Update README.md

README.md

@@ -1082,6 +1082,10 @@ model-index:
 It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
 We have designed it for high performance in mongolingual & cross-language applications and trained it specifically to support mixed Chinese-English input without bias. 
 
+`jina-embeddings-v2-base-zh` 是支持中英双语的文本向量模型，它支持长达8192字符的文本编码。
+该模型的研发基于BERT架构(JinaBERT)，JinaBERT是在BERT架构基础上的改进，首次将[ALiBi](https://arxiv.org/abs/2108.12409)应用到编码器架构中以支持更长的序列。
+不同于以往的单语言/多语言向量模型，我们设计双语模型来更好的支持单语言（中搜中）以及跨语言（中搜英）文档检索。
+
 The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
 This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
 
@@ -1175,7 +1179,7 @@ According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/b
 ## Plans
 
 1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
-2. Multimodal embedding models enable MultimodalRAG applications.
+2. Multimodal embedding models enable Multimodal RAG applications.
 3. High-performt rerankers.
 
 ## Contact