From 05cb56428926cef828105104ab9d2299b2f92ab4 Mon Sep 17 00:00:00 2001
From: ZiyiXia <joeyxia0805@163.com>
Date: Thu, 21 Nov 2024 13:00:38 +0000
Subject: [PATCH] update README

---
 README.md                               |  1 +
 Tutorials/4_Evaluation/4.5.3_MKQA.ipynb | 76 -------------------------
 Tutorials/README.md                     |  5 ++
 3 files changed, 6 insertions(+), 76 deletions(-)
 delete mode 100644 Tutorials/4_Evaluation/4.5.3_MKQA.ipynb

diff --git a/README.md b/README.md
index 632166f7..d85f391c 100644
--- a/README.md
+++ b/README.md
@@ -158,6 +158,7 @@ Currently we are updating the [tutorials](./Tutorials/), we aim to create a comp
 
 The following contents are releasing in the upcoming weeks:
 
+- Evaluation
 - RAG
 
 <details>
diff --git a/Tutorials/4_Evaluation/4.5.3_MKQA.ipynb b/Tutorials/4_Evaluation/4.5.3_MKQA.ipynb
deleted file mode 100644
index cf841342..00000000
--- a/Tutorials/4_Evaluation/4.5.3_MKQA.ipynb
+++ /dev/null
@@ -1,76 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Evaluate on MKQA"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[MKQA](https://github.com/apple/ml-mkqa) is an open-domain question answering evaluation set comprising 10k question-answer pairs aligned across 26 typologically diverse languages."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 0. Installation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First install the library we are using:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# % pip install FlagEmbedding"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "MKQA contains 10,000 queries sampled from the [Google Natural Questions dataset](https://github.com/google-research-datasets/natural-questions). We use the well-processed [corpus](https://huggingface.co/datasets/BeIR/nq) of NQ offered by the BEIR."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/Tutorials/README.md b/Tutorials/README.md
index 07ac0501..2d04748b 100644
--- a/Tutorials/README.md
+++ b/Tutorials/README.md
@@ -49,6 +49,11 @@ In this module, we'll show the full pipeline of evaluating an embedding model, a
 - [x] MTEB Leaderboard Eval
 - [x] C-MTEB intro
 - [x] C-MTEB leaderboard
+- [x] Evaluation using Sentence Transformers
+- [x] BEIR benchmark
+- [x] MIRACL
+- [x] MLDR
+- [ ] MKQA
 - [ ] ...
 
 ## [Reranking](./5_Reranking/)