--- tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:1200000 - loss:SpladeLoss - loss:SparseMarginMSELoss - loss:FlopsLoss base_model: - prajjwal1/bert-tiny widget: - text: >- Most Referenced:report - Return to the USDOJ/OIG Home Page - US Department of JusticeReturn to the USDOJ/OIG Home Page - US Department of Justice. Opinion:Roberts: Feds to stop using private prisons. - text: >- Paul O'Neill, the founder of the Trans-Siberian Orchestra (pictured) has died at age 61. Paul O'Neill, the founder of the popular Christmas-themed rock ensemble Trans-Siberian Orchestra has died. A statement on the group's Facebook page reads: The entire Trans-Siberian Orchestra family, past and present, is heartbroken to share the devastating news that Paul O’Neill has passed away from chronic illness. - text: meaning for concern - text: >- Additional Tips. 1 Do not rub the ink stains as it can spread the stains further. 2 Make sure you test the cleaning solution on a small, hidden area to check if it is suitable for the material. 3 In case an ink stain has become old and dried, the above mentioned home remedies may not be effective.arpet: For ink stained spots on a carpet, you may apply a paste of cornstarch and milk. Leave it for a few hours before brushing it off. Finally, clean the residue with a vacuum cleaner. Leather: Try using a leather shampoo or a leather ink remover for removing ink stains from leather items. - text: >- See below: 1. Get your marriage license. Before you can change your name, you'll need the original (or certified) marriage license with the raised seal and your new last name on it. Call the clerk's office where your license was filed to get copies if one wasn't automatically sent to you. 2. Change your Social Security card. pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio model-index: - name: SPLADE Sparse Encoder results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: Unknown type: unknown metrics: - type: dot_accuracy@1 value: 0.4772 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.793 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8964 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4772 name: Dot Precision@1 - type: dot_precision@3 value: 0.2713333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.18644000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.10094000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.4616666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.7798833333333334 name: Dot Recall@3 - type: dot_recall@5 value: 0.8874 name: Dot Recall@5 - type: dot_recall@10 value: 0.95595 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.721747648718731 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6489996031746051 name: Dot Mrr@10 - type: dot_map@100 value: 0.6446961471449598 name: Dot Map@100 - type: query_active_dims value: 18.334199905395508 name: Query Active Dims - type: query_sparsity_ratio value: 0.9993993119747921 name: Query Sparsity Ratio - type: corpus_active_dims value: 121.65303042911474 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9960142510179831 name: Corpus Sparsity Ratio datasets: - microsoft/ms_marco language: - en --- # SPLADE Sparse Encoder This is a SPLADE sparse retrieval model based on BERT-Tiny (4M) that was trained by distilling a Cross-Encoder on the MSMARCO dataset. The cross-encoder used was [ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2). This Tiny SPLADE model beats `BM25` by `65.6%` on the MSMARCO benchmark. While this model is `15x` smaller than Naver's official `splade-v3-distilbert`, is posesses `80%` of it's performance on MSMARCO. This model is small enough to be used without a GPU on a dataset of a few thousand documents. - `Collection:` https://huggingface.co/collections/rasyosef/splade-tiny-msmarco-687c548c0691d95babf65b70 - `Distillation Dataset:` https://huggingface.co/datasets/yosefw/msmarco-train-distil-v2 - `Code:` https://github.com/rasyosef/splade-tiny-msmarco ## Performance The splade models were evaluated on 55 thousand queries and 8.84 million documents from the [MSMARCO](https://huggingface.co/datasets/microsoft/ms_marco) dataset. ||Size (# Params)|MRR@10 (MS MARCO dev)| |:---|:----|:-------------------| |`BM25`|-|18.0|-|-| |`rasyosef/splade-tiny`|4.4M|30.9| |`rasyosef/splade-mini`|11.2M|33.2| |`naver/splade-v3-distilbert`|67.0M|38.7| ## 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("rasyosef/splade-tiny") # Run inference queries = [ "what do i need to change my name on my license in ma", ] documents = [ 'Change your name on MA state-issued ID such as driver’s license or MA ID card. All documents you bring to RMV need to be originals or certified copies by the issuing agency. PAPERWORK NEEDED: Proof of legal name change — A court order showing your legal name change. Your Social Security Card with your new legal name change', "See below: 1. Get your marriage license. Before you can change your name, you'll need the original (or certified) marriage license with the raised seal and your new last name on it. Call the clerk's office where your license was filed to get copies if one wasn't automatically sent to you. 2. Change your Social Security card.", "You'll keep the same number—just your name will be different. Mail in your application to the local Social Security Administration office. You should get your new card within 10 business days. 3. Change your license at the DMV. Take a trip to the local Department of Motor Vehicles office to get a new license with your new last name. Bring every form of identification you can get your hands on—your old license, your certified marriage certificate and, most importantly, your new Social Security card.", ] 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([[16.6297, 13.4552, 10.1923]]) ``` ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) - **Maximum Sequence Length:** 512 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): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'BertForMaskedLM'}) (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## More
Click to expand ## Evaluation ### Metrics #### Sparse Information Retrieval * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.4772 | | dot_accuracy@3 | 0.793 | | dot_accuracy@5 | 0.8964 | | dot_accuracy@10 | 0.96 | | dot_precision@1 | 0.4772 | | dot_precision@3 | 0.2713 | | dot_precision@5 | 0.1864 | | dot_precision@10 | 0.1009 | | dot_recall@1 | 0.4617 | | dot_recall@3 | 0.7799 | | dot_recall@5 | 0.8874 | | dot_recall@10 | 0.9559 | | **dot_ndcg@10** | **0.7217** | | dot_mrr@10 | 0.649 | | dot_map@100 | 0.6447 | | query_active_dims | 18.3342 | | query_sparsity_ratio | 0.9994 | | corpus_active_dims | 121.653 | | corpus_sparsity_ratio | 0.996 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 1,200,000 training samples * Columns: query, positive, negative_1, negative_2, and label * Approximate statistics based on the first 1000 samples: | | query | positive | negative_1 | negative_2 | label | |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------| | type | string | string | string | string | list | | details | | | | | | * Samples: | query | positive | negative_1 | negative_2 | label | |:----------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------| | does alzheimer's affect sleep | People with Alzheimer’s disease go through many changes, and sleep problems are often some of the most noticeable. Most adults have changes in their sleep patterns as they age. But the problems are more severe and happen more often for people with Alzheimer’s. | Could the position you SLEEP in affect your risk of Alzheimer's? People who sleep on their side enable their brain to 'detox' better while they rest. While asleep, brain is hard at work removing toxins that build up in the day. If left to build up, these toxins can cause Alzheimer's and Parkinson's. | The Scary Connection Between Snoring and Dementia. For more, visit TIME Health. If you don't snore, you likely know someone who does. Between 19% and 40% of adults snore when they sleep, and that percentage climbs even higher, particularly for men, as we age. | [1.407266616821289, 10.169305801391602] | | what is fy in steel design | Since the yield strength of the steel is quite clearly defined and controlled, this establishes a very precise reference in structural investigations. An early design decision is that for the yield strength (specified by the Grade of steel used) that is to be used in the design work.Several different grades of steel may be used for large projects, with a minimum grade for ordinary tasks and higher grades for more demanding ones.ost steel used for reinforcement is highly ductile in nature. Its usable strength is its yield strength, as this stress condition initiates such a magnitude of deformation (into the plastic yielding range of the steel), that major cracking will occur in the concrete. | fy is the yield point of the material. E is the symbol for Young's Modulus of the material. E can be measured by dividing the elastic stress by the elastic strain.That is, this measurement must be made before the yield point of the material is reached.y is the yield point of the material. E is the symbol for Young's Modulus of the material. E can be measured by dividing the elastic stress by the elastic strain. | The longest dimension of the cant. WT is 13'. Using ASTM A992 carbon steel, a WT9x35.5 is at full bending stress and deflection limits. (Fy = 50 ksi). The only information I've found about using stainless for structural design is that type 304 is usually used.This yield strength (Fy) is only equal to 39 or 42ksi.sing ASTM A992 carbon steel, a WT9x35.5 is at full bending stress and deflection limits. (Fy = 50 ksi). The only information I've found about using stainless for structural design is that type 304 is usually used. | [0.5, 0.5] | | most common nutritional deficiencies for teenagers | : Appendix B: Vitamin and Mineral Deficiencies in the U.S. Some American adults get too little vitamin D, vitamin E, magnesium, calcium, vitamin A and vitamin C (Table B1). More than 40 percent of adults have dietary intakes of vitamin A, C, D and E, calcium and magnesium below the average requirement for their age and gender. Inadequate intake of vitamins and minerals is most common among 14-to-18-year-old teenagers. Adolescent girls have lower nutrient intake than boys (Berner 2014; Fulgoni 2011). But nutrient deficiencies are rare among younger American children; the exceptions are dietary vitamin D and E, for which intake is low for all Americans, and calcium. | Common Nutritional Deficiencies. 10 Most Common Nutritional Deficiencies.. Calcium. Calcium is one of the most abundant minerals in your body, yet most people still manage to have a calcium deficiency. Calcium is best know for adding strength to your bones and teeth. | 1) Vitamin D–Vitamin D deficiency is common in infants born to mothers with low levels of Vitamin D. Severe deficiency of this nutrient in infancy and early childhood can lead to the development of Rickets, a disease that affects bone formation and causes bow-legs. | [3.182860851287842, 7.834665775299072] | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "document_regularizer_weight": 0.2, "query_regularizer_weight": 0.3 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.05 - `fp16`: True - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `push_to_hub`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `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`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.05 - `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`: True - `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`: True - `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} - `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`: True - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | dot_ndcg@10 | |:-------:|:----------:|:-------------:|:-----------:| | 1.0 | 37500 | 11.4095 | 0.7103 | | 2.0 | 75000 | 10.5305 | 0.7139 | | 3.0 | 112500 | 9.5368 | 0.7197 | | **4.0** | **150000** | **8.717** | **0.7216** | | 5.0 | 187500 | 8.3094 | 0.7217 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.0.0 - Transformers: 4.54.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.8.1 - Datasets: 4.0.0 - Tokenizers: 0.21.2 ## 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", } ``` #### 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}, } ``` #### SparseMarginMSELoss ```bibtex @misc{hofstätter2021improving, title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury}, year={2021}, eprint={2010.02666}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` #### 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} } ```