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: 1,670 Bytes
1a5fa14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | {
"types": {
"text_0_Transformer": "sentence_transformers.base.modules.transformer.Transformer",
"text_1_Pooling": "sentence_transformers.sentence_transformer.modules.pooling.Pooling",
"text_2_Normalize": "sentence_transformers.sentence_transformer.modules.normalize.Normalize",
"image_0_SiglipVisionTransformer": "modeling_multimodal_embed.SiglipVisionTransformer",
"image_1_Pooling": "sentence_transformers.sentence_transformer.modules.pooling.Pooling",
"image_2_Dense": "sentence_transformers.base.modules.dense.Dense",
"image_3_Normalize": "sentence_transformers.sentence_transformer.modules.normalize.Normalize",
"audio_0_WhisperEncoderTransformer": "modeling_multimodal_embed.WhisperEncoderTransformer",
"audio_1_Pooling": "sentence_transformers.sentence_transformer.modules.pooling.Pooling",
"audio_2_Dense": "sentence_transformers.base.modules.dense.Dense",
"audio_3_Normalize": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
},
"structure": {
"text": [
"text_0_Transformer",
"text_1_Pooling",
"text_2_Normalize"
],
"image": [
"image_0_SiglipVisionTransformer",
"image_1_Pooling",
"image_2_Dense",
"image_3_Normalize"
],
"audio": [
"audio_0_WhisperEncoderTransformer",
"audio_1_Pooling",
"audio_2_Dense",
"audio_3_Normalize"
]
},
"parameters": {
"default_route": "text",
"allow_empty_key": true,
"route_mappings": {}
}
} |