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README.md

@@ -2690,7 +2690,7 @@ print(scores.tolist())
 
 | Model                                                                     |      Language       |                                                                                                                                                                                       |                                                                     Description                                                                      | query instruction for retrieval [1] |
 |:--------------------------------------------------------------------------|:-------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
-| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl)         |       English       |                                                                                           -                                                                                           | A LLM-based dense retriever with in-context learning capabilities can fully leverage the model's potential based on a few shot examples(4096 tokens) | Provide instructions and few-shot examples freely based on the given task. |
+| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl)         |       English       |                                                                                           -                                                                                           | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |
 | [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3)                         |    Multilingual     | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) |         Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens)          |  |
 | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder)             |       English       |                                        [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md)                                        |                                  a unified embedding model to support diverse retrieval augmentation needs for LLMs                                  | See [README](./FlagEmbedding/llm_embedder/README.md) |
 | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English |                                 [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker)                                 |                                         a cross-encoder model which is more accurate but less efficient [2]                                          |   |