typo

app.py

@@ -27,7 +27,7 @@ we should not assign a specific gender or ethnicity to a synthetic figure genera
 
 In this app, we instead take a 2-step clustering-based approach. First, we generate 680 images for each model by varying mentions of terms that denote gender or ethnicity in the prompts.
 Then, we use a [VQA-based model](https://huggingface.co/Salesforce/blip-vqa-base) to cluster these images at different granularities (12, 24, or 48 clusters).
-Exploring these clusters allows us to examine trends in the models' associations between visual features and textual representation of social features.
+Exploring these clusters allows us to examine trends in the models' associations between visual features and textual representation of social attributes.
 We encourage users to take advantage of this app to explore those trends, for example through the lens of the following questions:
 - Find the cluster that has the most prompts denoting a gender or ethnicity that you identify with. Do you think the generated images look like you?
 - Find two clusters that have a similar distribution of gender terms but different distributions of ethnicity terms. Do you see any meaningful differences in how gender is visually represented?