BAAI
/

bge-en-icl

@@ -2487,33 +2487,11 @@ model-index:
 <h1 align="center">FlagEmbedding</h1>
-<h4 align="center">
-    <p>
-        <a href=#model-list>Model List</a> |
-        <a href=#frequently-asked-questions>FAQ</a> |
-        <a href=#usage>Usage</a>  |
-        <a href="#evaluation">Evaluation</a> |
-        <a href="#train">Train</a> |
-        <a href="#contact">Contact</a> |
-        <a href="#citation">Citation</a> |
-        <a href="#license">License</a>
-    <p>
-</h4>
-For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
-If you are looking for a model with rich semantic expression capabilities, consider choosing **BGE-EN-Mistral**. It combines the ability of in-context learning with the strengths of large models and dense retrieval, achieving outstanding results.
-**BGE-EN-Mistral** primarily demonstrates the following capabilities:
-- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.
-- Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.
-We will release a technical report about **BGE-EN-Mistral** soon with more details.
-[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
 FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
 - **LLM-based Dense Retrieval**: BGE-EN-Mistral, BGE-Multilingual-Gemma2
 - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
 - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
@@ -2521,75 +2499,25 @@ FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following p
 - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
 - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
-## News
-- 7/26/2024: Release **BGE-En-Mistral**, a Mistral-7B based dense retriever, by integrating in-context learning abilities into the embedding model, new state-of-the-art results have been achieved on both the MTEB and AIR-Benchmark.
-- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
-It is the first embedding model that supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
-[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
-- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
-- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
-- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
-- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
-- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) and [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
-- 09/12/2023: New models:
-    - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
-    - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
-<details>
-  <summary>More</summary>
-<!-- ### More -->
-- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
-- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
-- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
-- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
-- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
-</details>
-## Model List
-`bge` is short for `BAAI general embedding`.
-| 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-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]                                          |   |
-| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)   | 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]                                          |   |
-| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)   |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)     |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)   |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)   |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
-| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)     |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
-| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5)   |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
-| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en)             |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                             :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard                              | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en)               |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                            a base-scale model but with similar ability to `bge-large-en`                                             | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en)             |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                                 a small-scale model but with competitive performance                                                 | `Represent this sentence for searching relevant passages: `  |
-| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)             |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                      :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark                       | `为这个句子生成表示以用于检索相关文章：`  |
-| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)               |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                            a base-scale model but with similar ability to `bge-large-zh`                                             | `为这个句子生成表示以用于检索相关文章：`  |
-| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)             |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                                 a small-scale model but with competitive performance                                                 | `为这个句子生成表示以用于检索相关文章：`  |
-[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
-[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
-For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
-All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
-If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
 ## Usage
-### Usage for Embedding Model
-Here are some examples for using `bge-en-mistral` model with [FlagEmbedding](#using-flagembedding) or [Huggingface Transformers](#using-huggingface-transformers).
-#### Using FlagEmbedding
 ```
 pip install -U FlagEmbedding
 ```
@@ -2610,22 +2538,21 @@ examples = [
    'query': 'causes of back pain in female for a week',
    'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
 ]
-model = FlagICLModel('BAAI/bge-en-mistral',
                      query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
-                     examples_for_task=examples,
                      use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
 embeddings_1 = model.encode_queries(queries)
 embeddings_2 = model.encode_corpus(documents)
 similarity = embeddings_1 @ embeddings_2.T
 print(similarity)
 ```
-For the value of the argument `query_instruction_for_retrieval`, You can see [e5-mistral-7b](https://huggingface.co/intfloat/e5-mistral-7b-instruct), but we append `.` at the end of each instruct.
 By default, FlagICLModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
 You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
-#### Using HuggingFace Transformers
 With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
@@ -2669,7 +2596,7 @@ queries = [
     examples_prefix + get_detailed_instruct(task, 'how much protein should a female eat'),
     examples_prefix + get_detailed_instruct(task, 'summit define')
 ]
-# No need to add instruction for retrieval documents
 documents = [
     "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
     "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
@@ -2695,52 +2622,9 @@ print(scores.tolist())
 ```
-### Usage for Reranker
-Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
-You can get a relevance score by inputting query and passage to the reranker.
-The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
-#### Using FlagEmbedding
-```
-pip install -U FlagEmbedding
-```
-Get relevance scores (higher scores indicate more relevance):
-```python
-from FlagEmbedding import FlagReranker
-reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
-score = reranker.compute_score(['query', 'passage'])
-print(score)
-scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
-print(scores)
-```
-#### Using Huggingface transformers
-```python
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
-model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
-model.eval()
-pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
-with torch.no_grad():
-    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
-    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
-    print(scores)
-```
 ## Evaluation
 `bge-en-mistral` achieve **state-of-the-art performance on both MTEB and Air-Bench leaderboard!**
-For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
 - **MTEB**:
@@ -2808,11 +2692,32 @@ For more details and evaluation tools see our [scripts](https://github.com/FlagO
 | **bge-en-mistral few-shot**  | **79.63** | **79.36** |   **74.80**    |   67.79   | **74.83** |
-## Contact
-If you have any question or suggestion related to this project, feel free to open an issue or pull request.
-You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
 ## Citation
@@ -2831,5 +2736,4 @@ If you find this repository useful, please consider giving a star :star: and cit
 ```
 ## License
-FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

 <h1 align="center">FlagEmbedding</h1>
+For more details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
 FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
 - **LLM-based Dense Retrieval**: BGE-EN-Mistral, BGE-Multilingual-Gemma2
 - **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
 - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
 - **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
 - **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
+**BGE-EN-Mistral** primarily demonstrates the following capabilities:
+- In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.
+- Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.
+## 📑 Open-source Plan
+- [x] Checkpoint
+- [ ] Training Data
+- [ ] Evaluation Pipeline
+- [ ] Technical Report
+We will release the technical report and training data for **BGE-EN-Mistral** in the future.
 ## Usage
+### Using FlagEmbedding
 ```
 pip install -U FlagEmbedding
 ```
    'query': 'causes of back pain in female for a week',
    'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
 ]
+model = FlagICLModel('BAAI/bge-en-icl',
                      query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
+                     examples_for_task=examples,  # set `examples_for_task=None` to use model without examples
                      use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
 embeddings_1 = model.encode_queries(queries)
 embeddings_2 = model.encode_corpus(documents)
 similarity = embeddings_1 @ embeddings_2.T
 print(similarity)
 ```
 By default, FlagICLModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
 You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
+### Using HuggingFace Transformers
 With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
     examples_prefix + get_detailed_instruct(task, 'how much protein should a female eat'),
     examples_prefix + get_detailed_instruct(task, 'summit define')
 ]
+# No need to add instructions for documents
 documents = [
     "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
     "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
 ```
 ## Evaluation
 `bge-en-mistral` achieve **state-of-the-art performance on both MTEB and Air-Bench leaderboard!**
 - **MTEB**:
 | **bge-en-mistral few-shot**  | **79.63** | **79.36** |   **74.80**    |   67.79   | **74.83** |
+## Model List
+`bge` is short for `BAAI general embedding`.
+| 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-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]                                          |   |
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)   | 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]                                          |   |
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)   |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)     |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)   |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)   |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)     |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5)   |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                               version 1.5 with more reasonable similarity distribution                                               | `为这个句子生成表示以用于检索相关文章：`  |
+| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en)             |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                             :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard                              | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en)               |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                            a base-scale model but with similar ability to `bge-large-en`                                             | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en)             |       English       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                                 a small-scale model but with competitive performance                                                 | `Represent this sentence for searching relevant passages: `  |
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)             |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                      :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark                       | `为这个句子生成表示以用于检索相关文章：`  |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)               |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                            a base-scale model but with similar ability to `bge-large-zh`                                             | `为这个句子生成表示以用于检索相关文章：`  |
+| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)             |       Chinese       |                             [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)                              |                                                 a small-scale model but with competitive performance                                                 | `为这个句子生成表示以用于检索相关文章：`  |
 ## Citation
 ```
 ## License
+FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE).