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Getting Started

  • What is Argilla?
  • ๐Ÿš€ 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

  • ๐Ÿผ Basics
  • ๐Ÿง‘โ€๐Ÿ’ป Log, load, and prepare data
  • ๐Ÿ”Ž Query datasets
  • ๐Ÿ“Š Measure datasets with metrics
  • ๐Ÿ•ต๏ธ Explain predictions and bias
  • ๐Ÿ‘‚ Schedule jobs using listeners
  • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿซ Use Active Learning
  • ๐Ÿ‘ฎ Programmatic labelling with rules
  • ๐Ÿ”ซ Annotate with few-shot learning
  • ๐Ÿ”ฆ Label with semantic search

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
    • 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
    • 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
    • Monitoring
    • Listeners
  • Argilla UI
    • Pages
    • Features
  • Data Model
  • Notebooks
    • ๐Ÿ” Backup and version Argilla Datasets using DVC
    • ๐Ÿš€ Run Argilla with a Transformer in an active learning loop and a free GPU in your browser
    • ๐Ÿ’พ Monitor FastAPI model endpoints
    • ๐Ÿ—บ๏ธ Add bias-equality features to datasets with disaggregators
    • ๐Ÿ’ก Build and evaluate a zero-shot sentiment classifier with GPT-3
    • Bulk Labelling Multimodal Data
    • The Dataset: a โ€˜real worldโ€™ multimodal data
    • ๐Ÿ“ท Zero Shot Image Classification
    • ๐Ÿ“š ๐Ÿ™ˆ Zero-shot text classification
    • Consolidate our data with Argilla (Multi-modal) bulk labelling ๐Ÿ“ท โš“ ๐Ÿ“š
    • Fewshot Classification of bulk labelled data
    • ๐Ÿ’จ Label data with semantic search and Sentence Transformers
    • ๐Ÿงฑ Augment weak supervision rules with Sentence Transformers
    • ๐Ÿ”ซ Zero-shot and few-shot classification with SetFit
    • ๐Ÿ—‚ Multi-label text classification with weak supervision
    • ๐Ÿ“ฐ Train a text classifier with weak supervision
    • ๐Ÿ”ซ Evaluate a zero-shot NER with Flair
    • ๐Ÿญ Train a NER model with skweak
    • ๐Ÿ’ซ Explore and analyze spaCy NER predictions
    • ๐Ÿง Find label errors with cleanlab
    • Text Classification Model Comparison
    • ๐Ÿ•ต๏ธโ€โ™€๏ธ Analize predictions with explainability methods
    • ๐Ÿงผ Clean labels using your modelโ€™s loss
    • INSERT TITLE
    • Text classification active learning with classy-classification
    • ๐Ÿค” Text Classification active learning with ModAL
    • ๐Ÿคฏ Few-shot classification with SetFit
    • ๐Ÿค— Train a sentiment classifier with SetFit
    • ๐Ÿ‘‚ Text Classification active learning with small-text
    • ๐Ÿท๏ธ Fine-tune a sentiment classifier with your own data
    • ๐Ÿ•ธ๏ธ 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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๐Ÿ” Using modAL for Active Learning

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

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