--- tags: - sentence-transformers - sentence-similarity - feature-extraction - dense pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer results: - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@1 value: 0.7602405110860578 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8357760240511086 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8485531754979331 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.859075535512965 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.7602405110860578 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2785920080170362 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1697106350995866 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08590755355129649 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7602405110860578 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8357760240511086 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8485531754979331 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.859075535512965 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8143497069526588 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7995083302016781 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8018586288255459 name: Cosine Map@100 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) 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](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python 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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.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](https://huggingface.co/blog/train-sentence-transformers): the end-to-end guide for training or finetuning Sentence Transformer models. - [Introduction to Matryoshka Embedding Models](https://huggingface.co/blog/matryoshka): variable-size embeddings that can be truncated with minimal quality loss. - [Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval](https://huggingface.co/blog/embedding-quantization): post-training compression of embedding vectors. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): train multimodal embedding models, with a Visual Document Retrieval walkthrough. ## Citation ### BibTeX