Topic: terrier Goto Github
Some thing interesting about terrier
Some thing interesting about terrier
terrier,A simple docker setup for the Terrier IR Platform.
User: christophschmidl
terrier,DSL front-end for groove query analysis pipeline
User: hscells
Home Page: https://github.com/hscells/groove
terrier,Saas tool for developing and testing solutions for TREC PM. Includes built in trec_eval, Elasticsearch and Terrier
User: jakubriegel
terrier,Pipeline to provide MatchZoo with Terrier data
User: joseph94m
terrier,🍺 An intuitive beer search engine leveraging PyTerrier and FastAPI, with a user-friendly web interface powered by NextJS and React.js
User: lucadibello
terrier,An app that helps medical staff to search for medical articles
User: majedbannouri
terrier,Improving Arabic Light Stemming in Information Retrieval Systems
User: mhmdio
terrier,Cranfield collection in TREC XML format.
User: oussbenk
terrier,📚 Information Retrieval Tutorial using Terrier with Cranfield collection
User: oussbenk
terrier,A vanilla Vector Space Model for Terrier 3.6, did during the Information Retrieval MS Course. It comprehend a module for a pre-calculus of the L2 Norm of the document collection
User: roberto1987
terrier,COMPGI15 assignment to evaluate retrieval models on a standard TREC document test collection
User: santteegt
terrier,Create PyTerrier compatible dense indices using any sentence_transformers model
User: soldni
terrier,Extension for the Terrier search engine to import term statistics
User: soldni
terrier,Minimal server for terrier search.
User: soldni
terrier,Batch text search and filtering pipeline that takes in a large set of text documents and a set of user defined queries, then for each query, text documents are ranked by relevance for that query. The top 10 documents for each query are returned as output. Each document and query is processed to remove stopwords and stemming is applied. Documents are scored using the DPH ranking model.
User: srishti-bhat
terrier,Batch text search and filtering pipeline that takes in a large set of text documents and a set of user defined queries, then for each query, text documents are ranked by relevance for that query. The top 10 documents for each query are returned as output. Each document and query is processed to remove stopwords and stemming is applied. Documents are scored using the DPH ranking model.
User: srishti-bhat
terrier,Terrier IR Platform
Organization: terrier-org
Home Page: http://terrier.org/
terrier,Elias-Fano compression for Terrier 5
User: tonellotto
terrier,An efficient layer to perform query processing on top of Terrier
User: tonellotto
terrier,↕️ Intuitive axiomatic retrieval experimentation.
Organization: webis-de
Home Page: https://pypi.org/project/ir_axioms/
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