Create streamlit example

app.py

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+import streamlit as st
+from datasets import load_dataset
+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)
+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
+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
+layer_options = ['lastlayer', 'layermean', 'bestlayer']
+selected_layer = st.selectbox("Layer Selection", layer_options, index=1)
+
+# Add real-time logging in the future
+log_expander = st.expander("Expand to view log")
+log_placeholder = log_expander.empty()  # Placeholder for log updates
+
+# Button to run the ranking process
+if st.button("Run Model Ranking"):
+    with st.spinner("Running the transformer-ranker..."):
+
+        # Step 1: Load the selected dataset
+        dataset = load_dataset(selected_dataset)
+
+        # Step 2: Prepare the selected models
+        language_models = prepare_popular_models('base') if selected_models == [] else selected_models
+
+        # Step 3: Initialize the ranker
+        ranker = TransformerRanker(dataset, dataset_downsample=downsample_ratio)
+
+        # Placeholder for log updates
+        log_placeholder.text("Real-time logging will be added here...")
+
+        # Run the ranker
+        results = ranker.run(language_models, batch_size=64)
+
+        # Display the final results
+        st.write(results)
+
+    st.success("Ranking is Done!")

requirements.txt

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+transformer-ranker
+streamlit