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--- |
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tags: |
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- sentence-transformers |
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- sparse-encoder |
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- sparse |
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- asymmetric |
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- inference-free |
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- splade |
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- generated_from_trainer |
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- dataset_size:1700 |
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- loss:SpladeLoss |
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- loss:SparseMultipleNegativesRankingLoss |
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- loss:FlopsLoss |
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base_model: opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte |
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widget: |
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- text: Who likes Grilled Cheese Sandwiches? |
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- text: Who likes Disco Music? |
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- text: ' Reily Cano likes Walking Leaves, Chainsaws, Shorts, Anteaters, Painters, |
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Ho Hos, Banana Splits, French Toast, Historical Fiction.' |
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- text: ' Kristoffer Blanchard likes Protea Flowers, Soap Dishes, Zucchini, Jasmine, |
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Catharsis, Sitars, Olives, Caramel deLites, the Detroit Lions, Actuaries.' |
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- text: ' Ege Kennedy likes Excitement, Tap Dancing, the Houston Astros, Agave Nectar, |
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Cobras.' |
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pipeline_tag: feature-extraction |
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library_name: sentence-transformers |
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--- |
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# Asymmetric Inference-free SPLADE Sparse Encoder |
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This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte) using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. |
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## Model Details |
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### Model Description |
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- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder |
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- **Base model:** [opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte](https://huggingface.co/opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte) <!-- at revision 1646fef40807937e8e130c66d327a26421c408d5 --> |
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- **Maximum Sequence Length:** 8192 tokens |
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- **Output Dimensionality:** 30522 dimensions |
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- **Similarity Function:** Dot Product |
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<!-- - **Training Dataset:** Unknown --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) |
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### Full Model Architecture |
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``` |
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SparseEncoder( |
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(0): Router( |
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(sub_modules): ModuleDict( |
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(query): Sequential( |
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(0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=DistilBertTokenizerFast) |
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) |
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(document): Sequential( |
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(0): MLMTransformer({'max_seq_length': 8192, 'do_lower_case': False, 'architecture': 'NewForMaskedLM'}) |
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(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'log1p_relu', 'word_embedding_dimension': 30522}) |
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) |
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) |
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) |
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) |
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``` |
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### Metrics |
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``` |
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{ |
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"NDCG": { |
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"NDCG@2": 0.90484, |
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"NDCG@10": 0.91822, |
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"NDCG@20": 0.9204, |
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"NDCG@100": 0.92605 |
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}, |
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"MAP": { |
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"MAP@2": 0.90125, |
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"MAP@10": 0.91146, |
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"MAP@20": 0.91216, |
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"MAP@100": 0.91316 |
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}, |
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"Recall": { |
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"Recall@2": 0.9045, |
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"Recall@10": 0.931, |
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"Recall@20": 0.938, |
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"Recall@100": 0.963 |
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}, |
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"Precision": { |
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"P@2": 0.9045, |
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"P@10": 0.1862, |
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"P@20": 0.0938, |
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"P@100": 0.01926 |
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} |
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} |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SparseEncoder |
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# Download from the 🤗 Hub |
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model = SparseEncoder("Frinkleko/opensearch-project_opensearch-neural-sparse-encoding-doc-v3-gte-limit-samples-1700") |
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# Run inference |
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queries = [ |
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"Who likes Sonatas?", |
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] |
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documents = [ |
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' Kamron Rose likes Landscaping, Spinning, Rugs, Model Building, Figure Skating, Extension Cords, Doctors, Sonatas, Owls.', |
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' Ege Kennedy likes Excitement, Tap Dancing, the Houston Astros, Agave Nectar, Cobras.', |
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' Codey Luna likes Bodyboarding, Keys, Tarantulas, Bonsai Trees, Balsamic Vinegar, Kale, Ticket to Ride, Ricotta, Tuning Forks, Silver, Sperm Whales, The Roaring Twenties, Manchego.', |
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] |
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query_embeddings = model.encode_query(queries) |
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document_embeddings = model.encode_document(documents) |
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print(query_embeddings.shape, document_embeddings.shape) |
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# [1, 30522] [3, 30522] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(query_embeddings, document_embeddings) |
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print(similarities) |
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# tensor([[16.2629, 6.6099, 5.9649]]) |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 1,700 training samples |
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* Columns: <code>query</code> and <code>document</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | query | document | |
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|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 7.37 tokens</li><li>max: 12 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 44.42 tokens</li><li>max: 67 tokens</li></ul> | |
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* Samples: |
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| query | document | |
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|:---------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Who likes Sunflowers?</code> | <code> Rasmus Logan likes Dark Chocolate, Documentary Series, Washing Machines, Softball, Sunflowers, Gregorian chants, Za'atar, Abacuses, Dolphins, Root Beer Floats, Cumin, Coconut Flour.</code> | |
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| <code>Who likes Shaved Ice?</code> | <code> Abdulkarem Boyer likes Stag Beetles, Acacia Trees, Olives, Landscape Photography, Neoclassicism, Guinea Pigs, Mentoring, Parsley, Chemistry, Vases, Shaved Ice.</code> | |
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| <code>Who likes Rock Balancing?</code> | <code> Tanay Melton likes Poetry Slams, Sperm Whales, Tonic Water, Bat Flowers, Rock Balancing.</code> | |
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* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: |
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```json |
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{ |
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"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)", |
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"document_regularizer_weight": 3e-05, |
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"query_regularizer_weight": 5e-05 |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 85 |
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- `num_train_epochs`: 4 |
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- `warmup_ratio`: 0.1 |
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- `batch_sampler`: no_duplicates |
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- `router_mapping`: {'query': 'query', 'document': 'document'} |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 85 |
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- `per_device_eval_batch_size`: 8 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `parallelism_config`: None |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `hub_revision`: None |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `liger_kernel_config`: None |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
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- `router_mapping`: {'query': 'query', 'document': 'document'} |
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- `learning_rate_mapping`: {} |
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</details> |
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### Framework Versions |
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- Python: 3.12.9 |
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- Sentence Transformers: 5.1.0 |
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- Transformers: 4.56.0 |
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- PyTorch: 2.8.0+cu128 |
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- Accelerate: 1.10.1 |
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- Datasets: 4.0.0 |
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- Tokenizers: 0.22.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### LIMIT |
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``` |
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@misc{weller2025theoreticallimit, |
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title={On the Theoretical Limitations of Embedding-Based Retrieval}, |
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author={Orion Weller and Michael Boratko and Iftekhar Naim and Jinhyuk Lee}, |
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year={2025}, |
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eprint={2508.21038}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2508.21038}, |
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} |
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``` |
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#### OpenSearch Models |
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``` |
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@inproceedings{Shen_2025, series={SIGIR ’25}, |
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title={Exploring $\ell_0$ parsification for Inference-free Sparse Retrievers}, |
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url={http://dx.doi.org/10.1145/3726302.3730192}, |
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DOI={10.1145/3726302.3730192}, |
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booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval}, |
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publisher={ACM}, |
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author={Shen, Xinjie and Geng, Zhichao and Yang, Yang}, |
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year={2025}, |
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month=jul, pages={2572–2576}, |
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collection={SIGIR ’25} |
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} |
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``` |
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``` |
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@misc{geng2025competitivesearchrelevanceinferencefree, |
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title={Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers}, |
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author={Zhichao Geng and Yiwen Wang and Dongyu Ru and Yang Yang}, |
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year={2025}, |
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eprint={2411.04403}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2411.04403}, |
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} |
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``` |
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#### SpladeLoss |
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```bibtex |
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@misc{formal2022distillationhardnegativesampling, |
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title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, |
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author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, |
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year={2022}, |
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eprint={2205.04733}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2205.04733}, |
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} |
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``` |
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#### SparseMultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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#### FlopsLoss |
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```bibtex |
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@article{paria2020minimizing, |
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title={Minimizing flops to learn efficient sparse representations}, |
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author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, |
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journal={arXiv preprint arXiv:2004.05665}, |
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year={2020} |
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} |
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``` |
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<!-- |
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## Glossary |
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*Clearly define terms in order to be accessible across audiences.* |
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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