| --- |
| 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 |
| <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 1000000000000000019884624838656 tokens |
| - **Output Dimensionality:** 1536 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modalities:** Text, Image, Audio, Video, Message |
| <!-- - **Training Dataset:** Unknown --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### 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) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](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 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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 |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
| <!-- |
| ## Model Card Authors |
| |
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
| |
| <!-- |
| ## Model Card Contact |
| |
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
| --> |
