TRECCollectionIndexer ( index_path, collection = 'trec', verbose = False, meta =. Type ( IndexingType) – the specific indexing procedure to use. Tokeniser ( TerrierTokeniser) – the stemmer to apply. Stopwords ( TerrierStopwords) – the stopwords list to apply. Stemmer ( TerrierStemmer) – the stemmer to apply. Verbose ( bool) – Provide progess bars if possible. Overwrite ( bool) – If index already present at index_path, True would overwrite it, False throws an Exception. Ignored for IndexingType.MEMORY.īlocks ( bool) – Create indexer with blocks if true, else without blocks.

Index_path ( str) – Directory to store index. IterDictIndexer, DFIndexer, TRECCollectionIndexer and FilesIndsexer. All arguments listed below are available in It hosts common configurationĬonstructor called by all indexer subclasses. This is the super class for all of the Terrier-based indexers exposed by PyTerrier. TerrierIndexer ( index_path, * args, blocks = False, overwrite = False, verbose = False, meta_reverse =, stemmer = TerrierStemmer.porter, stopwords = TerrierStopwords.terrier, tokeniser = TerrierTokeniser.english, type = IndexingType.CLASSIC, ** kwargs ) ¶

Common indexer contrustor arguments for all four indexers are shown below. TerrierIndexer ¶Īll indexer classes extend TerrierIndexer. Further worked examples of indexing are provided in the example indexing notebook. We explain both the indexing types and the indexer classes below, with examples. There are also different types of indexing supported in Terrier that are exposed in PyTerrier.

You can create an index from TREC-formatted files, from a TREC test collection, using TRECCollectionIndexer.įor indexing TXT, PDF, Microsoft Word files, etc files you can use FilesIndexer.įor indexing Pandas Dataframe you can use DFIndexer.įor any abitrary iterable dictionaries, you can use IterDictIndexer.

pyterrier.new - Creating new dataframes.

pyterrier.anserini - Anserini/Lucene Support.

Coolutils mail terrier index problem