Update README.md

README.md

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 `jina-embeddings-v2-base-zh` is a Chinese/English bilingual text **embedding model** supporting **8192 sequence length**.
 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. 
+Additionally, we provide the following embedding models:
 
 `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.
-
-With a standard size of 161 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference.
-Additionally, we provide the following embedding models:
+除此之外，我们也提供其它向量模型:
 
 - [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters.
 - [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.