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README.md

@@ -7,7 +7,7 @@ tags:
 - mteb
 license: apache-2.0
 model-index:
-- name: bge-en-mistral
+- name: bge-en-icl
   results:
   - dataset:
       config: en
@@ -2616,7 +2616,7 @@ print(scores.tolist())
 
 ## Evaluation  
 
-`bge-en-mistral` achieve **state-of-the-art performance on both MTEB and Air-Bench leaderboard!**
+`bge-en-icl` achieve **state-of-the-art performance on both MTEB and Air-Bench leaderboard!**
 
 - **MTEB**:   
 
@@ -2630,12 +2630,12 @@ print(scores.tolist())
 |  **gte-Qwen2-7B-instruct**   |   83.04   |       31.35       |          85.79          |        86.58        |   **61.42**   |      56.92      |     60.25      |   70.24   |
 |    **stella_en_1.5B_v5**     |   84.51   |     **31.49**     |          88.07          |        88.07        |     61.21     |      57.69      |     61.21      |   71.19   |
 | **bge-multilingual-gemma2**  |   83.88   |       31.20       |          85.84          |        88.08        |     59.72     |      54.65      |     59.24      |   69.88   |
-| **bge-en-mistral zero-shot** |   83.74   |       30.75       |          87.21          |        88.66        |     59.66     |      57.57      |     61.67      |   71.26   |
-| **bge-en-mistral few-shot**  |   84.25   |       30.77       |          88.38          |      **88.99**      |     59.82     |    **57.89**    |   **62.16**    | **71.69** |
+| **bge-en-icl zero-shot** |   83.74   |       30.75       |          87.21          |        88.66        |     59.66     |      57.57      |     61.67      |   71.26   |
+| **bge-en-icl few-shot**  |   84.25   |       30.77       |          88.38          |      **88.99**      |     59.82     |    **57.89**    |   **62.16**    | **71.69** |
 
 - **BEIR**:   
 
-|        BEIR        | e5-mistral-7b-instruct | SFR-Embedding-Mistral | NV-Embed-v1 | Linq-Embed-Mistral | SFR-Embedding-2_R | gte-Qwen2-7B-instruct | stella_en _1.5B_v5 | bge-multilingual-gemma2 | bge-en-mistral zero-shot | bge-en-mistral few-shot |
+|        BEIR        | e5-mistral-7b-instruct | SFR-Embedding-Mistral | NV-Embed-v1 | Linq-Embed-Mistral | SFR-Embedding-2_R | gte-Qwen2-7B-instruct | stella_en _1.5B_v5 | bge-multilingual-gemma2 | bge-en-icl zero-shot | bge-en-icl few-shot |
 | :----------------: | :--------------------: | :-------------------: | :---------: | :----------------: | :---------------: | :-------------------: | :----------------: | :---------------------: | :----------------------: | :---------------------: |
 |    **ArguAna**     |          61.9          |         67.27         |    68.21    |       69.65        |       62.34       |         64.27         |       65.27        |          77.37          |          82.76           |        **83.08**        |
 |  **ClimateFEVER**  |          38.4          |         36.41         |    34.72    |       39.11        |       34.43       |       **45.88**       |       46.11        |          39.37          |          45.35           |          45.43          |
@@ -2666,8 +2666,8 @@ print(scores.tolist())
 |    **Linq-Embed-Mistral**    |   61.04   |   48.41   |   49.44   | **60.18**  |   20.34   |   50.04   |   47.56   |   60.50   |   49.69   |
 |  **gte-Qwen2-7B-instruct**   |   63.46   |   51.20   |   54.07   |   54.20    |   22.31   | **58.20** |   40.27   |   58.39   |   50.26   |
 |    **stella_en_1.5B_v5**     |   61.99   |   50.88   |   53.87   |   58.81    |   23.22   |   57.26   |   44.81   |   61.38   |   51.53   |
-| **bge-en-mistral zero-shot** |   64.61   |   54.40   |   55.11   |   57.25    |   25.10   |   54.81   |   48.46   |   63.71   |   52.93   |
-| **bge-en-mistral few-shot**  | **64.94** | **55.11** | **56.02** |   58.85    | **28.29** |   57.16   | **50.04** | **64.50** | **54.36** |
+| **bge-en-icl zero-shot** |   64.61   |   54.40   |   55.11   |   57.25    |   25.10   |   54.81   |   48.46   |   63.71   |   52.93   |
+| **bge-en-icl few-shot**  | **64.94** | **55.11** | **56.02** |   58.85    | **28.29** |   57.16   | **50.04** | **64.50** | **54.36** |
 
 **Long-Doc (en, Recall@10):**
 
@@ -2680,8 +2680,8 @@ print(scores.tolist())
 |    **Linq-Embed-Mistral**    |   75.46   |   73.81   |     71.58      |   68.58   |   72.11   |
 |  **gte-Qwen2-7B-instruct**   |   63.93   |   68.51   |     65.59      |   65.26   |   65.45   |
 |    **stella_en_1.5B_v5**     |   73.17   |   74.38   |     70.02      |   69.32   |   71.25   |
-| **bge-en-mistral zero-shot** |   78.30   |   78.21   |     73.65      |   67.09   |   73.75   |
-| **bge-en-mistral few-shot**  | **79.63** | **79.36** |   **74.80**    |   67.79   | **74.83** |
+| **bge-en-icl zero-shot** |   78.30   |   78.21   |     73.65      |   67.09   |   73.75   |
+| **bge-en-icl few-shot**  | **79.63** | **79.36** |   **74.80**    |   67.79   | **74.83** |
 
 
 ## Model List
@@ -2690,7 +2690,7 @@ print(scores.tolist())
 
 | Model                                                                     |      Language       |                                                                                                                                                                                       |                                                                     Description                                                                      | query instruction for retrieval [1] |
 |:--------------------------------------------------------------------------|:-------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------:|:--------:|
-| [BAAI/bge-en-mistral](https://huggingface.co/BAAI/bge-en-mistral)         |       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 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-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]                                          |   |