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{"embeddings":[{"tensorName":"jina-embeddings-v3","tensorShape":[65,1024],"tensorPath":"https://gist.githubusercontent.com/hanxiao/67caef8946efd3c3ddbcceb8a6b08d80/raw/5c7f5e89f96344c138a64c85f0cfd12b6a076712/tensor.tsv","metadataPath":"https://gist.githubusercontent.com/hanxiao/67caef8946efd3c3ddbcceb8a6b08d80/raw/05effa699842bef7802dd12f0192fa9600ba973d/metadata.tsv"}]} |
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XiaoA 和 XiaoB 一起吃苹果 | |
XiaoC 在吃苹果 | |
XiaoD 在跑步 | |
XiaoE 和朋友们一起跑步 | |
XiaoF 在跑步 | |
XiaoG 在睡觉 | |
XiaoH 和 XiaoI 一起读书 | |
XiaoJ 在读书 | |
XiaoK 在画画 | |
XiaoL 和家人一起玩游戏 |
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{"embeddings":[{"tensorName":"jina-embeddings-v3","tensorShape":[15,1024],"tensorPath":"https://gist.githubusercontent.com/hanxiao/1dab7ef9d3e8bd0c76f5a5a668f0caff/raw/607e5dfaf548359fe19323a981cffb9e1b01c59e/tensor.tsv","metadataPath":"https://gist.githubusercontent.com/hanxiao/1dab7ef9d3e8bd0c76f5a5a668f0caff/raw/913478984ce03a40fba92712fe2836a4b3c0cc3c/metadata.tsv"}]} |
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小明和小华一起吃苹果 | |
小明在吃苹果 | |
小红在跑步 | |
小明和朋友们一起跑步 | |
小明在跑步 | |
小芳在睡觉 | |
小明和小刚一起读书 | |
小明在读书 | |
小丽在画画 | |
小明和家人一起玩游戏 |
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{"embeddings":[{"tensorName":"jina-embeddings-v3","tensorShape":[10,1024],"tensorPath":"https://gist.githubusercontent.com/hanxiao/481853ee1e3240ec28255b077a2cf3bf/raw/551ce8f279514574dc779ce50e4c6a6d74bf4d83/tensor.tsv","metadataPath":"https://gist.githubusercontent.com/hanxiao/481853ee1e3240ec28255b077a2cf3bf/raw/8a8d3b005234f76d2ea437257c8f7ecaa22c7003/metadata.tsv"}]} |
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朋友们在公园里聚在一起,享受相同的户外活动。 | |
邻居们在社区中心形成紧密的小组,分享兴趣。 | |
家庭成员围坐在餐桌旁,讨论共同话题。 | |
购物者聚集在折扣区,挑选喜欢的商品。 | |
通勤者在地铁站排队,一起等待交通。 | |
学生们在图书馆的同一区域学习,专注于课程。 | |
运动爱好者在健身房分区,进行类似锻炼。 | |
工作同事在办公室角落聊天,交流工作心得。 | |
早起跑步者形成一条路线,一同晨练。 | |
家庭聚会中,亲戚们紧密环绕,分享回忆。 |
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{"embeddings":[{"tensorName":"jina-embeddings-v3","tensorShape":[5,1024],"tensorPath":"https://gist.githubusercontent.com/hanxiao/ff16f3fdc1a67d9ce18055a9f9677788/raw/6fb6c219336cac88ef147f4541155b87799da1a9/tensor.tsv","metadataPath":"https://gist.githubusercontent.com/hanxiao/ff16f3fdc1a67d9ce18055a9f9677788/raw/c850bd6941bea7391805bd796462248f620f4cb7/metadata.tsv"}]} |
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1. 明确目标: 想让AI做什么? | |
2. 准备数据: 制作高质量的"教材"。 | |
3. 选择模型: 挑选合适的"学生"。 | |
4. 执行微调: 选择高效的"特训"方法(如LoRA + 混合精度)。 | |
5. 评估部署: 检验成果并投入使用。 |
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{"embeddings":[{"tensorName":"jina-embeddings-v3","tensorShape":[92,1024],"tensorPath":"https://gist.githubusercontent.com/hanxiao/4100cfec2947d74b8ab1de6d14f8b2df/raw/5cd69bbc72c8cdfe7aa3db8c7278d3dcabf831ac/tensor.tsv","metadataPath":"https://gist.githubusercontent.com/hanxiao/4100cfec2947d74b8ab1de6d14f8b2df/raw/c124af08b395e0b83ec47447ac88f1b8e795a529/metadata.tsv"}]} |
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Model card | |
Files and versions | |
xet | |
Community | |
110 | |
编辑模型卡片 | |
all-MiniLM-L6-v2 | |
这是一个 sentence-transformers 模型:它将句子和段落映射到 384 维的密集向量空间中,可用于聚类或语义搜索等任务。 | |
使用(Sentence-Transformers) | |
当你安装了 sentence-transformers 后,使用这个模型就变得很容易: |
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