Change selection order in UI

app.py

@@ -4,20 +4,20 @@ from transformer_ranker import TransformerRanker, prepare_popular_models
 
 st.title("Choose Your Transformer")
 
-# 1) Select multiple models from HuggingFace model hub
-model_options = ['prajjwal1/bert-tiny', 'google/electra-small-discriminator', 'microsoft/deberta-v3-small',
-                 'bert-base-uncased', 'bert-base-cased', 'distilbert-base-uncased', 'roberta-base']  # List of models
-selected_models = st.multiselect("Select Models", model_options, default=model_options[:1])
-
-# 2) Select dataset names (from text classification or token classification subcategory)
+# 1) Select dataset names (from text classification or token classification subcategory)
 dataset_options = ['trec', 'conll2003']  # Example datasets; you can expand this list
 selected_dataset = st.selectbox("Select Dataset", dataset_options)
 
-# 3) Select the parameter for dataset downsampling
+# 2) Select the parameter for dataset downsampling
 downsample_values = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
 downsample_ratio = st.select_slider("Dataset Downsample Ratio", options=downsample_values, value=0.2)
 
-# 4) Select the parameter for "layer selection" with layermean as the default
+# 3) Select multiple models from HuggingFace model hub
+model_options = ['prajjwal1/bert-tiny', 'google/electra-small-discriminator', 'microsoft/deberta-v3-small',
+                 'bert-base-uncased', 'bert-base-cased', 'distilbert-base-uncased', 'roberta-base']  # List of models
+selected_models = st.multiselect("Select Models", model_options, default=model_options[:1])
+
+# 4) Select the parameter for layer selection with layermean as the default
 layer_options = ['lastlayer', 'layermean', 'bestlayer']
 selected_layer = st.selectbox("Layer Selection", layer_options, index=1)