reordering markdown for readability

.gitignore

@@ -0,0 +1 @@
+venv_sce/*

app.py

@@ -318,7 +318,7 @@ subreddit_example = [
     ', '.join(SUBREDDITS),
     'SUBREDDIT',
     "False",
-    1,
+    3,
     'I saw in r/SUBREDDIT that she is a hacker.'
 ]
 
@@ -354,44 +354,30 @@ demo = gr.Blocks()
 with demo:
     gr.Markdown("## Spurious Correlation Evaluation for our LLMs")
     gr.Markdown("Although genders are relatively evenly distributed across time, place and interests, there are also known gender disparities in terms of access to resources. Here we demonstrate that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.")
-    gr.Markdown("These spurious associations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.")
-    gr.Markdown("Selection of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.")
-
-    gr.Markdown("### Data Generating Process")
-    gr.Markdown("To pick values below that are most likely to cause spurious correlations, it helps to make some assumptions about the training datasets' likely data generating process, and where selection bias may come in.")
-
-    gr.Markdown("A plausible data generating processes for both Wikipedia and Reddit sourced data is shown as a DAG below. These DAGs are prone to collider bias when conditioning on `access`. In other words, although in real life `place`, `date`, (subreddit) `interest` and gender are all unconditionally independent, when we condition on their common effect, `access`, they become unconditionally dependent. Composing a dataset often requires the dataset maintainers to condition on `access`. Thus LLMs learn these dataset induced dependencies, appearing to us as spurious correlations.")
-    gr.Markdown("""
-    <center>
-    <img src="https://www.dropbox.com/s/f0numpllywdd271/combo_dag_block_party.png?raw=1" 
-        alt="DAG of possible data generating process for datasets used in training some of our LLMs.">
-    </center>
-    """)
-    
-    gr.Markdown("There may be misassumptions in our DAG above, which you can explore below.")
-    gr.Markdown("Or you may be interested in applying this demo to your own model of interest. This demo _should_ work with any Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task.")
-
+   
     gr.Markdown("### Dose-response Relationship")
     gr.Markdown("One intuitive way to see the impact that changing one variable may have upon another is to look for a dose-response relationship, in which a larger intervention in the treatment (the value in text form injected in the otherwise unchanged text sample) produces a larger response in the output (the softmax probability of a gendered pronoun).")
+    gr.Markdown("This dose-response plot requires a range of values along which we may see a spectrum of gender representation (or misrepresentation) in our datasets.")
 
     gr.Markdown("### This Demo")
-    gr.Markdown("This type of plot requires a range of values along which we may see a spectrum of gender representation (or misrepresentation) in our datasets.")
-    gr.Markdown("Click on one of the examples below (where we sweep through a spectrum of `places`, `date` and `subreddit` interest) to get an idea of whats intended here. Then try your own!")
+    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("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!")
 
     with gr.Row():
         gr.Markdown("X-axis sorted by older to more recent dates:")
-        place_gen = gr.Button('Country example')
+        place_gen = gr.Button('Click for country example inputs')
 
         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 by World Economic Forum in 2021:")
-        date_gen = gr.Button('Date example')
+        date_gen = gr.Button('Click for date example inputs')
 
         gr.Markdown(
             "X-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ")
-        subreddit_gen = gr.Button('Subreddit example')
+        subreddit_gen = gr.Button('Click for Subreddit example inputs')
 
         gr.Markdown("Date example with your own model loaded! (We recommend you try after seeing how others work. It can take a while to load new model.)")
-        your_gen = gr.Button('Your model example')
+        your_gen = gr.Button('Click for your model example inputs')
 
     with gr.Row():
         x_axis = gr.Textbox(
@@ -444,7 +430,9 @@ with demo:
         )
 
     gr.Markdown("### Outputs!")
-    gr.Markdown("Scroll down and 'Hit Submit'!")
+    #gr.Markdown("Scroll down and 'Hit Submit'!")
+    with gr.Row():
+        btn = gr.Button("Hit submit to generate predictions!")
 
     with gr.Row():
         sample_text = gr.Textbox(
@@ -470,12 +458,32 @@ with demo:
         your_gen.click(your_fn, inputs=[], outputs=[
             model_name, own_model_name, x_axis, place_holder, to_normalize,  n_fit, input_text])
 
-    with gr.Row():
-        btn = gr.Button("Hit submit")
-        btn.click(
-            predict_gender_pronouns,
-            inputs=[model_name, own_model_name, x_axis, place_holder,
-                    to_normalize, n_fit, input_text],
-            outputs=[sample_text, female_fig, male_fig, df])
-
-demo.launch(debug=True)
+    btn.click(
+        predict_gender_pronouns,
+        inputs=[model_name, own_model_name, x_axis, place_holder,
+                to_normalize, n_fit, input_text],
+        outputs=[sample_text, female_fig, male_fig, df])
+
+
+    gr.Markdown("### What is Causing these Spurious Correlations?")
+
+    gr.Markdown("Spurious correlations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.")
+    gr.Markdown("Selection of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.")
+
+    gr.Markdown("### Data Generating Process")
+    gr.Markdown("To pick values below that are most likely to cause spurious correlations, it helps to make some assumptions about the training datasets' likely data generating process, and where selection bias may come in.")
+
+    gr.Markdown("A plausible data generating processes for both Wikipedia and Reddit sourced data is shown as a DAG below. These DAGs are prone to collider bias when conditioning on `access`. In other words, although in real life `place`, `date`, (subreddit) `interest` and gender are all unconditionally independent, when we condition on their common effect, `access`, they become unconditionally dependent. Composing a dataset often requires the dataset maintainers to condition on `access`. Thus LLMs learn these dataset induced dependencies, appearing to us as spurious correlations.")
+    gr.Markdown("""
+    <center>
+    <img src="https://www.dropbox.com/s/f0numpllywdd271/combo_dag_block_party.png?raw=1" 
+        alt="DAG of possible data generating process for datasets used in training some of our LLMs.">
+    </center>
+    """)
+
+    gr.Markdown("There may be misassumptions in our DAG above, which you can explore below.")
+    gr.Markdown("Or you may be interested in applying this demo to your own model of interest. This demo _should_ work with any Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task.")
+
+demo.launch(debug=True)
+
+# %%