Weaviate 发文讲解如何为任务选择 embedding 模型
There is no "best" embedding model. There's only the best one for your data, your task, and your la...
Weaviate 这篇教你按自己的数据选 embedding 模型，还拿 OpenAI 大小模型实测对比，方法可直接照抄。
Weaviate 发文指出 MTEB 排行榜分数是自报的，不能直接反映你的数据表现。选模型应从领域、模态与语言、任务类型、模型大小四个维度入手，再用自己的数据评估。文中示例在小型烘焙数据集上对比 OpenAI 小型和大型 embedding 模型，两者平均 precision（0.5）和 recall（0.58）打平，更大的模型并没有更好。起步只需 50 到 100 条人工标注数据加少量定义了相关性的查询。
There is no "best" embedding model. There's only the best one for your data, your task, and your la...
There is no "best" embedding model. There's only the best one for your data, your task, and your latency budget. If you're still wondering which one to chose, Victoria made a video breaking it down. The short version: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗹𝗲𝗮𝗱𝗲𝗿𝗯𝗼𝗮𝗿𝗱. The questions that matter: • Domain: legal, medical, e-commerce? Specialized models understand the terminology and what matters in it better • Modality and language: text-only, images, multilingual? • Task: a model that's strong at retrieval can behave very differently from one tuned for summarization • Size: bigger models capture more nuance but cost you latency and memory, and can overfit on top of that 𝗧𝗵𝗲𝗻 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝗼𝗻 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗱𝗮𝘁𝗮. The MTEB leaderboard is a solid starting point for picking a baseline, but scores are self-reported and benchmark data may look nothing like yours (it may even be in the training data). You don't need much to start - 50 to 100 hand-labeled objects and a few queries with defined relevance judgments. In the blog post's example comparing OpenAI's small and large embedding models on a tiny pastry dataset, the two models tied on average precision and recall (0.5 and 0.58)... while taking turns beating each other on individual queries. Bigger wasn't better. It was just different. 𝗧𝗵𝗲𝗻 𝘁𝗵𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗶𝗱𝗲. Closed APIs mean rate limits and one more vendor to manage. Self-hosting open-source models means GPUs and maintenance. Weaviate Embeddings removes all this work: models hosted next to your data, no external API calls, no artificial limits on how fast you can embed, and pay-as-you-g youtube.com/watch?v=djp420… l basel weaviate.io/blog/how-to-ch… ur own data, iterate f weaviate.io/product/embedd… s://t.co/tWcJFd7DAM Blog: https://t.co/wKz29G62i8 Weaviate Embeddings: https://t.co/BwjEgtX9at 💬 1 🔄 0 ❤️ 8 👀 343 📊 2 ⚡
