rasyosef / splade-small

huggingface.co
Total runs: 90
24-hour runs: 0
7-day runs: 8
30-day runs: 70
Model's Last Updated: October 07 2025
feature-extraction

Introduction of splade-small

Model Details of splade-small

SPLADE-BERT-Small-Distil

This is a SPLADE sparse retrieval model based on BERT-Small (29M) that was trained by distilling a Cross-Encoder on the MSMARCO dataset. The cross-encoder used was ms-marco-MiniLM-L6-v2 .

This SPLADE model is 2x smaller than Naver's official splade-v3-distilbert while having 91% of it's performance on the MSMARCO benchmark. This model is small enough to be used without a GPU on a dataset of a few thousand documents.

Performance

The splade models were evaluated on 55 thousand queries and 8.84 million documents from the MSMARCO 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
rasyosef/splade-small 28.8M 35.2
naver/splade-v3-distilbert 67.0M 38.7
Usage
Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("rasyosef/splade-small")
# Run inference
queries = [
    "is cognac whisky",
]
documents = [
    'Cognac vs Whiskey. • Whiskey is the alcoholic drink made from grains whereas Cognac is the alcoholic drink made from grapes. • Cognac is a type of brandy. In fact, many label it as the finest of brandies. • Cognac is the brandy originating from a wine producing region of France called Cognac. • While a cognac is considered an after dinner beverage that is intended to digest food, there is no such stereotyping of whiskey that can be consumed anytime of the day.',
    '10 hilariously insightful foreign words. One of the most obvious differences between cognac and whiskey is that cognac makers use grapes, and whiskey makers use grains. Although both processes use fermentation to create the liquors, cognac makers use a double distillation process.',
    'The word whisky (or whiskey) is an anglicisation of the Classical Gaelic word uisce / uisge meaning water (now written as uisce in Irish Gaelic, and uisge in Scottish Gaelic). Distilled alcohol was known in Latin as aqua vitae (water of life).',
]
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([[22.4589, 20.5905, 10.0662]])
Model Details
Model Description
  • Model Type: SPLADE Sparse Encoder
  • Base model: prajjwal1/bert-small
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 30522 dimensions
  • Similarity Function: Dot Product
Model Sources
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
Metric Value
dot_accuracy@1 0.5172
dot_accuracy@3 0.8368
dot_accuracy@5 0.9232
dot_accuracy@10 0.9762
dot_precision@1 0.5172
dot_precision@3 0.2867
dot_precision@5 0.1924
dot_precision@10 0.1027
dot_recall@1 0.5006
dot_recall@3 0.8238
dot_recall@5 0.9153
dot_recall@10 0.9723
dot_ndcg@10 0.7554
dot_mrr@10 0.6877
dot_map@100 0.6829
query_active_dims 29.7198
query_sparsity_ratio 0.999
corpus_active_dims 168.3538
corpus_sparsity_ratio 0.9945
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
    • min: 4 tokens
    • mean: 9.04 tokens
    • max: 41 tokens
    • min: 18 tokens
    • mean: 81.11 tokens
    • max: 215 tokens
    • min: 18 tokens
    • mean: 77.81 tokens
    • max: 247 tokens
    • min: 22 tokens
    • mean: 76.2 tokens
    • max: 217 tokens
    • size: 2 elements
  • Samples:
    query positive negative_1 negative_2 label
    The _____________________ is a body system which consists of glands that produce hormones that act throughout the body. Endocrine System. The endocrine system is made up of a group of glands that produce the body's long-distance messengers, or hormones. Hormones are chemicals that control body functions, such as metabolism, growth, and sexual development.t is made up of a group of organs that transport blood throughout the body. The heart pumps the blood and the arteries and veins transport it. Oxygen-rich blood leaves the left side of the heart and enters the biggest artery, called the aorta. The endocrine system is a control system of ductless glands that secrete hormones within specific organs. Hormones act as messengers, and are carried by the bloodstream to different cells in the body, which interpret these messages and act on them.he pancreas is unusual among the body's glands in that it also has a very important endocrine function. Small groups of special cells called islet cells throughout the organ make the hormones of insulin and glucagon. These glands produce different types of hormones that evoke a specific response in other cells, tissues, and/or organs located throughout the body. The hormones reach these faraway targets using the blood stream. Like the nervous system, the endocrine system is one of your body’s main communicators.he Endocrine System Essentials. 1 The endocrine system is made up of a network of glands. 2 These glands secrete hormones to regulate many bodily functions, including growth and metabolism. [2.3722684383392334, 5.211579322814941]
    causes of low body temperature in adults Hypothermia is defined as a body temperature (core, or internal body temperature) of less than about 95 F (35 C). Usually, hypothermia occurs when the body's temperature regulation is overwhelmed by a cold environment. However, in the medical and lay literature there are essentially two major classifications, accidental hypothermia and intentional hypothermia. In general, a baby has a fever when their body temperature exceeds 100.4°F, or 38°C. A child has a fever when their temperature exceeds 99.5°F, or 37.5°C. An adult has a fever when their temperature exceeds 99 to 99.5°F, or 37.2 to 37.5°C. Consequently, an accurate measurement of body temperature (best is rectal core temperature) of 100.4 F (38 C) or above is considered to be a fever.. A newer option includes a temperature-sensitive infrared device that measures the temperature in the skin by simply rubbing the sensor on the body. [1.3747079372406006, 8.096447944641113]
    who is laila gifty akita Lailah Gifty Akita is a Ghanaian and founder of Smart Youth Volunteers Foundation. She obtained a BSc in Renewable Natural Resources Management at Kwame Nkrumah University of Science and Technology, Kumasi-Ghana. She also had MPhil in Oceanography at the University of Ghana. She obtained a doctorate in Geosciences at International Max Planck Research School for Global Biogeochemical Cycles-Friedrich Schiller University of Jena, Germany ( June 2011 to March 2016). Lailah is an influential lady with the passion of empowering the mind of young people to make a great difference. She is a PhD-student, studying Geosciences at the University of Jena, Germany. She is an enthusiastic inspirational writer. She wishes to challenge and inspire people from all walks of life to dare a greater life. You can capable of heroic deeds. Think well of yourself and act positively. You can correspond with Lailah via an email: [email protected] . https://www.goodreads.com/author/show/8297615.Lailah_Gifty_Akita/blog . Also in the Talmud, the interpretation is found of rabbi Hanina ben Pappa (3rd century AD), that Lailah is an angel in charge of conception who takes a drop of semen and places it before God, saying: For R. Hanina b. [2.6488447189331055, 15.058775901794434]
  • Loss: SpladeLoss with these parameters:
    {
        "loss": "SparseMarginMSELoss",
        "document_regularizer_weight": 0.12,
        "query_regularizer_weight": 0.2
    }
    
Training Hyperparameters
Non-Default Hyperparameters
  • eval_strategy : epoch
  • per_device_train_batch_size : 64
  • per_device_eval_batch_size : 64
  • learning_rate : 4e-05
  • num_train_epochs : 4
  • lr_scheduler_type : cosine
  • warmup_ratio : 0.025
  • 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 : 64
  • per_device_eval_batch_size : 64
  • 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 : 4e-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 : cosine
  • lr_scheduler_kwargs : {}
  • warmup_ratio : 0.025
  • 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 18750 7.806 0.7439
2.0 37500 5.7509 0.7520
3.0 56250 4.5026 0.7554
4.0 75000 3.909 0.7534
-1 -1 - 0.7554
  • The bold row denotes the saved checkpoint.
Framework Versions
  • Python: 3.11.13
  • Sentence Transformers: 5.1.0
  • Transformers: 4.55.2
  • PyTorch: 2.6.0+cu124
  • Accelerate: 1.10.0
  • Datasets: 4.0.0
  • Tokenizers: 0.21.4
Citation
BibTeX
Sentence Transformers
@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
@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
@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
@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}
}

Runs of rasyosef splade-small on huggingface.co

90
Total runs
0
24-hour runs
0
3-day runs
8
7-day runs
70
30-day runs

More Information About splade-small huggingface.co Model

More splade-small license Visit here:

https://choosealicense.com/licenses/mit

splade-small huggingface.co

splade-small huggingface.co is an AI model on huggingface.co that provides splade-small's model effect (), which can be used instantly with this rasyosef splade-small model. huggingface.co supports a free trial of the splade-small model, and also provides paid use of the splade-small. Support call splade-small model through api, including Node.js, Python, http.

splade-small huggingface.co Url

https://huggingface.co/rasyosef/splade-small

rasyosef splade-small online free

splade-small huggingface.co is an online trial and call api platform, which integrates splade-small's modeling effects, including api services, and provides a free online trial of splade-small, you can try splade-small online for free by clicking the link below.

rasyosef splade-small online free url in huggingface.co:

https://huggingface.co/rasyosef/splade-small

splade-small install

splade-small is an open source model from GitHub that offers a free installation service, and any user can find splade-small on GitHub to install. At the same time, huggingface.co provides the effect of splade-small install, users can directly use splade-small installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

splade-small install url in huggingface.co:

https://huggingface.co/rasyosef/splade-small

Url of splade-small

splade-small huggingface.co Url

Provider of splade-small huggingface.co

rasyosef
ORGANIZATIONS

Other API from rasyosef

huggingface.co

Total runs: 5.1K
Run Growth: 794
Growth Rate: 15.69%
Updated:October 07 2025
huggingface.co

Total runs: 659
Run Growth: 598
Growth Rate: 90.74%
Updated:October 07 2025
huggingface.co

Total runs: 7
Run Growth: 1
Growth Rate: 14.29%
Updated:September 16 2024