John Graham Reynolds commited on
Commit
6c9f31e
·
1 Parent(s): bbcb349

formatting

Browse files
Files changed (3) hide show
  1. .streamlit/config.toml +4 -4
  2. app.py +2 -2
  3. style.css +10 -1
.streamlit/config.toml CHANGED
@@ -1,6 +1,6 @@
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  [theme]
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- primaryColor="#D8D4F9"
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- backgroundColor="#AAD3F1"
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- secondaryBackgroundColor="#D8D4F9"
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- textColor="#1B3139"
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  font="sans serif"
 
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  [theme]
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+ primaryColor="#FC5D21"
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+ backgroundColor="#1A4F75"
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+ secondaryBackgroundColor="#FC5D21"
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+ textColor="#FFFFFF"
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  font="sans serif"
app.py CHANGED
@@ -20,9 +20,9 @@ EXAMPLE_PROMPTS = [
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  TITLE = "CyberSolve LinAlg 1.2"
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  DESCRIPTION= """Welcome to the CyberSolve LinAlg 1.2 demo! \n
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- **Overview and Usage**: This 🤗 Space is designed to demo the abilities of the **CyberSolve LinAlg 1.2** text-to-text language model. Specifically, the CyberSolve LinAlg 1.* family of models
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  are downstream versions of the 783M parameter FLAN-T5 text-to-text transformer, fine-tuned on the Google DeepMind Mathematics dataset for the purpose of solving linear equations of a single variable.
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- To effectively query the model for its intended task, prompt the model solve an arbitrary linear equation of a single variable with a query of the form: "Solve 24 = 1601*c - 1605*c for c."; the model
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  will return its prediciton in a simple format. The algebraic capabailites far exceed those of the base FLAN-T5 model. CyberSolve LinAlg 1.2 achieves a 90.7 percent exact match benchmark on the DeepMind Mathematics
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  evaluation dataset of 10,000 unique linear equations; the FLAN-T5 base model scores 9.6 percent. On the left is a sidebar of **Examples** that can be clicked to query to model.
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  TITLE = "CyberSolve LinAlg 1.2"
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  DESCRIPTION= """Welcome to the CyberSolve LinAlg 1.2 demo! \n
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+ **Overview and Usage**: This 🤗 Space is designed to demo the abilities of the **CyberSolve LinAlg 1.2** text-to-text language model. Specifically, the **CyberSolve LinAlg 1.x** family of models
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  are downstream versions of the 783M parameter FLAN-T5 text-to-text transformer, fine-tuned on the Google DeepMind Mathematics dataset for the purpose of solving linear equations of a single variable.
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+ To effectively query the model for its intended task, prompt the model solve an arbitrary linear equation of a single variable with a query of the form: *"Solve 24 = 1601c - 1605c for c."*; the model
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  will return its prediciton in a simple format. The algebraic capabailites far exceed those of the base FLAN-T5 model. CyberSolve LinAlg 1.2 achieves a 90.7 percent exact match benchmark on the DeepMind Mathematics
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  evaluation dataset of 10,000 unique linear equations; the FLAN-T5 base model scores 9.6 percent. On the left is a sidebar of **Examples** that can be clicked to query to model.
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style.css CHANGED
@@ -1,3 +1,12 @@
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- .st-emotion-cache-1tpusnk a{
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  color: #27497c;
 
 
 
 
 
 
 
 
 
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  }
 
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+ /* .st-emotion-cache-1tpusnk a{
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  color: #27497c;
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+ } */
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+
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+ @import url('https://fonts.googleapis.com/css2?family=Orbitron:[email protected]&display=swap');
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+
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+ html, body, [class*="css"] {
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+ font-family: 'Orbitron', sans-serif;
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+ font-size: 18px;
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+ font-weight: 600;
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+ color: #FFFFFF;
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  }