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Getting Started

  • What is Argilla?
  • ๐Ÿš€ Quickstart
    • Installation
    • Workflow
  • ๐ŸŽผ Cheatsheet
  • ๐Ÿ”ง Installation
    • Python
    • Docker
    • Docker Quickstart
    • Docker-compose
    • Cloud Providers and Kubernetes
    • Hugging Face Spaces
    • Google Colab
  • โš™๏ธ Configuration
    • Elasticsearch
    • Server configuration
    • User Management
    • Database Migrations
    • Image Support

LLMs

  • Conceptual Guide
    • Data collection for LLMs
    • Collecting demonstration data
    • Collecting comparison data
    • Feedback Data Model
  • How-to Guide
    • Create a Feedback Dataset
    • Set up your annotation team
    • Annotate a Feedback Dataset
    • Collect responses from an annotated Feedback Dataset
    • Export a Feedback Dataset
    • Fine-tune an LLM
    • Monitoring LangChain apps
  • Examples
    • ๐Ÿ–ผ๏ธ Curate an instruction dataset for supervised fine-tuning
    • ๐Ÿ† Train a reward model for RLHF

Guides

  • ๐Ÿง‘โ€๐Ÿ’ป Manage Data
  • ๐Ÿฆพ Train a Model
  • ๐Ÿ”Ž Query datasets
  • ๐Ÿ“Š Dataset Metrics
  • ๐Ÿ•ต๏ธ Bias and Ethics
  • ๐Ÿ‘‚ Job Scheduling
  • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿซ Active Learning
  • ๐Ÿ‘ฎ Weak Supervision
  • ๐Ÿ”ซ Few-shot and zero-shot
  • ๐Ÿ”ฆ Semantic search

Tutorials

  • What are Tutorials?
  • MLOps Steps
    • ๐Ÿท Labeling
    • 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
    • Unstructured
  • Techniques
    • ๐Ÿผ Basics
    • ๐Ÿ‘จ๐Ÿฝโ€๐Ÿซ Active Learning
    • ๐Ÿ”Ž Explainability and bias
    • ๐Ÿ”ซ Few-shot classification
    • ๐Ÿ”ฆ Semantic search
    • ๐Ÿ‘ฎ Weak Supervision

Reference

  • Python
    • Client
    • Metrics
    • Labeling
    • Training
    • 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
    • ๐Ÿ’จ Label data with semantic search and Sentence Transformers
    • ๐Ÿ“ธ Bulk Labeling Multimodal Data
    • 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—‚๏ธ Assign records to your annotation team
    • 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
    • ๐Ÿฅ‡ Compare Text Classification Models
    • 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
  • Terminology

Community

  • Slack
  • Github
  • Discussion forum
  • Developer documentation
  • Contributor Documentation
  • Migration from Rubrix
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๐Ÿ”ฆ Semantic search#

These tutorials show you how to use semantic search with Argilla.

๐Ÿ“ธ Bulk Labelling Multimodal Data

MLOps Steps: Labelling
NLP Tasks: TextClassification (images)
Libraries: Argilla, sentence-transformers
Techniques: Semantic search

๐Ÿ’จ Speed-up data labelling with Sentence Transformer embeddings

MLOps Steps: Labelling
NLP Tasks: TextClassification
Libraries: Argilla, sentence-transformers
Techniques: Semantic search

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๐Ÿ‘ฎ Weak Supervision
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๐Ÿ”ซ Few-shot classification
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