Comments (1)
For practice, the method to rerank long passages used in BCE-reranker is more effective and effecient than long context input model. You can test with other long context input models and share your experience if you like.
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Related Issues (20)
- 使用 langchain 调用程序报错 HOT 2
- pydantic版本问题 HOT 1
- 下载 tokenizer 时不能指定 auth token HOT 2
- Are there any optimization methods to support the optimized run of this project? HOT 1
- RerankerModel的token数量 HOT 4
- 复现 LlamaIndex RAG评测结果时的一些问题 HOT 1
- Xinference对比手动部署 HOT 4
- 单独部署-用api的方式提供服务 HOT 1
- python3.11报错 HOT 1
- 请问有 C-MTEB (非英文)的测试结果么? HOT 1
- 请问有没有提交到最新的mteb leaderboard的结果对比?
- 请问出现这种情况:Token indices sequence length is longer than the specified maximum sequence length for this model (781 > 512),如何解决? HOT 1
- query只能是一个句子吗?多轮交互下的召回或者排序,BCEEmbedding支持处理呢? HOT 3
- Input validation error: `inputs` must have less than 512 tokens. Given: 1009 HOT 1
- accelerate加速,微调bce模型报错:Could not find the transformer layer class to wrap in the model. HOT 1
- FYI:最近测了 bce chunksize & bge 对比 HOT 2
- 请问如何在特定数据集上优化这个词向量模型?
- 为什么匹配效果越来越差 HOT 1
- embedding、rerank模型加速推理 HOT 8
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