Sentence Similarity
PyTorch
sentence-transformers
multimodal
embeddings
retrieval
image-text
audio-text
text-image-audio
tri-encoder
semantic-router
Eval Results (legacy)
Instructions to use llm-semantic-router/multi-modal-embed-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use llm-semantic-router/multi-modal-embed-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("llm-semantic-router/multi-modal-embed-large") 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: 364 Bytes
1a5fa14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | {
"transformer_task": "feature-extraction",
"modality_config": {
"audio": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"audio+text": {
"method": "forward",
"method_output_name": "last_hidden_state"
}
},
"module_output_name": "token_embeddings"
} |