Sentence Similarity
sentence-transformers
Safetensors
English
llava_next
multimodal-retrieval
embedding-model
custom_code
Instructions to use BAAI/BGE-VL-MLLM-S2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BAAI/BGE-VL-MLLM-S2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BAAI/BGE-VL-MLLM-S2", 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: 177 Bytes
4de778d | 1 2 3 4 5 6 7 8 | {
"image_token": "<image>",
"num_additional_image_tokens": 1,
"patch_size": 14,
"processor_class": "LlavaNextProcessor",
"vision_feature_select_strategy": "default"
}
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