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PyTerrier
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PyTerrier

Guides

  • Installation
  • Importing Datasets
  • Terrier
    • Quick Start
    • Indexing
    • Retrieval and Re-Ranking
    • Query Expansion
    • How-To Guides
    • API Reference
  • Running Experiments
  • Learning to Rank
  • Artifacts
    • How-To Guides
    • Listing
    • API Reference

Explanations & Illustrations

  • PyTerrier Data Model
  • PyTerrier Transformers
  • Operators on Transformers
  • Examples of Retrieval Pipelines
  • Working with Document Texts
  • Neural Rankers and Rerankers
  • Tuning Transformer Pipelines
  • Experiments on TREC Robust 2004
  • Extending PyTerrier
    • Making a PyTerrier Extension Package
    • Writing Custom Transformers
    • Writing Custom Artifacts
    • Extending PyTerrier with New Datasets
    • Building in PyTerrier Support for Indexing and Retrieval Backends
    • Input Validation
  • Troubleshooting
    • Troubleshooting Installation
    • Troubleshooting Java
    • Troubleshooting Inspection and Verification

Other Modules

  • pt.io - Reading/writing files
  • pt.apply - Custom Transformers
  • pt.new - Creating new dataframes
  • pt.debug - Tools for Debugging
  • pt.inspect - Inspecting Live Objects
  • pt.schematic - Visualizing Pipelines

Extensions

  • Adaptive Retrieval
  • Alpha Features
    • Dataframe Builder
    • Rank Biased Overlap (RBO)
    • Inspection
    • Result Fusion
    • Parallelisation
    • Transform Function Decorators
    • Other Alpha Utils
    • Input Validation
  • Anserini
    • Extras
    • How-To Guides
    • API Documentation
  • API Services
    • DBLP
    • Google API
    • Pinecone
    • Semantic Scholar
  • AutoQrels
  • BMP
    • API Reference
  • Caching
    • Caching Scorer / Re-Ranker Results
    • Caching Indexing Pipeline Results
    • Caching Retriever Results
    • Extra Caching Utilities
  • ChatNoir
  • CIFF
  • ColBERT
  • DeepCT
  • DeepImpact
  • Dense Retrieval
    • Overview
    • Encoding
    • Indexing & Retrieval
    • Pseudo-Relevance Feedback
    • Diversity
    • How-To Guides
    • API Reference
  • Doc2Query
  • Document Quality
  • GenRank
  • OpenNIR
  • PISA
  • RAG
    • RAG Datamodel
    • RAG Measures
    • LLM Backends
    • Prompt Construction
    • Readers
  • Sentence Transformers
  • SPLADE
  • SuiteEval
    • Suites
    • API Reference
  • T5

Indices and tables

  • How-To Guides
  • Bibliography
  • Index
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Extending PyTerrierΒΆ

Note

This section of the documentation is under development.

PyTerrier is designed to be extensible. This section describes how to extend PyTerrier with new features, including:

  • Making a PyTerrier Extension Package
  • Writing Custom Transformers
  • Writing Custom Artifacts
  • Extending PyTerrier with New Datasets
  • Building in PyTerrier Support for Indexing and Retrieval Backends
  • Input Validation
Next
Making a PyTerrier Extension Package
Previous
Experiments on TREC Robust 2004
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