Update models, examples, explanatory text.

app.py

@@ -7,8 +7,7 @@ import random
 from matplotlib.ticker import MaxNLocator
 from transformers import pipeline
 
-MODEL_NAMES = ["bert-base-uncased",
-               "distilbert-base-uncased", "xlm-roberta-base", "roberta-base"]
+MODEL_NAMES = ["bert-base-uncased", "roberta-base", "bert-large-uncased", "roberta-large"]
 OWN_MODEL_NAME = 'add-your-own'
 
 DECIMAL_PLACES = 1
@@ -290,45 +289,47 @@ description = """
 
 """
 
-place_example = [
-    MODEL_NAMES[0],
+
+date_example = [
+    MODEL_NAMES[1],
     '',  
-    ', '.join(PLACES),
-    'PLACE',
+    ', '.join(DATES),
+    'DATE',
     "False",
     1,
-    'She is in PLACE.'
+    'She was a teenager in DATE.'
 ]
 
-date_example = [
+
+place_example = [
     MODEL_NAMES[0],
     '',  
-    ', '.join(DATES),
-    'DATE',
+    ', '.join(PLACES),
+    'PLACE',
     "False",
-    3,
-    'She will be a teenager in DATE.'
+    1,
+    'She was a child in PLACE.'
 ]
 
 
 subreddit_example = [
-    MODEL_NAMES[2],
+    MODEL_NAMES[3],
     '',  
     ', '.join(SUBREDDITS),
     'SUBREDDIT',
     "False",
     1,
-    'She was an adult. SUBREDDIT.'
+    'She was a kid. SUBREDDIT.'
 ]
 
 own_model_example = [
     OWN_MODEL_NAME,
-    'lordtt13/COVID-SciBERT',
+    'emilyalsentzer/Bio_ClinicalBERT',
     ', '.join(DATES),
     'DATE',
     "False",
-    3,
-    'She got a viral infection in DATE.'
+    1,
+    'She was exposed to the virus in DATE.'
 ]
 
 
@@ -354,7 +355,7 @@ 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("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")
@@ -365,19 +366,19 @@ with demo:
     gr.Markdown("## Example inputs")
     gr.Markdown("Click a button below to pre-populate input fields with example values. Then scroll down to Hit Submit to generate predictions.")
     with gr.Row():
-        gr.Markdown("X-axis sorted by older to more recent dates:")
         date_gen = gr.Button('Click for date example inputs')
+        gr.Markdown("<-- x-axis sorted by older to more recent dates:")
 
-        gr.Markdown(
-            "X-axis sorted by bottom 10 and top 10 [Global Gender Gap](https://www3.weforum.org/docs/WEF_GGGR_2021.pdf) ranked countries:")
         place_gen = gr.Button('Click for country example inputs')
-
         gr.Markdown(
-            "X-axis sorted in order of increasing self-identified female participation (see [bburky](http://bburky.com/subredditgenderratios/)): ")
+            "<-- x-axis sorted by bottom 10 and top 10 [Global Gender Gap](https://www3.weforum.org/docs/WEF_GGGR_2021.pdf) ranked countries:")
+
         subreddit_gen = gr.Button('Click for Subreddit example inputs')
+        gr.Markdown(
+            "<-- x-axis sorted in order of increasing self-identified female participation (see [bburky](http://bburky.com/subredditgenderratios/)): ")
 
-        gr.Markdown("Date example with your own model loaded! (If first time, try another example, it can take a while to load new model.)")
         your_gen = gr.Button('Click for your 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(