Making Text classification template similar to Image Classification (#92)

* Fix template sidebar

* Code style

Author: trsvchn

templates/text_classification/_sidebar.py

@@ -18,63 +18,66 @@ def get_configs() -> dict:
     config["eval_epoch_length"] = None
     default_none_options(config)
 
-    st.header("Transformer")
+    with st.beta_expander("Text Classification Template Configurations", expanded=True):
+        st.info("Names in the parenthesis are variable names used in the generated code.")
 
-    st.subheader("Model Options")
-    config["model"] = st.selectbox(
-        "Model name (from transformers) to setup model, tokenize and config to train (model)",
-        options=["bert-base-uncased"],
-    )
-    config["model_dir"] = st.text_input("Cache directory to download the pretrained model (model_dir)", value="./")
-    config["tokenizer_dir"] = st.text_input("Tokenizer cache directory (tokenizer_dir)", value="./tokenizer")
-    config["num_classes"] = st.number_input(
-        "Number of target classes. Default, 1 (binary classification) (num_classes)", min_value=0, value=1
-    )
-    config["max_length"] = st.number_input(
-        "Maximum number of tokens for the inputs to the transformer model (max_length)", min_value=1, value=256
-    )
-    config["dropout"] = st.number_input(
-        "Dropout probability (dropout)", min_value=0.0, max_value=1.0, value=0.3, format="%f"
-    )
-    config["n_fc"] = st.number_input(
-        "Number of neurons in the last fully connected layer (n_fc)", min_value=1, value=768
-    )
-    st.markdown("---")
+        st.subheader("Model Options")
+        config["model"] = st.selectbox(
+            "Model name (from transformers) to setup model, tokenize and config to train (model)",
+            options=["bert-base-uncased"],
+        )
+        config["model_dir"] = st.text_input("Cache directory to download the pretrained model (model_dir)", value="./")
+        config["tokenizer_dir"] = st.text_input("Tokenizer cache directory (tokenizer_dir)", value="./tokenizer")
+        config["num_classes"] = st.number_input(
+            "Number of target classes. Default, 1 (binary classification) (num_classes)", min_value=0, value=1
+        )
+        config["max_length"] = st.number_input(
+            "Maximum number of tokens for the inputs to the transformer model (max_length)", min_value=1, value=256
+        )
+        config["dropout"] = st.number_input(
+            "Dropout probability (dropout)", min_value=0.0, max_value=1.0, value=0.3, format="%f"
+        )
+        config["n_fc"] = st.number_input(
+            "Number of neurons in the last fully connected layer (n_fc)", min_value=1, value=768
+        )
+        st.markdown("---")
 
-    st.subheader("Dataset Options")
-    config["data_dir"] = st.text_input("Dataset cache directory (data_dir)", value="./")
-    st.markdown("---")
+        st.subheader("Dataset Options")
+        config["data_dir"] = st.text_input("Dataset cache directory (data_dir)", value="./")
+        st.markdown("---")
 
-    st.subheader("DataLoader Options")
-    config["batch_size"] = st.number_input("Total batch size (batch_size)", min_value=1, value=4)
-    config["num_workers"] = st.number_input("Number of workers in the data loader (num_workers)", min_value=1, value=2)
-    st.markdown("---")
+        st.subheader("DataLoader Options")
+        config["batch_size"] = st.number_input("Total batch size (batch_size)", min_value=1, value=4)
+        config["num_workers"] = st.number_input(
+            "Number of workers in the data loader (num_workers)", min_value=1, value=2
+        )
+        st.markdown("---")
 
-    st.subheader("Optimizer Options")
-    config["learning_rate"] = st.number_input(
-        "Peak of piecewise linear learning rate scheduler", min_value=0.0, value=5e-5, format="%e"
-    )
-    config["weight_decay"] = st.number_input("Weight decay", min_value=0.0, value=0.01, format="%f")
-    st.markdown("---")
+        st.subheader("Optimizer Options")
+        config["learning_rate"] = st.number_input(
+            "Peak of piecewise linear learning rate scheduler", min_value=0.0, value=5e-5, format="%e"
+        )
+        config["weight_decay"] = st.number_input("Weight decay", min_value=0.0, value=0.01, format="%f")
+        st.markdown("---")
 
-    st.subheader("Training Options")
-    config["max_epochs"] = st.number_input("Number of epochs to train the model", min_value=1, value=3)
-    config["num_warmup_epochs"] = st.number_input(
-        "Number of warm-up epochs before learning rate decay", min_value=0, value=0
-    )
-    config["validate_every"] = st.number_input(
-        "Run model's validation every validate_every epochs", min_value=0, value=1
-    )
-    config["checkpoint_every"] = st.number_input(
-        "Store training checkpoint every checkpoint_every iterations", min_value=0, value=1000
-    )
-    config["log_every_iters"] = st.number_input(
-        "Argument to log batch loss every log_every_iters iterations. 0 to disable it", min_value=0, value=15
-    )
-    st.markdown("---")
+        st.subheader("Training Options")
+        config["max_epochs"] = st.number_input("Number of epochs to train the model", min_value=1, value=3)
+        config["num_warmup_epochs"] = st.number_input(
+            "Number of warm-up epochs before learning rate decay", min_value=0, value=0
+        )
+        config["validate_every"] = st.number_input(
+            "Run model's validation every validate_every epochs", min_value=0, value=1
+        )
+        config["checkpoint_every"] = st.number_input(
+            "Store training checkpoint every checkpoint_every iterations", min_value=0, value=1000
+        )
+        config["log_every_iters"] = st.number_input(
+            "Argument to log batch loss every log_every_iters iterations. 0 to disable it", min_value=0, value=15
+        )
+        st.markdown("---")
 
-    distributed_options(config)
-    ignite_handlers_options(config)
-    ignite_loggers_options(config)
+        distributed_options(config)
+        ignite_handlers_options(config)
+        ignite_loggers_options(config)
 
     return config