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2 changes: 1 addition & 1 deletion .github/workflows/release_notes_check.yaml
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permissions:
contents: read
pull-requests: read
pull-requests: write

jobs:
ci_check:
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281 changes: 259 additions & 22 deletions notebooks/how_to/add_context_to_llm_descriptions.ipynb

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" <img src= \"add-content-block.gif\" alt=\"Screenshot showing insert block button in model documentation\" style=\"border: 2px solid #083E44; border-radius: 8px; border-right-width: 2px; border-bottom-width: 3px;\">\n",
" <br><br>\n",
"\n",
"5. Click **+** and then select **Test-Driven Block**:\n",
"5. Click **+** and then select **Test-Driven Block** under FROM LIBRARY:\n",
"\n",
" - Click on **VM Library** under TEST-DRIVEN in the left sidebar.\n",
" - In the search bar, type in `HighPearsonCorrelation`.\n",
" - Select `HighPearsonCorrelation:balanced_raw_dataset` as the test.\n",
"\n",
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" <img src= \"add-content-block.gif\" alt=\"Screenshot showing insert block button in model documentation\" style=\"border: 2px solid #083E44; border-radius: 8px; border-right-width: 2px; border-bottom-width: 3px;\">\n",
" <br><br>\n",
"\n",
"5. Click **+** and then select **Test-Driven Block**:\n",
"5. Click **+** and then select **Test-Driven Block** under FROM LIBRARY:\n",
"\n",
" - In the search bar, type in `ConfusionMatrix`.\n",
" - Select the custom `ConfusionMatrix` tests you logged above:\n",
" - Click on **Custom** under TEST-DRIVEN in the left sidebar.\n",
" - Select the two custom `ConfusionMatrix` tests you logged above:\n",
"\n",
" <img src= \"selecting-confusion-matrix-test.png\" alt=\"Screenshot showing the ConfusionMatrix tests selected\" style=\"border: 2px solid #083E44; border-radius: 8px; border-right-width: 2px; border-bottom-width: 3px;\">\n",
" <br><br>\n",
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"\n",
"Learn how to use ValidMind for your end-to-end model validation process with our series of four introductory notebooks. In this third notebook, develop a potential challenger model and then pass your model and its predictions to ValidMind.\n",
"\n",
"A *challenger model* is an alternate model that attempt to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle."
"A *challenger model* is an alternate model that attempts to outperform the champion model, ensuring that the best performing fit-for-purpose model is always considered for deployment. Challenger models also help avoid over-reliance on a single model, and allow testing of new features, algorithms, or data sources without disrupting the production lifecycle."
]
},
{
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