shadowlilac/omniembed-merged

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1536-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Maximum Sequence Length: 1000000000000000019884624838656 tokens
• Output Dimensionality: 1536 dimensions
• Similarity Function: Cosine Similarity
• Supported Modalities: Text, Image, Audio, Video, Message

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'audio': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Gemma4Model'})
  (1): MultiheadAttentionPooling({'hidden_size': 1536, 'num_attention_heads': 16, 'intermediate_size': 6144, 'layer_norm_eps': 1e-06})
  (2): Normalize({})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("shadowlilac/omniembed-merged")
# Run inference
queries = [
    'Which planet is known as the Red Planet?',
]
documents = [
    "Venus is often called Earth's twin because of its similar size and proximity.",
    'Mars, known for its reddish appearance, is often referred to as the Red Planet.',
    'Saturn, famous for its rings, is sometimes mistaken for the Red Planet.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1536] [3, 1536]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.3457, 0.8750, 0.6484]], dtype=torch.bfloat16)

Evaluation

Metrics

Information Retrieval

• Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7602
cosine_accuracy@3	0.8358
cosine_accuracy@5	0.8486
cosine_accuracy@10	0.8591
cosine_precision@1	0.7602
cosine_precision@3	0.2786
cosine_precision@5	0.1697
cosine_precision@10	0.0859
cosine_recall@1	0.7602
cosine_recall@3	0.8358
cosine_recall@5	0.8486
cosine_recall@10	0.8591
cosine_ndcg@10	0.8143
cosine_mrr@10	0.7995
cosine_map@100	0.8019

Training Details

Training Logs

Epoch	Step	cosine_ndcg@10
-1	-1	0.8143

Framework Versions

• Python: 3.12.13
• Sentence Transformers: 5.7.0
• Transformers: 5.14.1
• PyTorch: 2.13.0+cu130
• Accelerate: 1.14.0
• Datasets: 5.0.1
• Tokenizers: 0.22.2

Additional Resources

• Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

Citation

BibTeX