Added adult example. Tighten up presentation.

app.py

@@ -8,7 +8,7 @@ from matplotlib.ticker import MaxNLocator
 from transformers import pipeline
 
 MODEL_NAMES = ["bert-base-uncased", "roberta-base", "bert-large-uncased", "roberta-large"]
-OWN_MODEL_NAME = 'add-your-own'
+OWN_MODEL_NAME = 'add-a-model'
 
 DECIMAL_PLACES = 1
 EPS = 1e-5  # to avoid /0 errors
@@ -308,7 +308,7 @@ place_example = [
     'PLACE',
     "False",
     1,
-    'She was a child in PLACE.'
+    'She became an adult in PLACE.'
 ]
 
 
@@ -353,13 +353,13 @@ def your_fn():
 demo = gr.Blocks()
 with demo:
     gr.Markdown("# Spurious Correlation Evaluation for Pre-trained LLMs")
-    gr.Markdown("Find learned statistical dependencies between otherwise unconditionally independent variables (for example between `gender` and `time`) due to dataset selection bias, with almost any BERT-like LLM on Hugging Face, below.")
+    gr.Markdown("Find spurious correlations between seemingly independent variables (for example between `gender` and `time`) in almost any BERT-like LLM on Hugging Face, below.")
 
-    gr.Markdown("See why this happens how in our paper, [Selection Bias Induced Spurious Correlations in Large Language Models](https://arxiv.org/pdf/2207.08982.pdf), presented at [ ICML 2022 Workshop on Spurious Correlations, Invariance, and Stability](https://sites.google.com/view/scis-workshop/home).")
+    gr.Markdown("See why this happens how in our paper, [Selection Bias Induced Spurious Correlations in Large Language Models](https://arxiv.org/pdf/2207.08982.pdf), presented at [ICML 2022 Workshop on Spurious Correlations, Invariance, and Stability](https://sites.google.com/view/scis-workshop/home).")
     
        
     gr.Markdown("## Instructions for this Demo")
-    gr.Markdown("1) Click on one of the examples below (where we sweep through a spectrum of `places`, `date` and `subreddit` interest) to pre-populate the input fields.") 
+    gr.Markdown("1) Click on one of the examples below (where we sweep through a spectrum of `places`, `dates` and `subreddits`) to pre-populate the input fields.") 
     gr.Markdown("2) Check out the pre-populated fields as you scroll down to the ['Hit Submit...'] button!")
     gr.Markdown("3) Repeat steps (1) and (2) with more pre-populated inputs or with your own values in the input fields!")
 
@@ -377,17 +377,17 @@ with demo:
         gr.Markdown(
             "<-- x-axis sorted in order of increasing self-identified female participation (see [bburky](http://bburky.com/subredditgenderratios/)): ")
 
-        your_gen = gr.Button('Click for your model example inputs')
+        your_gen = gr.Button('Add-a-model example inputs')
         gr.Markdown("<-- x-axis dates, with your own model loaded! (If first time, try another example, it can take a while to load new model.)")
 
     gr.Markdown("## Input fields")
     gr.Markdown(
-        f"A) Pick a spectrum of comma separated values for text injection and x-axis, described above in the Dose-response Relationship section.")
+        f"A) Pick a spectrum of comma separated values for text injection and x-axis.")
 
     with gr.Row():
         x_axis = gr.Textbox(
-            lines=5,
-            label="A) Pick a spectrum of comma separated values for text injection and x-axis",
+            lines=3,
+            label="A) Comma separated values for text injection and x-axis",
         )
 
 
@@ -398,15 +398,15 @@ with demo:
         model_name = gr.Radio(
             MODEL_NAMES + [OWN_MODEL_NAME],
             type="value",
-            label="B) Pick a BERT-like model.",
+            label="B) BERT-like model.",
         )
         own_model_name = gr.Textbox(
-            label="C) If you selected an 'add-your-own' model, put your models Hugging Face pipeline name here. We think it should work with any model that supports the fill-mask task.",
+            label="C) If you selected an 'add-a-model' model, put any Hugging Face pipeline model name (that supports the fill-mask task) here.",
         )
 
     gr.Markdown("D) Pick if you want to the predictions normalied to these gendered terms only.")
     gr.Markdown("E) Also tell the demo what special token you will use in your input text, that you would like replaced with the spectrum of values you listed above.") 
-    gr.Markdown("And F) the degree of polynomial fit used for high-lighting possible dose response trend.")
+    gr.Markdown("And F) the degree of polynomial fit used for high-lighting potential spurious association.")
 
 
     with gr.Row():
@@ -416,11 +416,11 @@ with demo:
             type="index",
         )
         place_holder = gr.Textbox(
-            label="E) Special token place-holder that used in input text that will be replaced with the above spectrum of values.",
+            label="E) Special token place-holder",
         )
         n_fit = gr.Dropdown(
             list(range(1, 5)),
-            label="F) Degree of polynomial fit for high-lighting possible dose response trend",
+            label="F) Degree of polynomial fit",
             type="value",
         )
 
@@ -429,8 +429,8 @@ with demo:
 
     with gr.Row():
         input_text = gr.Textbox(
-            lines=3,
-            label="G) Input text that includes gendered pronouns and your place-holder token specified above.",
+            lines=2,
+            label="G) Input text with pronouns and place-holder token",
         )
 
     gr.Markdown("## Outputs!")