Sentence Similarity
sentence-transformers
Safetensors
English
clip
multimodal-retrieval
embedding-model
custom_code
Instructions to use BAAI/BGE-VL-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/BGE-VL-base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/BGE-VL-base", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
File size: 270 Bytes
cc4c733 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | [
{
"idx": 0,
"name": "0",
"path": "",
"type": "bge_vl_clip_transformer.BGEVLCLIPTransformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Normalize",
"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
}
]
|