update tutorial readme

Author: ZiyiXia

Tutorials/README.md

@@ -1,6 +1,8 @@
-# FlagEmbedding_tutorial
+# Tutorial
 
-If you are new to here, check out the 5 minute [quick start](./quick_start.ipynb)!
+FlagEmbedding holds a whole curriculum for retrieval, embedding models, RAG, etc. This section is currently being actively updated. No matter you are new to NLP or a veteran, we hope you can find something helpful!
+
+If you are new to embedding and retrieval, check out the 5 minute [quick start](./quick_start.ipynb)!
 
 <details>
   <summary>Tutorial roadmap</summary>
@@ -11,18 +13,43 @@ If you are new to here, check out the 5 minute [quick start](./quick_start.ipynb
 
 This module includes tutorials and demos showing how to use BGE and Sentence Transformers, as well as other embedding related topics.
 
+- [x] Intro to embedding model
+- [x] BGE series
+- [x] Usage of BGE
+- [x] BGE-M3
+- [ ] BGE-ICL
+- ...
+
 ## [Similarity](./2_Similarity)
 
 In this part, we show popular similarity functions and techniques about searching.
 
+- [x] Similarity metrics
+- ...
+
 ## [Indexing](./3_Indexing)
 
 Although not included in the quick start, indexing is a very important part in practical cases. This module shows how to use popular libraries like Faiss and Milvus to do indexing.
 
+- [x] Intro to Faiss
+- [x] Using GPU in Faiss
+- [ ] Index and Quantizer
+- [ ] Milvus
+- ...
+
 ## [Evaluation](./4_Evaluation)
 
 In this module, we'll show the full pipeline of evaluating an embedding model, as well as popular benchmarks like MTEB and C-MTEB.
 
+- [x] Evaluate MSMARCO
+- [x] Intro to MTEB
+- [x] MTEB Leaderboard Eval
+- [ ] C-MTEB
+- ...
+
 ## [Reranking](./5_Reranking/)
 
 To balance accuracy and efficiency tradeoff, many retrieval system use a more efficient retriever to quickly narrow down the candidates. Then use more accurate models do reranking for the final results.
+
+- [x] Intro to reranker
+- ...