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**We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance.** Model List | FAQ | Usage | Evaluation | Train | Citation | License **More details please refer to our Github: FlagEmbedding.**
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**We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance.** Model List | FAQ | Usage | Evaluation | Train | Citation | License **More details please refer to our Github: FlagEmbedding.**
译文排队中 · 当前显示来源原文
**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation 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. And the score can be mapped to a float value in [0,1] by sigmoid function.
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**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation 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. And the score can be mapped to a float value in [0,1] by sigmoid function.
译文排队中 · 当前显示来源原文
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. And the score can be mapped to a float value in [0,1] by sigmoid function. You can select the model according your senario and resource. - For **multilingual**, utilize BAAI/bge-reranker-v2-m3 and BAAI/bge-reranker-v2-gemma `query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.
译文排队中 · 当前显示来源原文
**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation 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. And the score can be mapped to a float value in [0,1] by sigmoid function.
译文排队中 · 当前显示来源原文
**Recommend switching to newest BAAI/bge-small-en-v1.5, which has more reasonable similarity distribution and same method of usage.** Model List | FAQ | Usage | Evaluation | Train | Citation | License More details please refer to our Github: FlagEmbedding.
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If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. 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 . Following this example to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this example, which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
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**Recommend switching to newest BAAI/bge-small-zh-v1.5, which has more reasonable similarity distribution and same method of usage.** Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding.
译文排队中 · 当前显示来源原文
Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs.
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
