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Update app.py

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  1. app.py +5 -2
app.py CHANGED
@@ -813,8 +813,11 @@ When the equivalent ratios are easy to compute (integral values), ER is much mor
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  <ol style="color: white;">
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  <li style="color: white;">Select a fine-tuned model:
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  <ul style="color: white;">
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- <li style="color: white;">ASTRA-FT-HGR: Fine-tuned with a small sample of data from schools that have a high graduation rate.</li>
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- <li style="color: white;">ASTRA-FT-Full: Fine-tuned with a small sample of data from a mix of schools that have high/low graduation rates.</li>
 
 
 
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  </ul>
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  </li>
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  <li style="color: white;">Select a percentage of schools to analyze (selecting a large percentage may take a long time). Note that the selected percentage is applied to both High Graduation Rate (HGR) schools and Low Graduation Rate (LGR schools).
 
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  <ol style="color: white;">
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  <li style="color: white;">Select a fine-tuned model:
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+ <li style="color: white;">ASTRA-FT-HGR: Fine-tuned with 10% data from schools that have a high graduation rate.</li>
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+ <li style="color: white;">ASTRA-FT-Skills: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills encoded using Bayesian Knowledge Tracing</li>
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+ <li style="color: white;">ASTRA-FT-Temporal: Fine-tuned with 10% of initial problems from both HGR and LGR schools + temporal features measuring student engagement with MATHia</li>
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+ <li style="color: white;">ASTRA-FT-Unified: Fine-tuned with 10% of initial problems from both HGR and LGR schools + Prior Skills + temporal features</li>
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  </ul>
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  </li>
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  <li style="color: white;">Select a percentage of schools to analyze (selecting a large percentage may take a long time). Note that the selected percentage is applied to both High Graduation Rate (HGR) schools and Low Graduation Rate (LGR schools).