update wording in description

app.py

@@ -290,15 +290,15 @@ title1 = "Patent-BERT: Context-Dependent Synonym Generator"
 description1 = """<p>
 Try inserting a few sentences from a patent, and pick keywords for the model to analyze. The model will analyze the 
 context of the keywords in the sentences and generate the top three most likely candidates for each word. 
-This can be used for more creative patent drafting or patent searches using the generated search string. This is a model based on 
-<a href= "https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md">Patent BERT</a> created by Google.
+This can be used for more creative patent drafting or patent searches using the generated search string. The base model is 
+<a href= "https://github.com/google/patents-public-data/blob/master/models/BERT%20for%20Patents.md">Patent BERT</a> created and trained by Google.
 
-<strong>Note:</strong> Current pipeline only allows for <strong>three</strong> keyword submission. Stemming is built into the output for 
+<strong>Note:</strong> Current pipeline only allows for <strong>three</strong> keyword submissions. Stemming (e.g., altering -> alter) is built into the output for 
 broader search string. <br/>
 
-WIP features include: (<strong>A</strong>) adjustment options for (i) the number of keywords, (ii) the number of context-dependent synonyms, 
-and (iii) a 'creativity' parameter of the model, (<strong>B</strong>) analysis of where these words appear in the patent (e.g., 
-claim, summary, etc.), and (<strong>C</strong>) a stemming option for input keywords. 
+Beta features (currently work-in-progress) include: (<strong>A</strong>) adjustment options for (i) the number of keywords, (ii) the number of context-dependent synonyms, 
+and (iii) a 'creativity' parameter of the model; (<strong>B</strong>) analysis of where these words appear in the patent (e.g., 
+claim, summary, etc.); and (<strong>C</strong>) a stemming option for input keywords. 
 <br/>
 <p/>"""