • Terminology

Deep Dives

  • What are Deep Dives?
  • Features
    • Datasets
    • Metrics
    • Queries
  • MLOps Steps
    • ๐Ÿท Labelling
    • ๐Ÿ’ช๐Ÿฝ Training
    • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿ’ป Deploying
    • ๐Ÿ“Š Monitoring
  • NLP Tasks
    • ๐Ÿ“•๐Ÿ“— Text Classification
    • ๐Ÿ‘จ๐Ÿฝ๐Ÿ’ฌ Text Generation
    • ๐Ÿˆด๐Ÿˆฏ๏ธ Token Classification
  • Libraries
  • Techniques
    • ๐Ÿผ Basics
    • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿซ Active Learning
    • ๐Ÿ”Ž Explainability and bias
    • ๐Ÿ”ซ Few-shot classification
    • ๐Ÿ‘ฎ Weak Supervision

Tutorials

  • What are Tutorials?
  • MLOps Steps
    • ๐Ÿท Labelling
    • ๐Ÿ’ช๐Ÿฝ Training
    • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿ’ป Deploying
    • ๐Ÿ“Š Monitoring
  • NLP Tasks
    • ๐Ÿ“•๐Ÿ“— Text Classification
    • ๐Ÿ‘จ๐Ÿฝ๐Ÿ’ฌ Text Generation
    • ๐Ÿˆด๐Ÿˆฏ๏ธ Token Classification
  • Libraries
    • FastAPI
    • BentoML
    • spaCy
    • Stanza
    • Hugging Face Transformers
    • Sentence Transformers
    • Flair
    • SetFit
    • Small-Text
    • modAL
    • Skweak
    • Snorkel
    • Transformers Interpret
    • Cleanlab
    • SHAP
  • Techniques
    • ๐Ÿผ Basics
    • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿซ Active Learning
    • ๐Ÿ”Ž Explainability and bias
    • ๐Ÿ”ซ Few-shot classification
    • ๐Ÿ‘ฎ Weak Supervision

Reference

  • Python
    • Client
    • Metrics
    • Labeling
    • Listeners
  • Argilla UI
    • Pages
    • Features
  • Data Model
  • Notebooks
    • ๐Ÿ’พ Monitor FastAPI predictions
    • ๐Ÿงฑ Extending weak supervision workflows with sentence embeddings
    • ๐Ÿ—‚ Weak supervision in multi-label text classification tasks
    • lets apply Weak Labeling again
    • ๐Ÿ“ฐ Building a news classifier with weak supervision
    • ๐Ÿ”ซ Zero-shot NER with Flair
    • ๐Ÿญ Weakly supervised NER with skweak
    • ๐Ÿ’ซ Explore and analyze spaCy NER pipelines
    • ๐Ÿง Find label errors with cleanlab
    • ๐Ÿ•ต๏ธโ€โ™€๏ธ Analyzing predictions with model explainability methods
    • ๐Ÿงผ Clean labels using your model loss
    • ๐Ÿค” Active learning with ModAL and scikit-learn
    • ๐Ÿคฏ Few-shot classification with SetFit and a custom dataset
    • ๐Ÿ‘‚ Active learning for text classification with small-text
    • ๐Ÿท๏ธ Label your data to fine-tune a classifier with Hugging Face
  • Telemetry

Community

  • Slack
  • Github
  • Discussion forum
  • Developer 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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๐Ÿ” Using modAL for Active Learning

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

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