diff --git a/docs/experiments-distilbert_kd.md b/docs/experiments-distilbert_kd.md
index 355751d16..2e8d2b9ba 100644
--- a/docs/experiments-distilbert_kd.md
+++ b/docs/experiments-distilbert_kd.md
@@ -1,6 +1,6 @@
 # Pyserini: Reproducing DistilBERT KD Results
 
-This guide provides instructions to reproduce the TCT-ColBERT dense retrieval model on the MS MARCO passage ranking task, described in the following paper:
+This guide provides instructions to reproduce the DistilBERT KD dense retrieval model on the MS MARCO passage ranking task, described in the following paper:
 
 > Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan, and Allan Hanbury. [Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
 .](https://arxiv.org/abs/2010.02666) arXiv:2010.02666, October 2020. 
diff --git a/docs/experiments-sbert.md b/docs/experiments-sbert.md
index 0d1c2753e..0f453fa65 100644
--- a/docs/experiments-sbert.md
+++ b/docs/experiments-sbert.md
@@ -40,7 +40,7 @@ Hybrid retrieval with dense-sparse representations (without document expansion):
 - dense retrieval with SBERT, brute force index.
 - sparse retrieval with BM25 `msmarco-passage` (i.e., default bag-of-words) index.
 
-```bas
+```bash
 $ python -m pyserini.hsearch dense  --index msmarco-passage-sbert-bf \
                                     --encoded-queries sbert-msmarco-passage-dev-subset \
                              sparse --index msmarco-passage \