Typo and document fixes

app.py

@@ -359,11 +359,12 @@ with demo:
     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 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("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!")
 
+    gr.Markdown("### Example inputs")
     with gr.Row():
         gr.Markdown("X-axis sorted by older to more recent dates:")
         place_gen = gr.Button('Click for country example inputs')
@@ -379,54 +380,58 @@ with demo:
         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('Click for your model example inputs')
 
+    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.")
+
     with gr.Row():
         x_axis = gr.Textbox(
             lines=5,
-            label="Pick a spectrum of comma separated values for text injection and x-axis",
+            label="A) Pick a spectrum of comma separated values for text injection and x-axis",
         )
 
 
     gr.Markdown(
-        "Pick a pre-loaded BERT-family model of interest, or add another Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task (this may take some time to load).")
+        f"B) Pick a pre-loaded BERT-family model of interest on the right. C) Or select `{OWN_MODEL_NAME}`, then add the mame of any other Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task on the right (note: this may take some time to load).")
 
     with gr.Row():
         model_name = gr.Radio(
             MODEL_NAMES + [OWN_MODEL_NAME],
             type="value",
-            label="Model: Pick a BERT-like model.",
+            label="B) Pick a BERT-like model.",
         )
         own_model_name = gr.Textbox(
-            label="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-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.",
         )
 
     gr.Markdown(
         "We are able to test the pre-trained LLMs without any modification to the models, as the gender-pronoun prediction task is simply a special case of the masked language modeling (MLM) task, with which all these models were pre-trained. Rather than random masking, the gender-pronoun prediction task masks only non-gender-neutral terms (listed in prior [Space](https://huggingface.co/spaces/emilylearning/causing_gender_pronouns_two)).")
-    gr.Markdown("For the pre-trained LLMs the final prediction is a softmax over the entire tokenizer's vocabulary, from which we sum up the portion of the probability mass from the top five prediction words that are gendered terms. Pick if you want to the predictions normalied to these gendered terms only.")
-    gr.Markdown("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, and the degree of polynomial fit used for high-lighting possible dose response trend ")
+    gr.Markdown("For the pre-trained LLMs the final prediction is a softmax over the entire tokenizer's vocabulary, from which we sum up the portion of the probability mass from the top five prediction words that are gendered terms. 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, and F) the degree of polynomial fit used for high-lighting possible dose response trend ")
 
 
     with gr.Row():
         to_normalize = gr.Dropdown(
             ["False", "True"],
-            label="Normalize model's predictions to only the gendered ones?",
+            label="D) Normalize model's predictions to only the gendered ones?",
             type="index",
         )
         place_holder = gr.Textbox(
-            label="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 that used in input text that will be replaced with the above spectrum of values.",
         )
         n_fit = gr.Dropdown(
             list(range(1, 5)),
-            label="Degree of polynomial fit for high-lighting possible dose response trend",
+            label="F) Degree of polynomial fit for high-lighting possible dose response trend",
             type="value",
         )
 
     gr.Markdown(
-        "Finally, add input text that includes at least one gendered pronouns and one place-holder token specified above.")
+        "G) Finally, add input text that includes at least one gendered pronouns and one place-holder token specified above.")
 
     with gr.Row():
         input_text = gr.Textbox(
             lines=3,
-            label="Input Text: Sentence that includes gendered pronouns and your place-holder token specified above.",
+            label="G) Input text that includes gendered pronouns and your place-holder token specified above.",
         )
 
     gr.Markdown("### Outputs!")