fig narrower, clean up text, rem overlapping plot label

app.py

@@ -178,7 +178,7 @@ def get_figure(df, gender, n_fit=1):
     ys = df[cols[0]]
     fig, ax = plt.subplots()
     # Trying small fig due to rendering issues on HF, not on VS Code
-    fig.set_figheight(4)
+    fig.set_figheight(3)
     fig.set_figwidth(8) 
 
     # find stackoverflow reference
@@ -217,7 +217,7 @@ def predict_gender_pronouns(
     normalizing,
     input_text,
 ):
-    """Run inference on input_text for each model type, returning df and plots of precentage
+    """Run inference on input_text for each model type, returning df and plots of percentage
     of gender pronouns predicted as female and male in each target text.
     """
     model = models[model_type]
@@ -323,15 +323,12 @@ def reddit_fn():
 demo = gr.Blocks()
 with demo:
     gr.Markdown("## Hunt for spurious correlations in 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. We suggest that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.")
+    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 bias 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("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("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). Specifically, below are examples of sweeping through a spectrum of place, date and subreddit interest (we encourage you to try your own).")
-    
-    gr.Markdown("This requires a spectrum of less to more gender-equal values for each covariate. For date, it’s easy to just use time itself, as gender equality has generally improved with time, so we picked years ranging from 1800 - 1999. For place we used the bottom and top 10 Global Gender Gap ranked countries. And for subreddit, we use subreddit name ordered by subreddits that have an increasingly larger percentage of self-reported female commenters.")
-    #gr.Markdown("Please see a better explanation in another [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two).")
+    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). Specifically, below are examples of sweeping through a spectrum of place, date and subreddit interest. We encourage you to try your own!")
     
     with gr.Row():
         x_axis = gr.Textbox(
@@ -362,10 +359,8 @@ with demo:
         sample_text = gr.Textbox(
             type="auto", label="Output text: Sample of text fed to model")
     with gr.Row():
-        female_fig = gr.Plot(
-            type="auto", label="Plot of softmax probability pronouns predicted female.")
-        male_fig = gr.Plot(
-            type="auto", label="Plot of softmax probability pronouns predicted male.")
+        female_fig = gr.Plot(type="auto")
+        male_fig = gr.Plot(type="auto")
     with gr.Row():
         df = gr.Dataframe(
             show_label=True,