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