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1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本 2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据 1. 对比学习损失函数 2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例) 3. EWC(Elastic Weights Consolidation)[4] 4. cosent loss[5] 5. 每一种类型的数据一个迭代器，分别计算loss进行更新 stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction( 比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。

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- **INF-Retriever-v1-1.5B** is a lightweight version of the **INF-Retriever-v1**, an LLM-based dense retrieval model developed by INF TECH. It is built upon the gte-Qwen2-1.5B-instruct model and specifically fine-tuned to excel in retrieval tasks, particularly for Chinese and English data. - As of February 19, 2025, **INF-Retriever-v1-1.5B** ranks both **No.1** on the Automated Heterogeneous Information Retrieval Benchmark of version 24.04 & 24.05(AIR-Bench) for the bilingual Chinese and English sub-leaderboard, among models with fewer than 7B parameters. This demonstrates its cutting-edge performance in heterogeneous information retrieval tasks. - **Optimized for Chinese and English retrieval**: The model has been specifically fine-tuned with retrieval-focused datasets in both languages, significantly improving its accuracy and efficiency for a variety of retrieval scenarios.

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Text Embeddings by Weakly-Supervised Contrastive Pre-training. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval. Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.

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e5-large

intfloat

**News (May 2023): please switch to e5-large-v2, which has better performance and same method of usage.** Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.

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Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `summarization_query`. Additionally, check out unilm/e5/utils.py for prompts we used for evaluation. You can use these via e.g. `model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ")`. This model is initialized from Mistral-7B-v0.1 and fine-tuned on a mixture of multilingual datasets. As a result, it has some multilingual capability. However, since Mistral-7B-v0.1 is mainly trained on English data, we recommend using this model for English only. For multilingual use cases, please refer to multilingual-e5-large.

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e5-small

intfloat

**News (May 2023): please switch to e5-small-v2, which has better performance and same method of usage.** Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf.

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