rename factory -> splade

app.py

@@ -58,9 +58,9 @@ def predict_query(input, agg):
 import pyterrier as pt ; pt.init()
 import pyt_splade
 
-factory = pyt_splade.SpladeFactory(agg={repr(agg)})
+splade = pyt_splade.SpladeFactory(agg={repr(agg)})
 
-query_pipeline = factory.query()
+query_pipeline = splade.query()
 
 query_pipeline({df2code(input)})
 '''
@@ -77,9 +77,9 @@ def predict_doc(input, agg):
 import pyterrier as pt ; pt.init()
 import pyt_splade
 
-factory = pyt_splade.SpladeFactory(agg={repr(agg)})
+splade = pyt_splade.SpladeFactory(agg={repr(agg)})
 
-doc_pipeline = factory.indexing()
+doc_pipeline = splade.indexing()
 
 doc_pipeline({df2code(input)})
 '''

wrapup.md

@@ -16,11 +16,11 @@ pt.init(version='snapshot')
 import pyt_splade
 
 dataset = pt.get_dataset('irds:msmarco-passage')
-factory = pyt_splade.SpladeFactory()
+splade = pyt_splade.SpladeFactory()
 
 indexer = pt.IterDictIndexer('./msmarco_psg', pretokenized=True)
 
-indxer_pipe = factory.indexing() >> indexer
+indxer_pipe = splade.indexing() >> indexer
 indxer_pipe.index(dataset.get_corpus_iter())
 ```
 
@@ -36,7 +36,7 @@ and then performs retrieval:
 </div>
 
 ```python
-splade_retr = factory.query() >> pt.BatchRetrieve('./msmarco_psg', wmodel='Tf')
+splade_retr = splade.query() >> pt.BatchRetrieve('./msmarco_psg', wmodel='Tf')
 ```
 
 ### References & Credits