fix: fix typo in description

src/streamlit_app.py

@@ -138,17 +138,16 @@ and functional-regulatory prediction, which includes diverse experimental tracks
  and translation (Ribo-seq).
 
 Data are drawn from a phylogenetically diverse set of species, including organisms seen during post-training 
-(human, chicken, Arabidopsis, rice, maize) and entirely unseen species (cattle, tomato), with careful curation 
+(human, chicken, arabidopsis, rice, maize) and entirely unseen species (cattle, tomato), with careful curation 
 to avoid data leakage. This design allows the dataset to probe long-range sequence-to-function mapping, 
 cross-species generalization, and transfer across heterogeneous regulatory modalities, 
 including assays not present in prior multispecies training corpora. By standardizing sequence length, 
-resolution, and evaluation metrics across all tracks, \brandbenchmark provides a controlled dataset 
+resolution, and evaluation metrics across all tracks, the NTv3 Benchmark provides a controlled dataset 
 for comparing representation quality across genomic foundation models.
 
 The metrics used are:
 - **Pearson correlations (multi-assay)**: per-dataset scores across species and models for functional tracks.
 - **MCC (bed tracks)**: per-track MCC values across species and models for gene annotation tracks.
-
 """
 
 HERE = os.path.dirname(os.path.abspath(__file__))  # /app/src