fix variable name typos

README.md

@@ -36,7 +36,7 @@ See the [indexing documentation](https://pyterrier.readthedocs.io/en/latest/terr
 topics = pt.io.read_topics(topicsFile)
 qrels = pt.io.read_qrels(qrelsFile)
 BM25_r = pt.terrier.Retriever(index, wmodel="BM25")
-res = BM25_br.transform(topics)
+res = BM25_r.transform(topics)
 pt.Evaluate(res, qrels, metrics = ['map'])
 ```
 
@@ -56,7 +56,7 @@ There is a worked example in the [experiment notebook](examples/notebooks/experi
 PyTerrier makes it easy to develop complex retrieval pipelines using Python operators such as `>>` to chain different retrieval components. Each retrieval approach is a [transformer](https://pyterrier.readthedocs.io/en/latest/transformer.html), having one key method, `transform()`, which takes a single Pandas dataframe as input, and returns another dataframe. Two examples might encapsulate applying the sequential dependence model, or a query expansion process:
 ```python
 sdm_bm25 = pt.rewrite.SDM() >> pt.terrier.Retriever(indexref, wmodel="BM25")
-bo1_qe = BM25_br >> pt.rewrite.Bo1QueryExpansion() >> BM25_br
+bo1_qe = BM25_r >> pt.rewrite.Bo1QueryExpansion() >> BM25_r
 ```
 
 There is documentation on [transformer operators](https://pyterrier.readthedocs.io/en/latest/operators.html) as well as [example pipelines](https://pyterrier.readthedocs.io/en/latest/pipeline_examples.html) show other common use cases. For more information, see the [PyTerrier data model](https://pyterrier.readthedocs.io/en/latest/datamodel.html).
@@ -81,7 +81,7 @@ You can see examples of how to use these, including notebooks that run on Google
 
 Complex learning to rank pipelines, including for learning-to-rank, can be constructed using PyTerrier's operator language. For example, to combine two features and make them available for learning, we can use the `**` operator.
 ```python
-two_features = BM25_br >> ( 
+two_features = BM25_r >> ( 
   pt.terrier.Retriever(indexref, wmodel="DirichletLM") ** 
   pt.terrier.Retriever(indexref, wmodel="PL2") 
 )
@@ -96,7 +96,7 @@ PyTerrier allows simple access to standard information retrieval test collection
 ```python
 topics = pt.get_dataset("trec-robust-2004").get_topics()
 qrels = pt.get_dataset("trec-robust-2004").get_qrels()
-pt.Experiment([BM25_br, PL2_br], topics, qrels, eval_metrics)
+pt.Experiment([BM25_r, PL2_r], topics, qrels, eval_metrics)
 ```
 
 You can index datasets that include a corpus using IterDictIndexer and get_corpus_iter: