Sentence Similarity
sentence-transformers
PyTorch
ONNX
xlm-roberta
feature-extraction
Eval Results
text-embeddings-inference
Instructions to use BAAI/bge-m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/bge-m3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/bge-m3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
1_Pooling 这个目录的作用是?
#72
by Godsing - opened
纯粹出于好奇,为什么需要 1_Pooling 这个目录,什么情况下需要用到它?
使用sentence_transformer加载时需要的文件,告诉它使用cls pooling
最后的向量是怎么获得,因为你不pooling,它是个多维的向量,没法用
使用sentence_transformer加载时需要的文件,告诉它使用cls pooling
代码里怎么加呢?
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不需要添加,sentence-transformer加载时会自动识别。
Thanks♪(・ω・)ノ