feat(docs): hide toc on getting started

walledai · Aug 2, 2024 · 529c037 · 529c037
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diff --git a/docs/quickstart/index.md b/docs/quickstart/index.md
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 # Getting Started
 
 WalledEval can serve **four** major functions, namely the following:
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     [:octicons-arrow-right-24: Prompt Benchmarking](prompts.md)
 
--   :material-library-outline:{ .lg .middle }  __LLM Knowledge__
+-   :material-book-check-outline:{ .lg .middle }  __LLM Knowledge__
 
     ---
 

diff --git a/docs/quickstart/judges.md b/docs/quickstart/judges.md
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 # Judge Benchmarking
 
 Beyond just LLMs, some datasets are designed to benchmark judges and identify if they are able to accurately classify questions as **safe** or **unsafe**. The general requirements for testing an LLM on Judge Benchmarks is as follows:
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 Here's how you can do this easily in WalledEval!
 
-```python title="judge_quickstart.py" linenums="1" hl_lines="25 28 38 39 45"
+```python title="judge_quickstart.py" linenums="1" hl_lines="25 28 38 39 46"
 from walledeval.data import HuggingFaceDataset
 from walledeval.types import SafetyPrompt
 from walledeval.judge import WalledGuardJudge

diff --git a/docs/quickstart/mcq.md b/docs/quickstart/mcq.md
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 # MCQ Benchmarking
 
 Some safety datasets (e..g [WMDP](https://www.wmdp.ai/) and [BBQ](https://aclanthology.org/2022.findings-acl.165/)) are designed to test LLMs on any harmful knowledge or inherent biases that they may possess. These datasets are largely formatted in multiple-choice question (**MCQ**) format, hence why we choose to call them MCQ Benchmarks. The general requirements for testing an LLM on MCQ Benchmarks is as follows:

diff --git a/docs/quickstart/prompts.md b/docs/quickstart/prompts.md
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 # Prompt Benchmarking
 
 Most safety datasets aim to test LLMs on their creativity / restraint in generating responses to custom unsafe/safe queries. The general requirements for testing an LLM on Prompt Benchmarks is as follows: